Correlation between histogram-based DCE-MRI parameters and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes.

OBJECTIVES: To investigate the correlation between histogram-based Dynamic Contrast-Enhanced magnetic resonance imaging (DCE-MRI) parameters and positron emission tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) values in oropharyngeal squamous cell carcinoma (OPSCC), both in primary tumors (PTs) and in metastatic lymph nodes (LNs).
METHODS: 52 patients with a new pathologically-confirmed OPSCC were included in the present retrospective cohort study. Imaging including DCE-MRI and 18F-FDG PET/CT scans were acquired in all patients. Both PTs and the largest LN, if present, were volumetrically contoured. Quantitative parameters, including the transfer constants, Ktrans and Kep, and the volume of extravascular extracellular space, ve, were calculated from DCE-MRI. The percentiles (P), P10, P25, P50, P75, P90, and skewness, kurtosis and entropy were obtained from the histogram-based analysis of each perfusion parameter. Standardized uptake values (SUV), SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated applying a SUV threshold of 40%. The correlations between all variables were investigated with the Spearman-rank correlation test. To exclude false positive results under multiple testing, the Benjamini-Hockberg procedure was applied.
RESULTS: No significant correlations were found between any parameters in PTs, while significant associations emerged between Ktrans and 18F-FDG PET parameters in LNs.
CONCLUSIONS: Evident relationships emerged between DCE-MRI and 18F-FDG PET parameters in OPSCC LNs, while no association was found in PTs. The complex relationships between perfusion and metabolic biomarkers should be interpreted separately for primary tumors and lymph-nodes. A multiparametric approach to analyze PTs and LNs before treatment is advisable in head and neck squamous cell carcinoma (HNSCC).

Introduction

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide [1]. In the last few decades, there has been an increase in the incidence of oropharyngeal squamous cell carcinoma (OPSCC) related to human papilloma virus (HPV), a distinct entity from the traditional tobacco- and alcohol-related OPSCC [2].

Magnetic resonance imaging (MRI) and positron emission tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) are the current diagnostic imaging methods for staging and treatment monitoring of HNSCC [3-5]. In recent years, dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) have also been introduced in clinical practice to obtain a more comprehensive characterization of HNSCC, based on functional parameters related to tissue microvascular properties and cellularity, respectively [6].

Concurrently, some histopathological parameters, such as p16 expression and proliferation index, measured from KI67 labelling, have been proposed to predict the tumor behaviour in HNSCC, as they can provide information about tumor aggressiveness, prognosis, and therapy response [7-8]. Other biomarkers, such as epidermal growth factor receptor (EGFR) and tumor suppressor protein p53 expression, have been investigated for their potential capability to support personalized treatment protocols, enabling the categorization of patients into different risk groups [9].

Considering the volume of data that can be derived from both histology and functional imaging, radiologists and clinicians should be aware of the different potential roles of several biomarkers with respect to specific clinical end points. To this purpose, a number of reports have recently evaluated the complementarity and/or associations between imaging and histopathological features in HNSCC [10-15], as well as in different malignancies, i.e. breast cancer, lung adenocarcinoma and glioma [16-18]. The ultimate goal of this is to determine which parameters or their combinations could be appropriately used in clinical practice for a more precise diagnosis and treatment of these cancers. It was found that the apparent diffusion coefficient (ADC) is able to predict cell count and proliferation activity, while although SUVmax may predict expression of HIF-1α, it is not a good surrogate marker for KI67 labelling and p53 expression [10]. DCE-MRI parameters also were demonstrated to be related to different histopathological features, such as vessel count, total vessel area [11] and microvessel density [15].

A better understanding of the complex interactions between functional imaging parameters is also advisable, as it may expand our knowledge of tumor biological characteristics, with potential clinical implications for treatment planning, prediction of treatment response and patient outcome. Several investigations have already focused on the relationships between DCE-MRI and 18F-FDG-PET/computed tomography (CT) parameters, even though these results are conflicting in HN tumors [19-25]. Most of the previous studies on DCE-MRI and/or PET/CT have only evaluated primary tumors [19-21,23,24,26], while only a small number of investigations also included the metastatic lymph nodes (LNs) [25,27-28]. Furthermore, limited research has addressed vascular heterogeneity within the lesion, using a histogram-based approach instead of the mean values of parameters, to better reveal the relationships between perfusion and metabolic variables [19,22].

Thus, the aim of our study was to further investigate the relationships between DCE-MRI and 18F-FDG-PET/CT parameters in OPSCC. To our knowledge, this is the first study investigating the correlation between histogram-based analysis of DCE-MRI parameters and volumetric 18F-FDG-PET values in a large and homogeneous population of OPSCC, both in primary tumors (PTs) and in metastatic LNs.

Materials and methods

Patient population

This cohort study was conducted at the IRCCS Regina Elena National Cancer Institute, Rome, Italy. It was conducted retrospectively on a patient population that was also included in a larger prospective study funded by the Italian Association for Cancer Research (project No. 17028) in OPSCC, aiming to investigate the ability of DCE-MRI and DWI to predict tumor response to chemo-radiotherapy.

The study was authorized by the hospital ethics committee i.e. `Central Ethics Committee, IRCCS LAZIO, IFO' with a reference number of N1214/19. Patient records have been anonymized at the end of the study to create an anonymous database, which has been provided as Supporting Information file. Due to the retrospective nature of the study and the lack of published data that could have supported a specific hypothesis for a conventional sample size calculation, we considered a sample size of 50 patients as adequate, based on the number of patients coming into our institute in the observational period selected.

Inclusion criteria were: (i) aged 18 years or older; (ii) Karnofsky performance status > 80; (iii) pathologically confirmed OPSCC; (iv) stage III or IV without distant metastases according to the 8th edition of American Joint Committee on Cancer (AJCC) staging system; (v) treatment with radiotherapy ± chemotherapy; (vi) DCE-MRI and 18F-FDG PET/CT performed at our institute during diagnosis. Exclusion criteria were: (i) any contraindication to MR examination; (ii) the presence of artifacts in the images that do not allow a quantitative evaluation; (iii) prior surgery or chemoradiotherapy to the primary disease and the neck. Specific informed consent was obtained from each patient.

