A smart tele-cytology point-of-care platform for oral cancer screening.

Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.

Introduction

Oral cancer accounts for 30% of cancer-related death in low and middle-income countries [1]. Risk stratification of oral potentially malignant lesions (OPML) and early malignant lesions may help to initiate therapeutic intervention and may improve the prognosis. Biopsy and histopathology-based grading of OPML is the current standard of care. However, due to the invasive nature of biopsies and lack of related expertise, this is neither feasible nor readily utilized as a screening tool. These issues are owed to the scarcity of trained specialists such as pathologists or surgeons in low-resource-settings. The studies shows that less than 65% of primary care centres have access to reliable pathology services in low-middle income countries[1-3]. Hence, a tele-cytology platform that provides reliable, remote connectivity to frontline health workers (FHW) and specialists may improve early detection of oral cancer.

Oral cytology, is considered an effective tool for the large-scale screening of high-risk populations [4, 5]. Telemedicine platforms have been used in cytology with proven benefits in remote diagnosis of cervical [6], lung [7, 8], breast [9, 10], and thyroid malignancies [11, 12]. Additionally, automated analysis of tele-cytology images using machine learning is an aspect that will impart a Point-of-Care (PoC) applicability to the system and is increasingly required wherein additional skilled manpower is not available. The applications of ANN have been previously explored for the classification of oral diseases [13] and cytopathology diagnosis of cervical [14-16], breast[17, 18], and blood malignancies [19]. Combining tele-cytology along with ANN will be a step towards translating the platform into a point of care application in oral cancer.

The platform used in this study (Fig 1) was an iPad tablet-based version of the “CellScope” mobile microscope [20], capable of automated focusing of cells, scanning of cytology slides, and uploading the captured images to a specialized web-based server. Our initial study showed the feasibility of tele-cytology in connecting FHW with pathologists [20]. In this study, we explored the clinical utility/efficacy of this portable, automated system in combination with Convolutional Neural Network (CNN) for classification of atypical cells [21] and subsequent training of the ANN, Inception V3 architecture [22]. We hypothesize that integration of ANN with the tele-cytology platform may improve risk stratification of OPML. This pilot study validates the risk stratification model prior to implementation in a low resource setting.

Fig 1

Study design.

Microscopic slides were prepared (a) using liquid based cytology and slides were reviewed by (b) conventional direct microscopy. Images were captured using CellScope (c) and sent to remote server (d). Tele-cytology diagnosis (f) were performed by pathologist. Image pre-processing algorithm were developed, and ANN based cytology diagnostic platform were developed (g) and validated. Conventional cytology diagnosis, tele-cytology diagnosis and ANN based diagnosis were compared with histopathology.

Materials and methods

Study population and data acquisition

The study was carried out among subjects attending the out-patient clinics of Department of Oral Medicine and Radiology, KLES Institute of Dental Sciences, Bangalore and Head and Neck Oncology Department, Mazumdar Shaw Medical Center, Bangalore, India for a period of 24 months from October 2014 to September 2016. The Narayana Health Medical Ethics Committee has approved the study (NHH/MEC-CL-2014/222) and the subjects clinically diagnosed with OPML and malignant lesions were recruited, while those who did not consent and/or had undergone biopsy previously were excluded from the study. Sample collection and slide preparation was carried out as reported previously [20].

High resolution cytology images (2592 x 1936) of 24-bit depth with optical resolution of 1.9 pixel/micrometre (optical resolution 200X) were captured using the CellScope as described previously [20] (S1A and S1B Fig). This platform captured the fields (100–125) in a raster scan pattern, the images were connected to the clinical/demographic data of the patients entered into the iPad application. (S2A Fig). For remote diagnosis, the pathologists utilized a custom, web portal interface for blind review of images [20], which enabled them to zoom into the image and visualize the morphological features without loss of resolution (S2B–S2E Fig). The overall quality of the images of each subject (n = 32, 3200 images) were assessed by recording the following parameters: i) overall image quality (good, adequate and poor), ii) diagnostic capability (diagnostic and non-diagnostic) and iii) time taken for diagnosis (<5 minutes, 5–10 minutes and >10 minutes).

Pathological review pipeline

Two board-certified pathologists conducted a blinded, review of the slides using tele-cytology and direct-microscopy and documented the following features: multi-nucleation, mitotic figures, prominent nucleoli, altered nuclear-cytoplasmic ratio, hyper-chromatic nucleus, irregular nuclear membrane and any additional features as per their discretion. These features, adapted from the oral/pharyngeal cytological scoring system [23], were confirmed after a consensus from both the pathologists.

