Lung cancer risk prediction models based on pulmonary nodules: A systematic review.

BACKGROUND: Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.
METHODS: The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.
RESULTS: A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.
CONCLUSION: The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.

INTRODUCTION

Lung cancer causes a significant burden on health care systems. In 2020, lung cancer resulted in the death of 1.8 million people worldwide. In China, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer death.

The overall 5‐year survival rate of lung cancer ranges from 10% to 20% in most countries. However, the prognosis of lung cancer largely depends on the stage of the disease at diagnosis. Although the 5‐year survival rate of lung cancer at stage I is above 80%, it is close to 0% for stage IV disease. Therefore, early diagnosis and treatment are important to reduce mortality from lung cancer, improve the quality of life and reduce the economic burden from this disease.

Screening with low‐dose computed tomography (LDCT) has been shown to be an efficient way to detect lung cancer at an earlier stage and reduce lung cancer mortality. Several lung cancer screening trials have been conducted worldwide. , , , , , The national lung cancer screening trial (NLST) of the United States has shown that early LDCT screening can detect potentially cancerous lung nodules at an early stage leading to a reduction in lung cancer mortality by 20%. Nevertheless, the false‐positive nodule detection rate by LDCT was extremely high at 96.4%, eventually leading to unnecessary radiation exposure from further follow‐up imaging tests, invasive biopsies, medical expenses, and anxiety among patients. Therefore, it is of paramount importance to identify the individuals at higher risk of developing lung cancer based on the pulmonary nodules identified on LDCT scans to recommend appropriate examination and management.

Further examinations in current lung cancer screening programs are recommended solely based on the nodule sizes on the LDCT scans. However, although this method of categorizing pulmonary nodules is easy to implement clinically, it may lead to a high rate of false‐positive results. On the contrary, risk prediction models based on pulmonary nodule size, calcification, density, and other relevant imaging information may facilitate the identification of high‐risk groups, significantly reduce the false positive rate, and improve the screening program's efficiency. Therefore, this method is now recommended by several clinical guidelines to reduce the high false‐positive rate of LDCT screening. ,

As a result, several statistical models have been developed in recent years to predict the risk of developing lung cancer based on the identification of pulmonary nodules on LDCT. However, without a systematic evaluation of the relevant models, it remains unclear which, if any of these models should be used clinically. Therefore, in this study, we reviewed the contemporary published literature to identify current multivariable statistical models used to predict the risk of developing lung cancer from the pulmonary nodules identified on LDCT. In addition, the effectiveness, reliability, bias, and extrapolation of the different models used in these studies were also compared.

METHODS

Search strategy

A literature search was conducted using the PubMed, Cochrane, Embase, and Web of Science electronic databases. The keywords “lung cancer” or “lung neoplasms” or “lung tumor” or “lung carcinoma” and “predict” or “assessment” or “risk” and “nodule” were used to identify all relevant articles published in English from January 1960 to February 2021. We also hand‐searched the reference lists of eligible studies to identify additional relevant publications. Further detail about the search strategy used in this study is available in Table S1.

Review methods and selection criteria

Two reviewers independently screened all titles and abstracts and made decisions regarding the potential eligibility of the research articles for full text review. Discrepancies in judgment were resolved by a third reviewer. Studies were eligible if they reported on the development of multivariable risk prediction models for the development of lung cancer based on the pulmonary nodules identified on LDCT and included a detailed description of the procedures used to evaluate and validate the model. Studies with an incomplete description of the procedures used to develop, validate, and evaluate the model were excluded. Informal publications such as conference abstracts were also excluded.

Data extraction

The models used in the studies were divided into two categories; traditional and deep learning models. In the traditional models, raw data (i.e., original image features) were translated into a finite number of feature descriptors (i.e., size, type, or density of nodules) that could be used as predictors for lung cancer. The association between lung cancer risk and each descriptor was tested, quantified, and subsequently developed into an appropriate statistical risk model. In the deep learning algorithm‐based models, the use of raw data was allowed and representations needed for detection or classification were automatically discovered, and the association between lung cancer risk and descriptors is partly unexplainable. ,