All patients' tissue samples and medical records were accessed between November 2018 and April 2019. Demographic data of the enrolled patients were obtained and tumor subsites were recorded. T and N classifications were (re)staged according to the 8th edition of AJCC staging system.

HPV testing

HPV-positive OPSCCs were identified by using both p16 immunohistochemistry and PCR-based detection techniques. HPV-positive patients were defined as those with both p16 and HPV-DNA positivity [29].

Formalin-fixed paraffin-embedded (FFPE) tissue was obtained from patients and each block was sectioned into 1–3 x 5 μm slices, depending on the tissue size available. DNA was purified using the DNeasy Blood and Tissue Kit (Qiagen). The PCR-based INNO-LiPA HPV Genotyping Extra II kit (Fujirebio) and TENDIGO™ instrument (Fujirebio) were used to detect and genotype HPV-DNA. This assay allows the identification of 32 high risk and low risk HPV types.

The p16 protein expression was assessed using the CINtec® Histology Kit (Roche Diagnostics, Milan, Italy). The staining was evaluated according to the AJCC (American Joint Committee on Cancer) Staging Manual, 8th Edition.

Histological grading of OPSCC was described according to the AJCC Staging Manual. Specifically, histological grading has been applied only for the HPV-negative OPSCCs, as no grading system currently exists for HPV-positive OPSCCs [30].

MR imaging protocol

The MRI exams were acquired with a 1.5-T system (Optima MR 450w, GE Healthcare, Milwaukee, WI) with 16-channels receive-only RF coils: a head, a surface neck, and a spine coil.

The MRI protocol included coronal fast spin-eco (FSE) T2-weighted images (acquisition matrix: 288×256, field of view: 27×27 cm, TR/TE: 5901/102 ms; slice thickness: 4 mm), axial FSE T2-weighted images (acquisition matrix: 288×256, field of view: 20×20 cm, TR/TE: 6844/105 ms; slice thickness: 3 mm), and pre-contrast axial T1-weighted images (acquisition matrix: 288×256, field of view: 20×20 cm, TR/TE: 617/8.1 ms; slice thickness: 3 mm), all acquired from the skull base to the level of the thoracic inlet. Axial DWI was obtained via single-shot spin-echo and echo-planar imaging (acquisition matrix: 128×128; field of view: 26×28 cm; TR/TE: 4500/77 ms; slice thickness: 3 mm, b value: 0-500-1000). DCE-MRI involved a 3D fast-spoiled gradient echo sequence, with a TR/TE of 4.9/1.60 ms, flip angle 30°, acquisition matrix 128X128, field of view 28 cm, number of slices 20, slice thickness of 4 mm, no spacing. Sixty dynamic volumes were acquired consecutively, with a temporal resolution of 5 s, and a total scanning time of 5 min and 15 s. At the fourth dynamic volume, 0.1 mmol/kg body weight of gadopentetate dimeglumine contrast agent was administered intravenously, at a rate of 3 ml/s. After contrast administration, axial and coronal T1-weighted images with liver acquisition with volume acceleration sequences were acquired (LAVA; acquisition matrix: 288×288, field of view: 26×26 cm, TR/TE 9.8/3 ms; slice thickness: 1 mm, acquisition time of 2.05 min).

18F-FDG-PET /CT image acquisition

Combined PET/CT imaging was performed using a non-TOF (Time of Flight) tomography (Biograph 16, Siemens). All patients fasted for at least 6 hours prior and were preconditioned to have a blood glucose level <150 mg/dl at the time of injection of FDG. 18F-FDG-PET/CT acquisition was performed 60±10 min. after intravenous (i.v.) injection of an average dose of 5 MBq/Kg of 18F-FDG. A non-contrast enhanced CT scan from the base of the skull to the upper thighs was acquired for anatomical localisation and attenuation correction of PET images, with the following parameters: 120–140 kV, 4 mm slice thickness. PET data were acquired in 3D mode immediately after the CT scan, taken for 2–3 minutes at each bed position. PET images were reconstructed by the TrueX algorithm, that employs a system matrix with point spread function modelling, with three iterations and 21 subsets. After reconstruction the images were filtered by a Gaussian filter with a full width at half maximum of 4 mm. PET images were finally corrected for attenuation using data from the CT scan.

DCE-MRI analysis and tumor delineation

A commercial software package (GenIQ General, GE Advanced Workstation, Palo Alto, CA) was used to analyze the DCE-MRI data. A pharmacokinetic modeling based on two compartments (plasma space and extravascular-extracellular space) was applied to obtain the following quantitative parameters: Ktrans, the transfer constant between plasma and the extravascular extracellular space (EES), Kep, the transfer constant between EES and plasma and ve, the fractional volume of EES [31]. MIM software (v.6.4.2, MIM Software Inc., USA) was used to visualize axial T2-weighted images and manually delineate the volume of the PT and the largest metastatic LN, if present, by an expert HN radiologist with more than 20 years of experience (A.V.). Arterial or venous structures, bony components and macroscopic necrosis were excluded from the lesions. The lesion contours, as well as the perfusion maps of Ktrans, Kep and ve were uploaded to the Matlab workspace (Release 7.10.0, The Mathworks Inc., Natick, MA), where dedicated scripts were developed for subsequent quantitative analyses. From the volumetric histogram of each perfusion parameter, the following eight variables were calculated: skewness, kurtosis, and entropy, as well as the 10th, 25th, 50th (median value), 75th and 90th percentiles.

The same bin size was used for each patient to calculate the histogram distribution of the parameters within the lesion; in particular, the bin sizes were 0.05 min−1, 0.3 min−1, and 0.02 for Ktrans, Kep, and ve, respectively. The volume size of each PT and LN was also quantified using MIM software and recorded.