Tele-cytology diagnosis (remote diagnosis)

The two pathologists logged onto the secure server, demarcated specific cells in the uploaded images as region of interest (ROI) and documented its abnormal features. The subjects were annotated as ‘most likely positive’ or ‘most likely negative’ or ‘unable to interpret’.

Cytology diagnosis (direct microscopy)

Cytology diagnosis by direct microscopy was diagnosed by indicating the presence (1)/absence (0) of atypia on the basis of the cytological characters mentioned above. The cytology score was calculated for each case by the sum of the individual scores and compared with the histological diagnosis.

Histopathology diagnosis

The specimens were evaluated by routine histopathology and diagnosis was reported according to WHO criteria [24, 25]. Hyperkeratosis with epithelial hyperplasia and mild dysplasia were considered as low-grade dysplasia (LGD), while moderate dysplasia, severe dysplasia and carcinoma-in-situ were categorized as high-grade dysplasia (HGD) based on the binary system of classification [26].

Automated diagnosis and ANN workflow

A subset of subjects (n = 60) was selected for development and validation of a risk stratification model. Tele-cytology images were segmented to detect and segregate cells and labelled as atypical or normal by a pathologist (S3 Fig). These images were used for training an existing ANN (Inception V3) using transfer learning. All programming related to the ANN was implemented in Python (Google’s TensorFlow library; https://github.com/tensorflow/tensorflow). The learning was done for 4000 epochs with a learning rate of 0.01, wherein 90% of the dataset were used for training and 10% for validation. After training, each of the patients’ extracted cells was fed to the ANN to generate a score between 0 and 1. (0: most likely normal, 1: most likely atypical). The results of individual cells were aggregated for each patient; cells with scores above 0.5 (out of 1) were taken as atypia. The percentage of atypical cells, mean score of all cells and the mean score of atypical cells of individual patients were calculated. A classification learner model was developed using these values and validated.

Statistical analysis

The minimum sample size was calculated for screening study[27]. The prevalence of expected neoplastic and high grade dysplastic lesions were approximately 80% [20]. We expected 15% improvement in sensitivity [20, 28], considering the alpha value of <0.05, power of 80% and drop out (50%) the minimum sample size required for the study was 98.Therefore 100 subjects were recruited for the study including OPML and Malignant lesions.

Descriptive statistics were used to summarize details of patient demography, clinical features, and pathological diagnosis Normal distribution of continuous variables were tested using Kolmorgorov-smirnov test [29]. All statistical comparisons between multiple groups were assessed by one-way analysis of variance (ANOVA; Kruskal-Wallis test). Pearson correlation coefficient was used to find the correlation between the variables. The specificity, sensitivity and accuracy of tele-cytology, direct cytology and risk stratification model were computed. McNemar test was used to compare the proportions. The agreement between diagnoses was examined using Kappa statistics [30, 31]. ROC curve analysis was performed to find the cut-off score. P value less than 0.05 was considered statistically significant in all analyses. All statistical analyses were done using SPSS version 23 and MedCalc v14.8.1.

Results

Clinical and pathological details of the patients

Subjects recruited for the study were largely from southern states of India, including Karnataka, Tamil Nadu, and Kerala. A total of 100 patients were recruited for the study after written informed consent. The protocol, merits and confidentiality of the study was explained to all subjects in their native language. The consent form was provided to the patient in their respective language and was signed by them, their physician and the study investigator. Among 100 patients, biopsy could not be performed for 16 subjects, while two were rejected due to low cell number. Eighty-two patients were hence selected for further analysis; majority of the subjects were males (males: 78%; females: 22%) with a mean age of 45.4 years. Eighty-four percent (n = 69) subjects reported at least one of the high-risk habits: chewing, smoking or alcohol consumption. Demographic details were given in S1 Table. Patient consort chart indicating pathological diagnosis is provided (S4 Fig).

Tele-cytology shows good agreement with direct microscopy

The quality assessment of the tele-cytology images (n = 2880) in the patient cohort (n = 32) indicated that 96% (n = 2765) were of good quality. Assessment of time taken for diagnosis showed that the majority of the patients could be diagnosed within 10 minutes (61%), whereas conventional cytology-based evaluation recorded an average time of 15 minutes for diagnosis. Additionally, the diagnosis of carcinoma could be arrived at with a lesser number of images (n = 20) as compared to dysplasia (n = 100).

Remote diagnosis showed an overall average sensitivity of 81% (85–76) and average specificity of 90% (84–96) in detecting atypical cells (OSCC/HGD Vs LGD) when compared with direct microscopy (gold standard) (Table 1). Tele-cytology diagnosis revealed high PPV (90%; 86.7–93.3) and NPV (82%; 81.1–82.7). There was no significant difference in tele-cytology and conventional cytology diagnosis of either pathologist (McNemar’s test; pathologist I p-value: 1; pathologist II p-value: 0.06) with the Kappa value (0.67–0.72; p<0.05) indicating good agreement [30, 31].