For each of the included studies, basic information about the research methodology, variables used to develop the models, and the methods used to evaluate the models were extracted. The basic information included the first author, publication year, study design, study method, target population, inclusion criteria of participants and nodules, and the number of normal and lung cancer cases used for modeling. The model variables extracted from the studies included: basic information about the clinical and epidemiological characteristics, such as age, sex, smoking, family history, occupational exposure, or history of chronic respiratory diseases; and imaging nodule characteristics, like size, density or shape; other tumor biomarkers like neuron‐specific enolase (NSE), or carcinoembryonic antigen (CEA). For the studies based on the deep learning algorithm, it was not possible to extract these variables because of the method used to develop the risk model. The model evaluation criteria included the type of validation (external or internal), the sample size used for verification, the area under the curve (AUC), model calibration slope results, sensitivity, specificity, and the risk threshold. The findings of either the Hosmer‐Lemeshow test or the expected to observe ratio (excellent, poor, or uncalibrated) were also recorded. Furthermore, we used the same dataset to compare the performance (AUC, sensitivity, or specificity) of all deep learning models with existing prediction methods or clinically based guidelines published by professional bodies such as the American College of Radiology Lung Imaging Reporting and Data System (ACR Lung‐RADS) based on the conclusion in the original text.

Quality assessment

The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) method was used to evaluate the quality of evidence in traditional models. This method assesses the quality of the publication based on the risk of bias, consistency, accuracy, directness, and publication bias.

Data synthesis

The sample size used in each study was recorded when available and estimated for evaluation purposes when not available. If several models were used to train the algorithm on the same data set, the model with the highest AUC was selected.

Limited statistical power may lead to insufficient power to detect a significant association, resulting in unstable models. To overcome this problem, we calculated the events per variable (EPV) for traditional models. EPV was defined as the number of events divided by the number of predictor variables included in the multivariable model. An EPV value <10 suggests limited statistical power. Because it was not possible to record and name the variables used in the deep learning models, the EPV could not be calculated.

RESULTS

Study characteristics and quality assessment

The literature search revealed a total of 3230 publications, of which 630 were found to be duplicated and were, therefore, removed from the evaluation. A total of 2293 articles that did not meet our criteria were excluded from the screening. After evaluating the full texts of the remaining 307 articles, 41 articles met the eligibility criteria and were included for further analysis (Figure 1).

FIGURE 1

Flow chart of literature search

After evaluating the articles, 43 models were identified. Overall the models were based on more than 20 000 Asian, North American, and European participants (Figure 2(a)). After 2018, the number of relevant studies grew rapidly. As a result, over half (67.4%, 29/43) of all models were released after 2018 (Figure 3).

FIGURE 2

Characters of existing models; (a) size and distribution of training sets used for modeling; (b) number and distribution of existing models; (c) number and distribution of models seeking validation in different ways; (d) number and distribution of models from different regions and data sources; and (e) frequency of risk factors used in traditional final models

FIGURE 3

AUCs and confidence intervals of existing models by regions and time periods

Most models (58.1%, 25/43) were developed based on deep learning algorithms, and the remaining (41.9%, 18/43) were developed using traditional models (Figure 2(b)) such as logistic regression. However, in recent years, the use of deep learning algorithms increased significantly (Table 2).