18F-FDG-PET/CT analysis and tumor delineation

A nuclear medicine specialist with 10 years of PET experience (R. P.) reviewed all 18F-FDG-PET/CT images from a dedicated workstation (SyngoVia, Siemens). PET images were analysed both qualitatively (presence/absence of tracer uptake outside sites of physiological accumulation or excretion) and semi-quantitatively. For the latter approach, a volumetric volume of interest (VOI) was placed over the PT and the largest LN. To ensure consistency in the identification of the chosen LN, the delineation was done in consensus with the radiologist. A threshold of 40% SUVmax was used to obtain the metabolic tumor volume (MTV), from which SUVmax, SUVpeak, SUVmean, and the total lesion glycolysis (TLG) were automatically derived. Adjacent FDG-avid structures and areas exhibiting physiological uptake were excluded.

Statistics

All variables were synthesized through absolute and percentage frequencies and via median values and their relative ranges, when appropriate. Median rather than mean values were used for the analyses, given that the median is less affected by outliers and skewed data. The correlations between all variables were assessed using the Spearman rank correlation test. To exclude false positive results under multiple testing, the Benjamini-Hockberg procedure with a false discovery rate (FDR) of 0.05 was applied.

The paired-sample Wilcoxon signed rank test was used to investigate the differences in imaging parameters between PTs and LNs. The Mann-Whitney test was used to explore the differences between the imaging variables by the HPV status. A p<0.05 was considered statistically significant. The analyses were carried out with SPSS version 21.

Results

From January 2016 to October 2018 a total of 52 patients affected by OPSCC were retrospectively enrolled in the present study. Patient and tumor characteristics are summarized in Table 1.

Table 1

Patient and tumor characteristics.

Characteristic		N
Patients		52
Gender	Male	44 (84.6%)
	Female	8 (15.4%)
Age		62.32
(years, mean, SD)		(9.38)
Tumor site	Tonsil	27
	Base of the tongue	24
	Both	1
T stage	T1	7
	T2	11
	T3	5
	T4	29
N stage	N0	3
	N1	19
	N2	22
	N3	8
Primary tumor volume (cm3,SD)		18.0 (15.9)
Lymph-nodes volume (cm3,SD)		11.2 (12.4)
Time Interval between MRI and PET-CT (days, SD)		16 (15)
HPV	+	33 (63.5%)
	-	19 (36.5%)

Abbreviations: SD, standard deviation; HPV, human papilloma virus.

Out of 52 patients, 33 were HPV positive and 19 were HPV negative, of whom 13 were graded as G3, 4 as G2 and 2 were without available grading. In 4 patients, evaluation of the PT by DCE-MRI and 18F-FDG-PET/CT was not possible because the primary lesion was not visible or too small (< 0.5 cm3). In 7 patients, evaluation of the LNs was not feasible because the patients were N0 (3/7), the DCE-MRI did not entirely include the LN (2/7), or the LN was too small (2/7).

Summary statistics of all the variables derived from DCE-MRI and 18F-FDG-PET/CT are reported in Tables 2 and 3.

Table 2

Summary statistics of DCE-MRI parameters in primary tumors (PTs) and metastatic lymph nodes (LNs).

Parameter		PT (N = 48)		LN (N = 45)
		median	IQR	median	IQR	P
	P10	0.37	0.21	0.27	0.16	0.009
	P25	0.53	0.28	0.42	0.19	0.012
	P50	0.71	0.38	0.58	0.32	0.030
	P75	0.98	0.61	0.88	0.58	0.161
Ktrans	P90	1.27	0.92	1.17	0.96	0.595
	Skewness	2.12	1.26	2.30	1.61	0.512
	Kurtosis	10.88	11.27	10.71	15.48	0.442
	Entropy	4.84	0.98	4.54	1.72	0.042
	P10	0.88	0.40	0.72	0.50	0.002
	P25	1.28	0.60	1.12	0.68	0.133
	P50	1.84	0.92	1.76	1.04	0.514
	P75	2.64	1.68	2.48	1.92	0.677
Kep	P90	3.68	2.83	3.52	2.92	0.408
	Skewness	5.64	8.75	4.48	4.77	0.032
	Kurtosis	69.4	212	40.3	86.9	0.024
	Entropy	3.72	1.18	3.59	1.17	0.648
	P10	0.24	0.14	0.17	0.16	0.001
	P25	0.33	0.15	0.24	0.13	0.002
	P50	0.41	0.17	0.31	0.16	0.004
	P75	0.48	0.15	0.39	0.16	0.016
ve	P90	0.54	0.17	0.49	0.21	0.051
	Skewness	0.37	0.90	0.52	0.95	0.154
	Kurtosis	4.55	2.19	3.86	2.64	0.253
	Entropy	4.60	0.69	4.49	0.65	0.183

Abbreviations: IQR, interquartile range; Ktrans (min-1), transfer constant between plasma and EES (extravascular extracellular space); Kep (min-1), transfer constant between EES and plasma; ve, fractional volume of EES; P10, P25, P50, P75, P90 are 10th, 25th, 50th, 75th and 90th percentiles of the volumetric distribution of each parameter inside PT/LN. P values refer to the paired-sample Wilcoxon signed rank test. Statistically significant p-values are bold.

Table 3

Summary statistics of 18F-FDG-PET parameters in primary tumors (PTs) and metastatic lymph nodes (LNs).

Parameter	PT (N = 48)		LN (N = 49)
	median	IQR	Median	IQR	P
SUVmax	17.16	9.91	10.38	8.24	<0.001
SUVpeak	13.03	6.62	6.54	6.49	<0.001
SUVmean	10.24	5.60	6.06	4.84	<0.001
SD	2.48	1.24	1.55	1.31	<0.001
TLG	86.76	88.68	20.18	72.47	0.005
MTV	8.49	9.02	4.95	7.33	0.034

Abbreviations: IQR, interquartile range; SUVmax, maximum standardized uptake, SUVpeak peak standardized uptake within 1 cm3; SUVmean, mean standardized uptake; SD, standard deviation of SUV values; TLG, total glycolysis volume; MTV, metabolic tumor volume. P values refer to the paired-sample Wilcoxon signed rank test. Statistically significant p-values are bold.