Table 1

Sensitivity, specificity and accuracy of Tele-cytology and direct microscopy.

Test Vs reference standard		Pathologist I			Pathologist IIa
		Sensitivity	Specificity	Accuracy	Sensitivity	Specificity	Accuracy
OSCC / HGD Vs LGDb	Tele-cytology Vs direct microscopy	84.8	83.3	84.1	75.7	95.6	86.6
	Direct microscopy Vs HPc	61.4	75	63.4	67.9	96.6	78.1
	Tele-cytology Vs HP	60	75	62.2	54.7	96.6	69.5
OSCC Vs LGD	Tele-cytology Vs direct microscopy	94.7	72.7	89.8	77.8	93.3	84.9
	Direct microscopy Vs HP	92.1	72.7	87.76	92.1	96.4	93.9
	Tele-cytology Vs HP	94.7	72.7	89.8	76.3	96.4	84.85
HGD Vs LGD	Tele-cytology Vs direct microscopy	45.5	87.5	76.7
	Direct microscopy Vs HP	25	72.7	37.2
	Tele-cytology Vs HP	18.8	72.7	32.6

aPathologist II couldn’t detect atypical cells in HGD, LGD using cytology.

bOSCC = Oral squamous cell carcinoma, LGD = Low grade dysplasia, HGD = High grade dysplasia.

cHP = Histopathology diagnosis

Assessment of the efficacy of diagnosis between OPML and malignant lesions (OSCC) separately indicated a higher accuracy of diagnosis in malignant cases with both methods. Among the malignant lesions diagnosed by conventional cytology (35/38); 80–97% were diagnosed by tele-cytology (pathologist I: 97%; pathologist II: 80%), indicating a nearly perfect concordance between the two techniques. However, in the case of HGD, the diagnostic efficacy was only comparable between the two methods. Pathologist I diagnosed 25% (8/32) of HGD as atypia by conventional cytology, out of which 38% (3/8) were detected using tele-cytology. Notably, 3 cases of HGD missed by conventional cytology were diagnosed using tele-cytology, indicating a comparable overall detection efficacy (19%; 6/32). Pathologist II did not detect any of the HGD lesions by tele-cytology (Table 1).

Tele-cytology and direct microscopy correlate with neoplastic histology

In the diagnosis of the malignant cases (n = 38) both pathologist, pathologist I (95%, n = 36) and pathologist II (76%, n = 29), showed good sensitivity (76–95%) and specificity (73–96%) in comparison with histology. Tele-cytology diagnosis of HGD lesions (Pathologist I; 32/43) revealed a sensitivity and specificity of 18.8% and 72.7% respectively. The overall efficacy (in detecting OSCC and HGD lesion) of tele-cytology with histology as the gold standard were 62% and 69.5% respectively (Table 1).

The efficacy of conventional cytology using direct microscopy when compared to histology as the gold standard indicated that, as observed in the case of tele-cytology, the discrepancies were in the diagnosis of HGD. Among the 38 neoplastic cases, 92% (n = 35) of cases were diagnosed by both pathologists by direct microscopy. However, pathologist I and II detected atypia in only 25% (n = 8/32) and 7% (n = 1/15) of HGD lesions respectively by direct microscopy, when compared to their individual histological assessment.

To assess the efficacy of the cytology features adapted from the oral and pharyngeal cytological scoring system [23], each of them were individually assigned a score and total manual cytology score calculated for each subject. The most important cytological features sufficient for diagnosis were irregular nuclear membrane, abnormal cell shape and increased nuclear-cytoplasmic ratio (S2C–S2E Fig). Comparison of the cytology score with the histology diagnosis indicated that although these features could significantly delineate malignant patients from those with dysplasia (p<0.0001), they could not demarcate between HGD and LGD (p = 0.32).

Image processing algorithm to obtain individual cells

Each of the tele-cytology images were passed into an image processing algorithm (Fig 2A and 2B, S3 Fig) (ImageJ) wherein the cells were segmented, Field of View (FoV) boundaries detected and the cells masses (S5 Fig) detected based on the highest contrast in the green channel (RGB colour space). In case of presence of clumped cells, a watershed algorithm was used to approximately disconnect them [32].

Fig 2

Workflow of image processing and ANN.

Complete workflow (a) diagram of the automated diagnosis system; The cells are extracted from the tele-cytology images and are fed into a neural network and the values from all the cells in a patient are aggregated and used for developing risk stratification model. Image pre-processing algorithm (b) consisting of Field of View (FOV) extraction from the tele-cytology images, detection of contrasting cellular mass from the background, detachment of connected Region of Interests (ROIs), removing the artefacts outside the ROIs, and extracting cell ROIs. The graph represents validation accuracy during training (epochs = 4,000) of the ANN (c).