TABLE 2

Basic information and development of models based on the deep learning algorithm

First author	Year	Study design	Targeted population	Inclusion criteria of participants	Inclusion criteria of nodules	Sample size	Cases of lung cancer	Data source
Yoganand Balagurunathan 14	2019	Screening trial	American	55–74 years old and smoker	≥4 mm	244	78	Multicenter
Gerard A. Silvestri 15	2018	Cohort study	American and Canadian	>40 years old	8–30 mm	178	29	Multicenter
Chao Zhang 16	2019	Cohort study	American and Chinese			Unspecified	Unspecified	Multicenter
Johanna Uthoff 17	2019	Cohort study	American			363	74	Multicenter
Ilaria Bonavita 18	2020	Cohort study	American			Unspecified	Unspecified	Multicenter
Parnian Afshar 19	2020	Cohort study	American			1010	Unspecified	Multicenter
Huafeng Wang 20	2018	Cohort study	American			1018	Unspecified	Multicenter
Jason L. Causey 21	2018	Cohort study	American			1018	Unspecified	Multicenter
Samuel Hawkins 1 22	2016	Screening trial	American	55–74 years old and smoker	≥4 mm	600	200	Multicenter
Samuel Hawkins 2 22	2016	Screening trial	American	55–74 years old and smoker	≥4 mm	600	200	Multicenter
Andrew V. Kossenkov 23	2019	Cohort study	American	smoker	6–20 mm	583	293	Multicenter
G. A. Soardi 24	2015	Cohort study	American		≤30 mm	311	199	Single‐center
Zuohong Wu 25	2021	Cohort study	Chinese		≤30 mm	995	772	Single‐center
Stéphane Chauvie 26	2020	Screening trial	Chinese	45–75 years old and smoker		234	32	Multicenter
Shulong Li 27	2019	Cohort study	American			1010	Unspecified	Multicenter
Rekka Mastouri 28	2021	Cohort study	American			Unspecified	Unspecified	Multicenter
Yin‐Chen Hsu 29	2020	Cohort study	Chinese			836	27	Single‐center
Jiabao Liu 30	2020	Cohort study	Chinese		6–30 mm	879	601	Multicenter
Rahul Paul 31	2020	Cohort study	American	55–74 years old and smoker	≥4 mm	261	85	Multicenter
Muahammad Bilal Zia 32	2020	Cohort study	American			1010	Unspecified	Multicenter
Yi‐Ming Xu 33	2020	Cohort study	American	55–74 years old and smoker	≥4 mm	1109	926	Multicenter
Subba R. Digumarthy 34	2019	Cohort study	American			36	Unspecified	Single‐center
Yangwei Xiang 35	2019	Cohort study	Chinese			588	462	Single‐center
Liting Mao 36	2019	Cohort study	Chinese			294	61	Single‐center
Shaun Daly 37	2013	Cohort study	American			136	69	Single‐center

Only 23% (10/43) of the models were externally validated (Figure 2(c)). Data from multiple sources were used to develop the models in half of the studies (Figure 2(d)). Thirty‐three studies used data from cohort studies to develop the models, whereas in eight studies, the models were constructed using the data from screening trials (Tables 3 and 4). Almost all studies (97.6%, 40/41) had medium to very low credibility, largely because of publication bias, indirectly, and imprecision (Table S2).

TABLE 3

Validation of traditional models

First author	Year	Type of validation	Calibration	Sample size	AUC a	Thresholds	Sensitivity	Specificity
Annette McWilliams 38	2013	External	Excellent	1090	0.970	0.05	0.71	0.96
Barbara Nemesure 39	2019	Internal	Not calibrated	1455	0.860		0.73	0.81
Michael W. Marcus 40	2019	Internal	Excellent	1013	0.882
Martin T. ammemagi 41	2018	External	Excellent	3680	0.947
Vineet K. Raghu 42	2019	External	Not calibrated	126	0.882	0.61	0.28	1.00
Joan E Walter 43	2018	Internal	Excellent	809	0.850
Xianfeng Li 44	2017	Internal	Not calibrated	39	0.921
Michal Reid 45	2019	External	Excellent	45	0.810
Michael K. Gould 46	2007	Internal	Excellent	375	0.790
Sungmin Zo 47	2020	Internal	Excellent	157	0.952
Xiao‐Bo Chen 48	2019	External	Excellent	216	0.848
Stephen J. Swensen 49	1997	Internal	Excellent	210	0.833	0.10	0.93	0.47
						0.40	0.51	0.90
Man Zhang 50	2015	Internal	Not calibrated	120	0.910	0.55	0.87	0.85
Bin Zheng 1 51	2015	Internal	Not calibrated	198	0.808
Bin Zheng 2 51	2015	Internal	Not calibrated	84	0.845
Jingsi Dong 52	2014	Internal	Not calibrated	1679	0.935
Yun Li 53	2012	External	Not calibrated	145	0.874	0.46	0.95	0.70
Li Yang 54	2017	Internal	Not calibrated	344	0.784		0.70	0.79

AUC, area under curve.