PTs showed significantly higher Ktrans and ve values, particularly for P10, P25 and P50 percentiles. PTs also showed significantly higher Kep P10, and Kep skewness and kurtosis. At the same time, all 18F-FDG-PET parameters were larger in PTs than in LNs.

No significant correlation was found between DCE-MRI and 18F-FDG-PET parameters in PTs (data reported in S1, S2 and S3 Tables), while significant associations emerged between variables derived from Ktrans and 18F- FDG-PET in LNs, as shown in Table 4.

Table 4

Results of Spearman's correlation tests between Ktrans and 18F-FDG-PET parameters in lymph nodes (N = 45).

Variables		SUVmax	SUVpeak	SUVmean	SD	TLG	MTV
P10	Rho	-.375	-.330	-.369	-.305	-.098	.001
	P	.011	.027	.013	.042	.524	.993
P25	Rho	-.384	-.346	-.378	-.309	-.168	-.081
	P	.009	.020	.011	.039	.271	.599
P50	Rho	-.405	-.372	-.394	-.331	-.226	-.126
	P	.006	.012	.007	.026	.135	.408
P75	Rho	-.429	-.424	-.421	-.374	-.348	-.236
	P	.003	.004	.004	.011	.019	.119
P90	Rho	-.433	-.443	-.438	-.397	-.402	-.270
	P	.003	.002	.003	.007	.006	.073
skewness	Rho	.269	.221	.234	.222	.123	.054
	P	.075	.145	.122	.144	.419	.724
kurtosis	Rho	.285	.254	.260	.243	.199	.110
	P	.057	.092	.085	.107	.190	.472
Entropy	Rho	-.296	-.299	-.279	-.234	-.273	-.223
	P	.048	.046	.063	.121	.070	.141

Statistically significant p-values after applying Benjamini-Hockberg correction are bold (the corrected p-value threshold is 0.014). Abbreviations as in Tables 2 and 3.

Data relative to the correlations between Kep/ve and 18F-FDG-PET parameters in LNs are reported in the S4 and S5 Tables.

In HPV-positive patients, the kurtosis of ve of PTs was higher than in HPV-negative patients (p = 0.009), while the MTV of LNs was larger (p = 0.025). No other significant difference in imaging parameters by HPV status was found.

Two representative cases are illustrated in Figs 1 and 2.

Fig 1

53-year-old man affected by HPV-positive oropharyngeal squamous cell carcinoma of the base of the tongue with a large metastatic lymph-node in the left IIa level, as shown on axial T2-weighted image (a). Ktrans map (b) indicates heterogeneous Ktrans levels in the metastatic lymph-node with a low median value of 0.27 min-1. Correspondently, a high 18F-FDG uptake (SUVmax: 14.48; SUVpeak: 10.5; SUVmean: 8.76) was found, as illustrated in 18F-FDG PET/CT image (c). Histogram of Ktrans values within the entire lymph node is shown (d).

Fig 2

72-year-old man affected by HPV-positive oropharyngeal squamous cell carcinoma of the base of the tongue with enlarged metastatic lymph-nodes in the left IIa/IIb level is shown on axial T2-weighted image (a). Ktrans map (b) indicates high Ktrans levels in the lymph-nodes, of which the largest posterior node was analyzed, with a median Ktrans of 2.08 min-1. Correspondently, a low to intermediate 18F-FDG uptake (SUVmax: 6.09; SUVpeak: 5.82; SUVmean: 4.2), was found, as illustrated in 18F-FDG PET/CT image (c). Histogram of Ktrans values within the entire lymph node is shown (d).

Discussion

A multiparametric approach to analyze primary tumors and nodal masses before treatment is advisable in HNSCC, mainly to clarify the complex associations between multiple imaging-based functional biomarkers. These biomarkers have been demonstrated to be useful for differential diagnosis, as well as for predicting and monitoring the treatment response in HNSCC [5,32-35].

Previous studies have focused on the correlation between perfusion and metabolic imaging in HNSCC, using DCE-MRI and 18F-FDG PET or 18F-FMISO (fluoromisonidazole) PET [19-28], as well as with simultaneous PET/MR systems [21,23,32,36]. However, most of these studies investigated perfusion and metabolic parameters in the PT [19-21,23,24,26], while only a few investigations have evaluated the metastatic LNs [25,27,28], reporting conflicting results.

In the present study, we analyzed a homogeneous patient population of OPSCCs, both in PTs and LNs, and found no significant correlation between DCE-MRI and 18F-FDG-PET in PTs but evident relationships between Ktrans and 18F-FDG-PET in LNs.

Prior to performing these analyses, we had explored the potential influence of the HPV status on perfusion and MTV. It is known that HPV-related OPSCC represents a distinct subtype of HNSCC with unique molecular pathogenesis, clinical presentation and prognosis [37]. However, the percentiles of each perfusion parameter, as well as SUVmax, SUVpeak, SUVmean, did not significantly differ by HPV status. However, the MTV of LNs was found to be higher in the HPV-positive group than in the HPV-negative one. Our results on DCE-MRI are in line with previous investigations that did not report any difference in perfusion parameters according to HPV status, for both PTs and metastatic LNs [22,28]. While conflicting results have been reported on the association between 18F-FDG-PET/CT parameters and HPV-status, some studies documenting SUV values of PTs have shown that these are lower in HPV-positive than in HPV-negative patients [38-40]. Others have shown no significant difference in the metabolic parameters in nodal metastases [39], as supported by our findings. The lack of significant differences between imaging parameters derived from DCE-MRI and FDG-PET in head and neck cancer by p16 status has also recently been reported by Cao et al. [41].