These images were further analysed with a particle analysis algorithm (ImageJ) and individual cells’ region of interests (ROIs) obtained. Each ROI was then cropped and verified against a set of criteria (S5 Fig and S6 Fig) [33]. Finally, these thresholded images were passed through the particle analysis tool to check the presence of a nucleus. The images of cells, which passed these quality checks, were then fed to the neural network for classification (S3 Fig). A total of 11,981 cell images from 60 patients were segmented, and an average of 200 cells per patient were obtained. Each tele-cytology image took less than a second to be segmented into individual cells (Fig 2B).

ANN scoring and risk stratification model correlate with tissue-specific diagnosis

The training set included cell images (normal: 252; atypical: 280) labelled by the pathologist as atypical (Fig 3A) or normal (Fig 3B) and taken randomly from six subjects (LGD: 3; OSCC: 3). These images were removed from the validation dataset. The images were augmented to generate a larger training set (n = 12,768 augmented images). The ANN (S7 Fig) was trained for 4000 epochs (Fig 2C) with final validation accuracy of 95%. Each cell takes approximately one second to get classified and on average 3 minutes for categorizing all cells of a patient. Cells that gave scores above 0.5 were assumed to be atypical cells. An increasing abnormality of the cell was correlated with increase in atypical scores (Fig 3C–3G). The percentage of atypical cells, mean score of all cells and the mean score of atypical cells were calculated for each patient by below formula.

Fig 3

A batch of trained and validated cell images.

Images of atypical cells(a) and normal cells (b) used for training the ANN. Cells classified by ANN: cells having atypical score less than 0.3 (c) from benign subjects, cells with atypical score between 0.3 to 0.5 from LGD patients(d), cells with atypical score between 0.5 to 0.7 (e) from HGD patients, cells with atypical score between 0.7 to 0.9 (f) from OSCC patients and cells with atypical score greater than 0.9 (g) from OSCC patients.

The average percentage of atypical cells in benign (BNG)/LGD, HGD and OSCC were 14%, 16% and 39%, respectively. Manual cytology score calculated by adding cytological features (Fig 4A), the mean score of all cells (Fig 4B) and percentage of atypical cells (Fig 4C) showed a statistically significant difference between dysplasia (HGD or LGD) and OSCC (p<0.005), but no significant difference between LGD and HGD (p>0.05). The mean score of atypical cells (Fig 4D) showed statistically significant difference between LGD and HGD (p<0.05). These three parameters were considered to make the risk stratification model.

Fig 4

Distribution of manual and ANN cytology scores.

Box and whisker plot represent (a) cytology score of direct microscopy method (n = 82), OSCC (4.08±1.92) score shows significant difference from (*p<0.005) LGD (0.63±1.12) and HGD lesions (1±1.05). ANN Scoring (n = 60): The mean score of all cells (b) shows statistical significance between dysplasia (HGD, LGD) and OSCC (0.40±0.08, *p<0.005), but does not show significant difference between LGD (0.17±0.09) and HGD (0.21±0.08). The percentage of atypical cells (c) OSCC (0.38±0.11) shows significant difference from dysplasia (*p<0.005) but not show significant between HGD (0.17±0.09), and LGD (0.17±0.09). The mean atypical score of atypical cells (cells having score >0.05) (d) in each patient demonstrating statistical significance between dysplasia (HGD, LGD) and OSCC (0.71±0.02, *p<0.005) and also between LGD (0.78±0.03) and HGD (0.76±0.03) (**p<0.05). The mean and standard deviation values are provided in brackets.

The risk stratification model included two tests; initially, a model was developed to delineate OSCC from HGD/LGD/Benign using the classification learner module (MatlabR2018a). The mean score of cells and percentage of atypical cells (which showed strong correlation; r = 0.992) of 50% patients (n = 30) were randomly selected for training classification model (Fig 5A). The model was then validated with another 30 patients using holdout validation. Among the linear models (MatlabR2018a classifier documentation) trained and compared (Support Vector Machine (SVM), Random forest, Logistic regression, Linear Discriminant Analysis and K-Nearest Neighbour) [34] (S2 Table), the SVM model gave the best accuracy, with a sensitivity and specificity of 93% (n = 14/15) and 88% (n = 13/15) respectively (Test 1, Fig 5C). The patients that the model predicted as positive were considered to have a high risk of OSCC, while the patients that predicted as negative were passed through the second test.

Fig 5

Risk stratification model.