TABLE 4

Validation of models based on the deep learning algorithm

First author	Year	Sample size	Type of validation	AUC a	Threshold	Sensitivity	Specificity
Yogan and Balagurunathan 14	2019	235	Internal	0.850		0.54	0.91
Gerard A. Silvestri 15	2018	178	Internal	0.760	0.05	0.97	0.44
Chao Zhang 16	2019	Unspecified	External	0.855		0.84	0.83
Johanna Uthoff 17	2019	100	External	0.965	0.38	1.00	0.96
Ilaria Bonavita 18	2020	Unspecified	Internal	Unspecified
Parnian Afshar 19	2020	1010	Internal	0.964		0.95	0.90
Huafeng Wang 20	2018	1018	Internal	0.970
Jason L. Causey 21	2018	1018	Internal	0.993
Samuel Hawkins 1 39	2016	600	Internal	0.83
Samuel Hawkins 2 39	2016	600	Internal	0.79
Andrew V. Kossenkov 23	2019	158	External	0.825		0.69	0.84
G. A. Soardi 24	2015	311	Internal	0.893
Zuohong Wu 25	2021	995	Internal	0.851		0.88	0.64
Stéphane Chauvie 26	2020	234	Internal	Unspecified		0.90	1.00
Shulong Li 27	2019	1010	Internal	0.931		0.83	0.92
Rekka Mastouri 28	2021	Unspecified	Internal	0.92		0.92	0.92
Yin‐Chen Hsu 29	2020	836	Internal	0.873		0.75	0.85
Jiabao Liu 30	2020	879	Internal	0.938	0.58	0.84	0.91
Rahul Paul 31	2020	261	Internal	0.960
Muahammad Bilal Zia 32	2020	1010	Internal	Unspecified		0.91	0.91
Yi‐Ming Xu 33	2020	1109	Internal	Unspecified		0.93	0.89
Subba R. Digumarthy 34	2019	36	Internal	0.708
Yangwei Xiang 35	2019	588	Internal	0.890		0.90	0.80
Liting Mao 36	2019	294	Internal	0.970		0.81	0.92
Shaun Daly 37	2013	81	External	0.676		0.95	0.25

AUC, area under curve.

Development and performance of traditional models

The model from the Mayo clinic in the United States published in 1997 was the first model used to predict the risk of developing cancer from pulmonary nodules. Since then, 18 traditional models have been developed to predict the pathological characteristics of pulmonary nodules. Seven of these models were based on the North American population; two models were based on the European population, and nine models were based on the Asian population. Of the nine Asian models evaluated in this review, eight models were based on the Chinese population (Table 1).

TABLE 1

Basic information and development of traditional models

First author	Year	Study design	Study method	Target population	Inclusion criteria of participants	Inclusion criteria of nodules	Sample size	Cases of lung cancer	EPVb	Data source
Annette McWilliams 38	2013	Screen trial	Logistic regression	Canadian	50–74 years old	≥1 mm	1871	102	11.33	Multicenter
Barbara Nemesure 39	2019	Cohort study	Cox regression	American			1469	85 a	6.54	Single‐center
Michael W. Marcus 40	2019	Screen trial	Logistic regression	English	50–75 years old	≥3 mm	1013	52	2.60	Multicenter
Martin Tammemagi 41	2018	Screen trial	Logistic regression	Canadian	50–74 years old	≥1 mm	1871	111	10.10	Multicenter
Vineet K. Raghu 42	2019	Cohort study	Logistic regression	American	Smoker		92	50	10.00	Multicenter
Joan E. Walter 43	2018	Screen trial	Logistic regression	Dutch/Belgian	50–75 years old and smoker		809	50 a	7.14	Multicenter
Xianfeng Li 44	2017	Cohort study	Fisher discriminant analysis	Chinese	20–80 years old	5–30 mm	39	20	1.00	Single‐center
Michal Reid 45	2019	Cohort study	Logistic regression	American	≥18 years old	≤30 mm	301	200	10.00	Single‐center
Michael K. Gould 46	2007	Cohort study	Logistic regression	American		7–30 mm	375	204	13.60	Multicenter
Sungmin Zo 47	2020	Cohort study	Logistic regression	Korean			157	90	5.29	Single‐center
Xiao‐Bo Chen 48	2019	Cohort study	Logistic regression	Chinese		8–20 mm	493	214	11.26	Single‐center
Stephen J. Swensen 49	1997	Cohort study	Logistic regression	American		4‐30 mm	419	145 a	8.06	Single‐center
Man Zhang 50	2015	Cohort study	Logistic regression	Chinese		≤30 mm	314	248	14.59	Multicenter
Bin Zheng 1 51	2015	Cohort study	Logistic regression	Chinese		≤30  mm and GCO b <50%	405	367	11.84	Single‐center
Bin Zheng 2 51	2015	Cohort study	Logistic regression	Chinese		≤30 mm and GCO ≥50%	159	166	5.35	Single‐center
Jingsi Dong 52	2014	Cohort study	Logistic regression	Chinese			1679	1296	58.91	Single‐center
Yun Li 53	2012	Cohort study	Logistic regression	Chinese			371	229	15.27	Unspecified
Li Yang 54	2017	Cohort study	Logistic regression	Chinese			1078	721	65.55	Single‐center