The larger MTV of LNs in HPV-positive patients may be explained by considering that patients with HPV-related OPSCC are more likely to have a higher N-stage than patients with non-HPV-related OPSCC [38], thus generally exhibiting larger volumes and glycolytic indexes of LNs [39].

The DCE-MRI and 18F-FDG-PET parameters of PTs in our study were similar to those reported by Bisdas et al. [24], who investigated the relationships between vascular and metabolic characteristics in primary HNSCC. They found no relationship between SUVmax/SUVmean and Ktrans/Kep, but a significant correlation emerged between SUVmean and ve, which contradicts our present findings. This discrepancy may be attributed to differences in the patient population, as we analyzed a larger and homogenous HNSCC population, in the acquisition protocols of both DCE-MRI and 18F-FDG-PET/CT, and/or in the methods for image analysis.

As suggested by more recent literature [19], we performed a histogram-based analysis of the DCE-MRI parameters, to consider the vascular heterogeneity within the lesion, and potentially increase the ability to demonstrate associations between perfusion and metabolic variables. It has already been reported that SUVmax is related to Kep P10, and that P25 and TLG tended to be related to Kep P25 and Ktrans P10 in primary HNSCCs [19]. Moreover, it has been suggested that the evidence of these correlations may also depend on tumor grading, with G1/G2 PTs showing significant associations, while no correlations are evident in G3 PTs [19, 13]. This may partially explain the lack of correlations in PTs emerging from our study: in our cohort, HPV-negative OPSCCs were predominately G3 (13/19 patients) while no grading system currently exists for HPV-positive OPSCCs according to the American Joint Committee on Cancer Staging Manual [30].

It should also be stressed that, unlike other investigators, we applied a p-value correction to exclude false positive results under multiple testing, and this may have contributed to reducing the evidence of correlations, as well as corroborating our findings.

Concerning the LNs, all Ktrans percentiles showed strong associations with SUV values, with Ktrans P90 also correlating with TLG. Our data suggest that the complex relationships between perfusion and metabolic biomarkers should be interpreted separately for PTs and LNs. This may be attributed to the differences in tissue microvascular architecture between the PT and the pathological lymphadenopathy [42]. This difference between PTs and LNs is also compounded by the fact that LNs had significantly lower vascular and metabolic values than PTs. This is in line with Fischbein et al. [42], who observed that semi-quantitative perfusion parameters of LNs, as peak enhancement and maximum slope of signal increase, were unexpectedly lower in tumor-involved compared with non-tumor- involved LNs. This may suggest that, especially for reactive nodal tissue, the tumor does not necessarily show higher vascularity compared with normal lymphoid tissue.

Recently, possible associations between 18F-FDG-PET and microvessel density (MVD) have been evaluated in HNSCC [43]. MVD assessments have been proposed as measures of tumor vascularity, based on the expression levels of some vascular endothelium markers by immunohistochemistry [44]. Surov et al. [43] found that SUVmax correlated with vessel area and vessel count in PTs. Unfortunately, there are no reports of similar analyses in malignant cervical LNs, which could have been helpful in explaining our findings.

Previous studies have also investigated the relationship between DCE-MRI and 18F-FMISO PET in HN neck nodal metastases [25], showing that hypoxic nodes are poorly perfused compared to nodes without hypoxia with a negative correlation between FMISO uptake and the median Kep value. At the same time, positive correlations were observed between FMISO uptake and FDG uptake in LNs [25], and between hypoxic volume using 18F-FMISO and hypermetabolic volume using 18F-FDG in HN cancer [27], suggesting that the presence of hypoxia may lead to a greater glucose uptake. The above-mentioned considerations may help explain our findings, even though further studies are needed to better clarify the complex interplay between multi-modal imaging measurements.

To this aim, it would be of interest to evaluate the associations between 18F-FDG PET and ADC measurements for a better tumor characterization, as proposed by some investigators [45,46]. The findings were highly incongruent, showing either no significant correlations or a wide range of correlation coefficients between FDG-PET parameters and ADC [45,46], with a possible dependence on the tumor grade [46]. Interestingly, Teng et al [47] also investigated the spatial relationship between tumor subvolumes of high FDG uptake, low blood volume, and low ADC values in HN cancer, suggesting that multiple imaging techniques, instead of a single imaging modality, should be used to define a potential boosting target and adequately identify tumor subvolumes at higher risk of treatment failure.

There were some limitations in the current study. First, its retrospective nature may have introduced bias and confounding factors. This also prevented us from performing a correlation study at the voxel level, which would have required an accurate image co-registration between PET and MR studies, using a similar patient positioning in both scans. The histogram analysis was proposed only for DCE-MRI maps, and not for 18F-FDG PET images, considering the large difference in spatial resolution between the two imaging modalities. We could not have explored the influence of the tumor grading on the strength of the associations between imaging parameters, as our population had a larger proportion of high-grade tumors.

In conclusion, evident relationships emerged between DCE-MRI and 18F-FDG PET parameters in OPSCC LNs, while no association was found in PTs. Further studies are warranted for a better understanding of the underlying interactions between microvascular properties and tumor metabolism in both tumor sites. These studies would support both radiologists and clinicians in identifying which parameters, alone or in combination, should be proposed in clinical practice for more precise diagnosis and personalized treatment protocols.

Results of Spearman's correlation tests between Ktrans and 18F-FDG-PET parameters in primary tumors (N = 47).

(DOCX)

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Results of Spearman's correlation tests between Kep and 18F-FDG-PET parameters in primary tumors (N = 47).

(DOCX)

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Results of Spearman's correlation tests between ve and 18F-FDG-PET parameters in primary tumors (N = 47).

(DOCX)

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Results of Spearman's correlation tests between Kep and 18F-FDG-PET parameters in lymph nodes (N = 45).

(DOCX)

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Results of Spearman's correlation tests between ve and 18F-FDG-PET parameters in lymph nodes (N = 45).