Scatter plot (a) representing percentage of atypical cell and mean score of all cells (n = 60) showing high correlation (r = 0.992, CI = 0.986–0.995) and these variables used for test 1, in risk stratification model (SVM). The cut-off value of ROC curve analysis (b) in delineating OSCC from LGD were used in risk stratification model as test 2 (c).

The optimal threshold cut-off value for delineating OSCC from LGD was calculated from the mean score of atypical cells alone (Fig 5B). This cut-off was used to delineate HGD from LGD (Test 2, Fig 5C). The data from the subjects showing positive results were considered most likely to have HGD and OSCC. The negative subjects were considered to have LGD or Benign lesions (Fig 5C). The test gave a sensitivity and specificity of 73% (8/11) and 100% (3/3) respectively for delineating HGD from LGD/Benign. The entire model had an accuracy of 90% in delineating OSCC and HGD from LGD with 89% sensitivity, while the direct microscopy (same cohort, n = 30) had 59% sensitivity with an accuracy of 60% (Table 2).

Table 2

Sensitivity and specificity of manual cytology method and risk stratification model.

	OSCC Vs HGD/LGD	HGD/ LGD	OSCC/HGD Vs LGD	Accuracy
Cytology Manual Method (n = 30)
Sensitivity	87 (13/15)	25 (3/12)	59 (16/27)	60%
Specificity	73 (11/15)	66 (2/3)	67 (2/3)
Risk stratification model (n = 30)
	Test 1a	Test 2a
Sensitivity	93 (14/15)	73 (8/11)	89 (24/27)	90%
Specificity	88 (13/15)	100 (3/3)	100 (3/3)

aTest 1 and test 2 explained in risk stratification model (Fig 5)

Discussion

The estimated delay in diagnosis of oral cancer from the time a patient seeks medical assistance is three months [35]; lack of specialists’ expertise at the primary care settings being a major factor. Brush biopsy and cytology, are readily adaptable in primary health care centres [36] and are currently being investigated as a tool for oral cancer risk-stratification [37, 38]. The precision in detection and risk stratification can be further improved using machine learning algorithms, which also detaches the subjectivity of cytology interpretation. In this study, we evaluated the efficacy of a tele-cytology platform for oral cancer screening and developed a risk stratification model using an Artificial Neural Network. The results of the study the clinical efficacy of the platform and an improved accuracy in the diagnosis of OSCC/HGD with integration of ANN indicated that automated image capture/analysis provided the requisite information essential for point-of care remote diagnosis.

We have previously reported the feasibility of remote pathology diagnosis in oral cytology using a mobile and automated tele-cytology device [20], CellScope. This study showed that in addition to the high efficacy in capturing images (96% of images were of diagnostic quality), tele-cytology also showed high sensitivity/specificity in the diagnosis with good agreement (К = 0.68–0.72) with the conventional direct microscopy-based method. Similar ranges of agreement (К = 0.47 to 0.77) have been reported in tele-cytology-based diagnosis of cervical cancer [30, 38]. While the previous tele-cytology studies, necessitated a specialist intervention at the PoC [30, 38, 39], the tele-cytology pipeline detailed in this study enabled remote diagnosis facilitating risk stratification and appropriate triaging of patients by a FHW. The images could be transferred using the mobile cellular network with an adequate resolution of the cellular morphology for accurate interpretation, making it a potential tool in a low-resource setting. This prospective, blinded study using a tele-cytology platform, also showed a high level of concordance with conventional cytology by direct microscopy.

Although cytology could diagnose oral cancer with high efficacy, its ability to detect atypia in HGD lesions was low, which was underscored by the low sensitivity of both tele-cytology and direct microscopy in patients with HGD. This is in accordance with the poor performance of manual cytology to diagnose HGD reported in multiple studies [40, 41]. The most common cytological features considered for diagnosis in this study, though sufficient for the detection of OSCC (Fig 4A, cytology score >3), were not efficient for detecting HGD. Limitations of the brush biopsy in obtaining cells from the deeper layers due to high keratinization of stratified squamous epithelium [42] might be the primary reason, leading to subjectivity in diagnosis. This platform is hence inefficient in the detection of HGD with current standard cytological features (sensitivity = 25%). These challenges necessitated the use of objective machine learning solutions along with the tele-cytology platform to enable better stratification of patients.