(DOCX)

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(XLSX)

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11 Dec 2019

PONE-D-19-29908

Correlation between histogram-based DCE-MRI parameters  and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes

PLOS ONE

Dear Dr gangemi,

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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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: Correlation between histogram-based DCE-MRI parameters and 18F-FDG PET

values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and

metastatic nodes

MAJOR Weakness

Unclear clinical relevance of the study in the present form

Abstract

OK

Key words: OK

Introduction

In my point of view, in the introduction, data about associations between imaging findings and histopathology in different malignancies should be given.

It is well known that besides clinical and histopathological factors, imaging parameters, especially those derived from PET, DWI and DCE MRI are important prognostic biomarkers in different malignancies.

The purpose of the study, especially, clinical relevance etc. is unclear defined.

M&M

OK. Please see my suggestions for the section results.

Results

Interesting data.

In my point of view, you should also analyse associations between DCE MRI and PET parameters in HPV+ and HPV- tumors separately.

Discussion

Well.

However, there are reports regarding relationships between PET and microvessel density in different tumors, also in HNSCC. These associations may explain correlations between DCE MRI and PET parameters.

Please discuss

Conclusion:

Please give more detailed possible clinical relevance of your results.

References:

Some references may be added (see my suggestions above).

Figures

Well

Tables

Well

Reviewer #2: This is an interesting article, which studies the intercorrelation between standard FDG PET and DCE MRI imaging in a limited head and neck cancer patient population. While the study is scientifically sound, there are a number of issues that should be addressed prior to publication. Most of these issues are grammatical in nature, but some may influence the overall conclusions of the work. The work has not been published before to my knowledge.

In general the manuscript needs full English-language review. For example, first two sentences in Discussion need significant work (I could not ascertain what the authors were trying to say), as do Lines 353+. Line 384 "while no tendency of correlations..." is not a standard use of English. Full English-language review is recommended prior to publication.

Specific Comments (including science questions):

FDG Tumor Delineation:

Was the largest LN on PET always the same largest LN on MRI? How was it ensured that the same LN was being analyzed on both imaging modalities. If these are not the same LN this could have a profound effect on the overall conclusions of the paper.

Table 2 and 3: This is a very confusing header row. We have PT (Primary Tumor) with median and IQR, then we have LN (Lymph Nodes) with median and IQR. Then we have a column "Both" but only a P value (not a median or IQR). I would assume you would want the DCE/PET parameters in the PT and LNs separately in addition to the combined Tumor (PT+LNs) but this doesn't seem to be the case? The headings here are confusing.

Please comment on ADC metrics and refer to recent paper looking at ADC vs. FDG/PET in H/N cancer: https://www.frontiersin.org/articles/10.3389/fonc.2019.01118/full which showed high correlations between MRI and FDG metrics in a very similar (and large) patient population. Although impact is not directly assessed in this review, this paper should be referenced since it showed that correlation between high glucose metabolism and high restricted water diffusion varied greatly spatially from patient to patient.

Line 72: correct LSs to LNs

Line 115: even "though" not even "if"

Line 217: Were scans attenuation corrected? If so, please include this in addition to all corrections (Time-of-Flight? Decay?)

Line 401: Period is needed.

**********

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Reviewer #1: No

Reviewer #2: Yes: Benjamin Rosen

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29 Jan 2020

In response to the Editor:

1) Both the reviewers and myself had some concerns regarding the language both in terms of grammatical errors and clarity. I suggest that you have the manuscript revised by a native English speaker to improve the overall quality of the work. Please note that PLOS ONE does minimal copy-editing upon acceptance of a manuscript, so it is important that the final version does not have grammar and spelling mistakes.

R: As suggested, a full English-language revision has been performed prior to submit the revised manuscript.

2) Also, I would also like to invite you to the carefully check that your manuscript is in line with the STROBE reporting requirements for retrospective studies. You can find an associated checklist here: https://www.strobe-statement.org/index.php?id=available-checklists

Please ensure that all items are met as several are currently missing (e.g. Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection; Explain how the study size was arrived at).

R: We have checked that the manuscript is in line with the STROBE reporting requirements for retrospective studies. In particular:

• we have reported in the abstract the following sentence: “52 patients with a pathologically confirmed OPSCC were included in the present retrospective cohort study” (line 59-60);

• we have added the setting in the methods of the manuscript: “This cohort study was conducted retrospectively at the IRCCS Regina Elena National Cancer Institute, Rome, Italy” (line 135-136);

• we have added the timing in the methods (All patients' tissue samples and medical records were accessed between November 2018 and April 2019, line 156-157) and results (From January 2016 to October 2018 a total of 52 patients affected by OPSCC were retrospectively enrolled in the present study, line 262-263).

• Time of Follow up is not included in the manuscript, because it is not relevant for the aim of the study.

• Due to the retrospective nature of the study and the lack of a specific clinical endpoint, we decided to consider adequate a sample size of about 50 patients, based on the number of patients coming into our institute in the observational period selected. In fact, considering the rarity of the disease and the advanced methodologies used, no published data could have supported specific hypothesis for a conventional sample size calculation (line 143-147).

3) 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.

R: The deposit of our laboratory protocols in protocols.io is not applicable.

In response to Reviewer #1:

MAJOR Weakness

1) Unclear clinical relevance of the study in the present form

R: We thank the Reviewer for this comment. The clinical relevance of the study has been better addressed in introduction (line 100-109) and Discussion (line 334-338; 438-441).

2) Abstract OK

3) Key words: OK

4) Introduction.

In my point of view, in the introduction, data about associations between imaging findings and histopathology in different malignancies should be given. It is well known that besides clinical and histopathological factors, imaging parameters, especially those derived from PET, DWI and DCE MRI are important prognostic biomarkers in different malignancies. The purpose of the study, especially, clinical relevance etc. is unclear defined.

R: Thank you for this suggestion. We have modified the introduction, giving more emphasis on the associations between imaging findings and histopathology, also in different malignancies (line 102-114). To address this point, we have added some new references, which are indicated below. The purpose of the study related to its potential clinical relevance is now better defined.

New references:

1. Surov A, Meyer HJ, Wienke (2018) A. Can Imaging Parameters Provide Information Regarding Histopathology in Head and Neck Squamous Cell Carcinoma? A Meta-Analysis. Transl Oncol;11(2):498-503. doi: 10.1016/j.tranon.2018.02.004.

2. Zheng H, Cui Y et al. (2019) Prognostic Significance of 18F-FDG PET/CT Metabolic Parameters and Tumor Galectin-1 Expression in Patients With Surgically Resected Lung Adenocarcinoma Clin Lung Cancer 20(6):420-428. doi: 10.1016/j.cllc.2019.04.002.

3. Incoronato M, Grimaldi AM, Cavaliere C, et al.(2018) Relationship between functional imaging and immunohistochemical markers and prediction of breast cancer subtype: a PET/MRI study. Eur J Nucl Med Mol Imaging. 2018 Sep;45(10):1680-1693. doi: 10.1007/s00259-018-4010-7.

4. Bekaert L, Valable S, Lechapt-Zalcman E et al. (2017) [18F]-FMISO PET study of hypoxia in gliomas before surgery: correlation with molecular markers of hypoxia and angiogenesis. Eur J Nucl Med Mol Imaging.;44(8):1383-1392. doi: 10.1007/s00259-017-3677-5.

5. Rasmussen GB, Vogelius IR, Rasmussen JH, et al. (2015) Immunohistochemical biomarkers and FDG uptake on PET/CT in head and neck squamous cell carcinoma. Acta Oncol.;54(9):1408-15. doi: 10.3109/0284186X.2015.1062539.

6. Unetsubo T1, Konouchi H, Yanagi Y, et al. (2009) Dynamic contrast-enhanced magnetic resonance imaging for estimating tumor proliferation and microvessel density of oral squamous cell carcinomas. Oral Oncol. 2009 Jul;45(7):621-6. doi: 10.1016/j.oraloncology.2008.09.003.

5) M&M see my suggestions for the section results.

R: As suggested for the section results, we have performed the correlation tests in HPV+ and HPV- tumors separately, although we have preferred not to include these analyses in the manuscript for the reasons explained below.

6) Results.

You should also analyze associations between DCE MRI and PET parameters in HPV+ and HPV- tumors separately.

R: We had explored and discussed the potential influence of the HPV status on imaging parameters derived from DCE-MRI and FDG-PET, as reported in the original version of the manuscript (Statistics: line 260-261; Results: line 313-315; Discussion: line 353-369). Because DCE-MRI parameters, as well as SUVmax, SUVpeak, SUVmean did not significantly differ by the HPV status (based on the Mann-Whitney test results), we decided not to stratify by HPV, also in order not to reduce the statistical power of the analyses: our population had a larger proportion of HPV positive (N = 33) than HPV negative (N=19), thus the statistical power would be limited by the small sample size, particularly in HPV negative patients.

The lack of significant differences between imaging parameters derived from DCE-MRI and FDG-PET in head and neck cancer by p16 status has also recently been reported by Cao et al. [41]. (Cao Y, Aryal M, Li P et al. (2019) Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer. Front Oncol 9:1118. doi: 10.3389/fonc.2019.01118.), which has been added in the reference list.

However, as suggested, we have performed the correlation tests in HPV+ and HPV- tumors separately, although we have preferred not to include these analyses in the manuscript for the reasons mentioned above. The results are illustrated in the Tables inserted at the end of the attached document called "Response to Reviewers": no correlation reached statistically significance after correction for multiple testing in either group.

7) Discussion.

There are reports regarding relationships between PET and microvessel density in different tumors, also in HNSCC. These associations may explain correlations between DCE MRI and PET parameters.

Please discuss

R: Thank you for this suggestion. We have now included some reports evaluating the relationships between PET and microvessel density in HNSCC in Discussion. Unfortunately, there are no reports addressing these analyses in malignant cervical lymph nodes, which could have been helpful in explaining our findings.

A paragraph has been added, as follows (line 400-406):

Recently, possible associations between 18F-FDG-PET and microvessel density (MVD) have been evaluated in HNSCC [43]. MVD assessments have been proposed as measures of tumor vascularity, based on the expression levels of some vascular endothelium markers by immunohistochemistry [44]. Surov et al. found that SUVmax correlated with vessel area and vessel count in PTs. Unfortunately, there are no reports of similar analyses in malignant cervical LNs, which could have been helpful in explaining our findings.

References added:

1. Szafarowski T, Sierdzinski J, Szczepanski MJ, Whiteside TL, Ludwig N, Krzeski A (2018) Microvessel density in head and neck squamous cell carcinoma. Eur Arch Otorhinolaryngol 275:1845-1851. Doi: 10.1007/s00405-018-4996-2.

2. Surov A, Meyer HJ, Höhn AK, Wienke A, Sabri O, Purz S (2019) 18F-FDG-PET Can Predict Microvessel Density in Head and Neck Squamous Cell Carcinoma. Cancers 11(4). pii: E543. DOI: 10.3390/cancers11040543.

Due to the increased length of the manuscript and the increased number of references, as a consequence of the revision process, we did not mention papers focused on the relationships between PET and microvessel in other malignancies.

8) Conclusion:

Please give more detailed possible clinical relevance of your results.

R: We thank the reviewer for this comment, we have now better emphasized the possible clinical relevance of our results throughout the Discussion and in Conclusion (line 334-338; 438-441)

9) References:

Some references may be added (see my suggestions above).

R: As suggested above, we have now added new references regarding both the associations between imaging findings and histopathology and the relationships between PET and microvessel density.

In response to Reviewer #2:

1) In general the manuscript needs full English-language review. For example, first two sentences in Discussion need significant work (I could not ascertain what the authors were trying to say), as do Lines 353+. Line 384 "while no tendency of correlations..." is not a standard use of English. Specific Comments (including science questions):

R: As suggested, a full English-language revision has been performed prior to submit the revised manuscript.