Integration of automated diagnosis in this study included development of new image pre-processing algorithm that segmented individual cells (200-300cells/patient) within each image, removed clumped cells/artefacts. Further improvements in algorithms that can effectively utilise the clumped cells may increase the number of diagnosable images, however advanced segmentation algorithms available are computationally intensive [43-45]. We have not implemented such algorithms in the device keeping in mind that there will be low computational resources available at PoC. The Convolutional Neural Network (CNN) adopted in this study enables direct image input with the filters being trained to extract features automatically [21]. This bypasses the need to have well-defined criteria to detect HGD that incapacitated accurate manual diagnosis. ANN classification is robust in classifying cells that are over-segmented and in this study we used transfer learning, wherein a pre-trained ANN is fine-tuned using the new dataset, a method well-used for training small data set [46]. Inception V3, used in this study, was chosen due to its ability to provide better accuracy while using computational resources effectively [22] and is a network tested in various forms of cancer detection such as in cervix [47], skin [48], breast [49] and lung [50]. Additionally, if a need arises, the automated system allows for the images that are classified as atypical to be sent for remote pathologist review, lowering the network bandwidth required for image transfer and thus improving the throughput and reach of the pathologist.

The risk stratification model developed in this study adopted a sequential mode for patient stratification; it first detected malignancy (sensitivity: 93%) using linear SVMs (with 90% accuracy) and then delineated HGD (sensitivity = 73%). SVMs were shown to have good performance in a previous study for cytopathology-based DNA Index in oral leukoplakia [51]. The differential efficacy in the diagnosis of OSCC and HGD/LGD might be attributed to the presence of a higher percentage of atypical cells with a significantly higher mean score of all the cells imaged (p<0.05). Given this discrepancy in the percentage of atypical cells, in this model, delineation of HGD and LGD was based on the score of atypical cells alone. These criteria could distinguish the two dysplastic groups (p = 0.043). The entire automation involving image segmentation and risk stratification of a patient took around ten minutes making it as fast and appropriate as a point of care, tele-cytology tool.

Conclusion

The tele-cytology platform evaluated in this study is an effective tool for remote diagnosis since it could successfully retain features of diagnostic value. ANN-based automated diagnosis and risk stratification improved the sensitivity in detection of HGD lesions. This, in turn, increased the overall accuracy of the system by 30% when compared to the manual method. A study with a larger cohort is required to improve the robustness of the system in low resource environments. Nevertheless, this pilot study is a significant effort to improve the accuracy of oral cytology-based risk stratification and for enabling tele-cytology-based point of care diagnosis.

Cellscope slide scanning device.

Front View of the device (A) showing the scanning platform and top view showing the iPad mini 2 (B) as the user interface.

(TIF)

Click here for additional data file.

Web interface used in iPad for entering patient information and representative images.

An image, (A) captured by Cellscope (200X magnification) with resolution of 2592 x 1936 (72dpi) and cell with irregular nuclear membrane, (B, blue arrow). The images were zoomed in to 200% representing cell with irregular nuclear membrane, (C), abnormal cell shape, (D) and increased nuclear to cytoplasmic ratio, (E).

(TIF)

Click here for additional data file.

Detailed flowchart for image pre-processing.

An Input image, (A) was analysed to estimate the Field of View (FOV) and was trimmed to contain only the same, (B), once detected, the green channel from RGB image, (C) was considered to detect areas of cellular mass owing to its better contrast of cell mass. These images were thresholded, (D) to get cellular area. The binarized image is then analysed for extracting high level Region of Interests (ROI), (E) that is used to clear the background, (F). Images were then water segmented, (G) and again analysed for cellular-level ROIs, (H). Then each ROI, (I) is then extracted as a single image. The red channel, (J), is then obtained and thresholded, (I) to detect nucleus. The images are then checked with pass criteria and then saved, (L) into a folder.

(TIF)

Click here for additional data file.

Study consort chart.

Distribution of subjects according to clinical and histopathological diagnosis. Eighty subjects were included in the analysis, of which, 43 were OPML and 39 were malignant. For automated diagnosis, 22 subjects were excluded since their images were taken during the developmental stages of the tele-cytology platform and the images were very different from the final set of images. Thus (n = 60) subjects were considered for development and validation of ANN based diagnosis. OPML = Oral Potentially Malignant Lesion, HGD = High Grade Dysplasia, LGD = Low Grade Dysplasia, OSCC = Oral Squamous Cell Carcinoma.

(TIF)

Click here for additional data file.

Detailed flow chart for FOV estimation.

The algorithm finds out 3 pixels at the edge of the FOV in the input image, (A), Assuming the FOV is circular, the equation of circle (x−p)2+(x−p)2 = r2 representing the circular edge is solved to obtain the boundary of ROI, (B), a circular ROI is then extracted, (C).

(TIF)

Click here for additional data file.

Detailed criteria for detecting ROIs containing cells.

The first test (A) reduces the number of clumped cells based on size and aspect ratio. From the passed images, images with shadow artefacts are removed based on the ratio of mean channel intensities of red and green channels, (B), which are then again filtered based on Hematoxylin stained area, (C). Finally, the ROIs are analysed to find the presence of a nucleus, (D).

(TIF)

Click here for additional data file.

Inception v3 architecture.

The deep convolutional neural network used here to delineate between normal cells and atypical cells.

(TIF)

Click here for additional data file.

Showing demographics of subjects included in study.

(DOCX)

Click here for additional data file.

Machine learning model comparison.

Comparison of performance of various machine learning models used to delineate Oral Squamous Cell Carcinoma (OSCC) from High Grade Dysplasia (HGD) and Low-Grade Dysplasia (LGD).

(DOCX)

Click here for additional data file.

15 Aug 2019

PONE-D-19-15321

A smart tele-cytology point-of-care platform for oral cancer screening

PLOS ONE

Dear Dr. Kuriakose,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by Sep 29 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Fernando Schmitt, MD, PhD

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for including your ethics statement on the online submission form; "Narayana Health Medical Ethics Committee [NHH/MEC-CL-2014/222]. Written informed consent were obtained"

Please amend your current ethics statement to confirm that your named institutional review board or ethics committee specifically approved this study.

Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

3. In the Methods, please report the sample size that was calculated for this study.

4. In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as: a) the recruitment date range (month and year), b) a description of any inclusion/exclusion criteria that were applied to participant recruitment, c) a table of relevant demographic details, d) a statement as to whether your sample can be considered representative of a larger population, e) a description of how participants were recruited, and f) descriptions of where participants were recruited and where the research took place.

5.  Thank you for stating the following in the Financial Disclosure section:

This work was supported by the Wellcome Trust/DBT India Alliance Fellowship [IA/RTF/15/1/1017] awarded to Sumsum Sunny

https://www.indiaalliance.org/fellowsprofile/dr-sumsum-sunny--270

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We note that one or more of the authors are employed by a commercial company: Siemens Healthcare Pvt Ltd and Siemens Healthineers

1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

6. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files

Additional Editor Comments:

Please address the questions of the reviewer.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors evaluated the efficacy of a tele-cytology platform for oral cancer screening and developed a

risk stratification model using an Artificial Neural Network. The study is very interesting and designed in detailed.

Reviewer #2: This article is very well written and demonstrates the potential of a practical approach using artificial intelligence to provide adequate assistance to PoC. It is clearly described. It would be useful for the reader to have the resolution of CellScope specified in pixels/micrometer as well as the optical resolution used. The former was not described in your previous study entitled: 'Mobile microscopy as a screening tool for oral cancer in India: A pilot study.' Such data would also help to highlight that cytologic diagnosis can be performed under lower optical resolutions.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

17 Oct 2019

Response to Editor’s/Reviewers Comments

We thank the Editor and the Reviewers for their comments. Their comments have enabled us to improve the manuscript. Please see the response to each comment listed below.

Response to Editor’s comments

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: Thank you for reviewing the article. We have revised manuscript and corrected according to PLOS ONE’s style.

2. Thank you for including your ethics statement on the online submission form; "Narayana Health Medical Ethics Committee [NHH/MEC-CL-2014/222]. Written informed consent were obtained"

Please amend your current ethics statement to confirm that your named institutional review board or ethics committee specifically approved this study.

Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

Response: Our current ethics committee has reviewed and approved the study.

We have amended the ethics statement to include this in the revised manuscript. (Page 5; line 116). We have also made the change in the submission form.

3. In the Methods, please report the sample size that was calculated for this study.

Response: The minimum sample size for screening was calculated as previously established [1]. The prevalence of expected neoplastic and high grade dysplastic lesions were approximately 80% [2]. We expected sensitivity of 80% and with 15% improvement [2, 3], considering the alpha value of <0.05 and power of 80%, the minimum sample size required for the study was 63 including Low grade–OPML (n=7), High Grade- OPML (n=28) and OSCC (n=28). We expected a drop out of 50% due to lack of biopsy, poor images and less number of cells. Therefore 100 subjects were recruited for the study including OPML and Malignant lesions.

This information has been included in the manuscript (Page 8; line 169).