2) FDG Tumor Delineation:

Was the largest LN on PET always the same largest LN on MRI? How was it ensured that the same LN was being analyzed on both imaging modalities. If these are not the same LN this could have a profound effect on the overall conclusions of the paper.

R: Yes, the largest LN on PET was always the same largest LN on MRI. The contouring on MRI and PET-CT was performed in consensus by the radiologist and the nuclear doctor. This is now specified in the text (line 242-244 “To ensure consistency in the identification of the chosen LN, the delineation was done in consensus with the radiologist”).

3) Table 2 and 3: This is a very confusing header row. We have PT (Primary Tumor) with median and IQR, then we have LN (Lymph Nodes) with median and IQR. Then we have a column "Both" but only a P value (not a median or IQR). I would assume you would want the DCE/PET parameters in the PT and LNs separately in addition to the combined Tumor (PT+LNs) but this doesn't seem to be the case? The headings here are confusing.

R: In the header of Table 2 and 3, the term ‘Both’ and the number in brackets specifies the number of patients having both a PT and a LN analyzed, in fact some patients do not have the PT analyzed, while some others do not have a LN analyzed as described in Results (line 269-274). However, because it may appear confusing, the term ‘Both’ has been removed from the header of Table 2 and 3.

4) Please comment on ADC metrics and refer to recent paper looking at ADC vs. FDG/PET in H/N cancer: https://www.frontiersin.org/articles/10.3389/fonc.2019.01118/full which showed high correlations between MRI and FDG metrics in a very similar (and large) patient population. Although impact is not directly assessed in this review, this paper should be referenced since it showed that correlation between high glucose metabolism and high restricted water diffusion varied greatly spatially from patient to patient.

R: As suggested, we have read the study of Cao Y et al (https:// www. frontiersin.org/articles/10.3389/fonc.2019.01118/full) and have mentioned it in Discussion (line 360-362) when addressing the potential influence of the HPV status on imaging parameters. In fact, in this paper the authors did not report correlation analyses between parameters derived from MRI and FDG metrics or between their subvolumes but they compared the predictive power of MRI and PET biomarkers in terms of local, regional of distant failure after chemoradiation in advanced head and neck cancer patients, including the HPV status.

With regard to the correlation between high glucose metabolism and high restricted water diffusion, we have found more relevant the paper of Teng et al.(Teng F, Aryal M, Lee J et al. (2018) Adaptive Boost Target Definition in High-Risk Head and Neck Cancer Based on Multi-imaging Risk Biomarkers. Int J Radiat Oncol Biol Phys.102:969-977. doi: 10.1016/j.ijrobp.2017.12.269). In this study, the spatial relationship between FDG uptake, perfusion and ADC in HN cancer patients were investigated, to evaluate potential implication of their spatial overlap for adaptive boosting of radiotherapy. We found this paper very interesting, suggesting that multiple imaging techniques, instead of a single imaging modality, should be used to define a potential boosting target and adequately identify tumor subvolume at higher risk of treatment failure. We mentioned it in Discussion (line 416-425) as follows:

“…To this aim, it would be of interest to evaluate the associations between 18F-FDG PET and ADC measurements for a better tumor characterization, as proposed by some investigators [45,46]. The findings were highly incongruent, showing either no significant correlations or a wide range of correlation coefficients between FDG-PET parameters and ADC [45,46], with a possible dependence on the tumor grade [46]. Interestingly, Teng et al [47] also investigated the spatial relationship between tumor subvolumes of high FDG uptake, low blood volume, and low ADC values in HN cancer, suggesting that multiple imaging techniques, instead of a single imaging modality, should be used to define a potential boosting target and adequately identify tumor subvolumes at higher risk of treatment failure.”

Reference added:

• Cao Y, Aryal M, Li P et al. (2019) Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer. Front Oncol. 9:1118. DOI: 10.3389/fonc.2019.01118.

• Teng F, Aryal M, Lee J et al. (2018) Adaptive Boost Target Definition in High-Risk Head and Neck Cancer Based on Multi-imaging Risk Biomarkers. Int J Radiat Oncol Biol Phys.102:969-977. doi: 10.1016/j.ijrobp.2017.12.269.

• Meyer HJ, Purz S, Sabri O, Surov A. (2018) Relationships between histogram analysis of ADC values and complex 18F-FDG-PET parameters in head and neck squamous cell carcinoma. PLoS One ;13:e0202897. doi: 10.1371/journal.pone.0202897.

• Shen G, Ma H, Liu B, Ren P, Kuang A. (2017) Correlation of the apparent diffusion coefficient and the standardized uptake value in neoplastic lesions: a meta-analysis. Nucl Med Commun. 2017;38:1076-1084. DOI: 10.1097/MNM.0000000000000746.

5) Line 72: correct LSs to LNs

R: DONE

6) Line 115: even "though" not even "if"

R: DONE

7) Line 217: Were scans attenuation corrected? If so, please include this in addition to all corrections (Time-of-Flight? Decay?)

R: Our PET/CT scanner has not Time-of-Flight capability. PET images were corrected for attenuation using data from CT. This is now specified in the text (Materials and Methods, 18F-FDG-PET /CT image acquisition paragraph, line 201; 209-210).

8) Line 401: Period is needed.

R: DONE

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

11 Feb 2020

Correlation between histogram-based DCE-MRI parameters  and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes

PONE-D-19-29908R1

Dear Dr. gangemi,

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Niels Bergsland

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. 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

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. 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: No

**********

5. 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

**********

6. 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 addressed my suggestions adequately. The manuscript in the present form provides interesting data About associations between DCE MRI and PET Parameters in HNSCC (HPV+ and HPV- Tumors).

Reviewer #2: Comments have been adequately addressed. Manuscript is clearer now and relevant references have been incorporated. Thank you for the opportunity to review this work.

**********

7. 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

14 Feb 2020

PONE-D-19-29908R1

Correlation between histogram-based DCE-MRI parameters and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes

Dear Dr. gangemi:

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