4. In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants.

Response: Please see the response to each comment below

a) The recruitment date range (month and year)-

Response: The patient recruitment period was October 2014 to September 2016. The details have been added in the revised manuscript (page 5; 116).

b) a description of any inclusion/exclusion criteria that were applied to participant recruitment

Response: Subjects who are clinically diagnosed with oral potentially malignant and malignant lesion and given consent for the study were included and subjects who are already undergone biopsy for the oral lesions were excluded from the study (page 5; 117).

c) a table of relevant demographic details,

Response: Demographic details were added in revised manuscript as a supplementary table S1 (Table is referred to in the manuscript, Page 9; line 196).

d) a statement as to whether your sample can be considered representative of a larger population

Response: Subjects recruited for the study were largely from southern states of India, including Karnataka, Tamil Nadu, and Kerala. The sample can hence be considered representative of the South Indian population. (page 9; 187)

e) a description of how participants were recruited, and

Response: The subjected who meet inclusion and exclusion criteria were consented for the study after the study protocol is explained in detail. The protocol, merits and confidentiality of the study was explained to all subjects in their native language. The consent form was provided to the patient in their respective language and was signed by them, their physician and the study investigator (Page 5; line 117, Page 9; line 189).

f) description of where participants were recruited and where the research took place.

Response: The study was carried out in subjects recruited from the out-patient clinics of Department of Oral Medicine and Radiology, KLES Institute of Dental Sciences, Bangalore and Head and Neck Oncology Department, Mazumdar Shaw Medical Center, Bangalore, India (Page 5; line 113). The pathology review of the slides/images was carried out at the department of pathology of the two centres. Image analysis, AI was carried out at Integrated head and neck oncology program, MSMF and at the Department Biomedical and Electronic Engineering Systems Laboratory, IISc.

5. Thank you for stating the following in the Financial Disclosure section:

This work was supported by the Wellcome Trust/DBT India Alliance Fellowship [IA/RTF/15/1/1017] awarded to Sumsum Sunny https://www.indiaalliance.org/fellowsprofile/dr-sumsum-sunny--270

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We note that one or more of the authors are employed by a commercial company: Siemens Healthcare Pvt Ltd and Siemens Healthineers

1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

Response: The authors from Siemens Healthcare Pvt Ltd and Siemens Healthineers have supported the study by providing the instrument, Cellscope and IT support for the instrument and reviewed the final manuscript. They were also involved in managing the collaboration between the multiple centres.

The funding statement has also been modified as “The funder provided support in the form of salaries for authors [DBT Alliance fellowship: SPS; Siemens Healthcare Pvt Ltd and Siemens Healthineers: LL, PG, MK], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

We have amended the Authors Contribution section on Online submission accordingly.

2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

Response: The Competing interests statement now includes the statement “This does not alter our adherence to PLOS ONE policies on sharing data and materials”

Both the Competing Interests Statement and the Funding Statement have been included in the cover letter.

6. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files

Response: Thank you for the comment. We have added the tables in main manuscript and removed individual files. Supplementary files have been retained separately.

Response to Reviewer's Comments

Reviewer #1: The authors evaluated the efficacy of a tele-cytology platform for oral cancer screening and a risk stratification model using an Artificial Neural Network. The study is very interesting and designed in detailed.

Response: We thank the Reviewer for reviewing the manuscript and for their positive comments.

Reviewer #2: This article is very well written and demonstrates the potential of a practical approach using artificial intelligence to provide adequate assistance to PoC. It is clearly described. It would be useful for the reader to have the resolution of CellScope specified in pixels/micrometer as well as the optical resolution used. The former was not described in your previous study entitled: 'Mobile microscopy as a screening tool for oral cancer in India: A pilot study.' Such data would also help to highlight that cytologic diagnosis can be performed under lower optical resolutions.

Response: Thank you for reviewing the article and for all encouraging comments. We agree that information regarding the resolution will be important for the reader. The resolution of Cellscope is 1.9 pixel/micrometre (optical resolution is 200x).

We have added this in revised manuscript (Page 6; line 121).

References

1. Bujang MA, Adnan TH. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. Journal of clinical and diagnostic research : JCDR. 2016;10(10):YE01-YE6. doi: 10.7860/JCDR/2016/18129.8744. PubMed PMID: 27891446; PubMed Central PMCID: PMC5121784.

2. Skandarajah A, Sunny SP, Gurpur P, Reber CD, D'Ambrosio MV, Raghavan N, et al. Mobile microscopy as a screening tool for oral cancer in India: A pilot study. PloS one. 2017;12(11):e0188440. doi: 10.1371/journal.pone.0188440. PubMed PMID: 29176904; PubMed Central PMCID: PMC5703562.

3. Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. Journal of biomedical informatics. 2014;48:193-204. doi: 10.1016/j.jbi.2014.02.013. PubMed PMID: 24582925.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

24 Oct 2019

A smart tele-cytology point-of-care platform for oral cancer screening

PONE-D-19-15321R1

Dear Dr. Kuriakose,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Fernando Schmitt, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

31 Oct 2019

PONE-D-19-15321R1

A smart tele-cytology point-of-care platform for oral cancer screening

Dear Dr. Kuriakose:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof Fernando Schmitt

Academic Editor

PLOS ONE