Approximate number.

EPV, events per variable; GCO, ground glass opacity.

Traditional models included numerous imaging features such as nodule size, type, location, shape, and margin to determine the pathological characteristics of the pulmonary nodules. In addition, basic information such as age, gender, family history of cancer, and smoking status was also commonly used. However, biomarkers were used in only seven models (Figure 2(e)).

Logistic regression analysis was used to develop most (16/18) traditional models. The models in the other two studies were developed using either Cox regression analysis or Fisher linear discriminant analysis. Most models (14/18) were cohort studies, and the remaining four were constructed using screening test results (Table 1). Based on the regression analysis, the size, margin of the nodules, smoking status, and age of patients were statistically significant in more than half of all models. The addition of biomarkers to tumor markers improved the AUC and statistical significance in three of the seven evaluated models, as shown in Table 5. These findings suggest that although biomarkers were not widely used to develop traditional models, they may have an important role in improving the accuracy of these models.

TABLE 5

Variables of traditional models

Variables a		First authors of models
		Annette McWilliams 38	Barbara Nemesure 39	Michael W. Marcus 40	Martin Tammemagi 41	Vineet K. Raghu 42	Joan E. Walter 43	Xianfeng Li 44	Michal Reid 45	Michael K. Gould 46	Sungmin Zo 47	Xiao‐Bo Chen 48	Stephen J. Swensen 49	Man Zhang 50	Bin Zheng 1 51	Bin Zheng 2 51	Jingsi Dong 52	Yun Li 53	Li Yang 54
Basic character	Age	0	1	1	0	1		1	1	1	0	1	1	1	0	0	1	1	1
	Sex	1	0	1	1			0		0	0	0	0	0	1	1	0	0	1
	Personal history of other cancer		1	1							0	0	1	0	0	0		0	1
	Family history of lung cancer	0	0	1	1			0		0		0		0	0	0	1	1	0
	Family history of other cancer		0	0				0	1	0				0	0	0	1	1	0
	BMI b			0								0			0	0
	Exposure of asbestos		0	1									0
	FVC b			1
	History of respiratory diseases		1	1								0	0	0	0	0
	Smoke		1	1	0	0		1	0	0	0	0	1	1	1	0	1	0	1
	Clinical symptoms														0	0			0
	Time since previous lung cancer was diagnosed									0
	FEV1 b			0											1	1
Biomarkers	Squamous cell carcinoma antigen											0
	NSE b							0	0
	CEA b							1	0			0		0	0	0	1
	CYFRA21‐1 b							1	0					1			1
	MiRNA‐21‐5p b							1	0
	MiR‐574‐5p							1	0
	Laboratory indicators														0	0
	Ferritin											0
Imaging information	Size	1	1		0	1		1		1	0	0	1	1	1	1	1	1	1
	Volume			1	1		1
	Density	1	1	1				0	1		0			0	0	0
	Location	0	0	1	1		1	0	1	0	1	0	1		0	0	1	0	0
	Count	0	0	0	1	0
	Margin (spiculate)	1	1	0	1		0	1	1		1	1	1	1	0	0	1	1
	Satellite lesions						1				1				0	0	1
	Calcification												0	0	1	1	0	1	1
	Cavitation							0							0	0
	Shape			0	0			0		0	0			0	0	0	1	0	1
	Enhancement											1			0	0
	Pleural indentation											1		0			0	0
	Bronchus sign										1		0
	Vascular signs													0				0
	Enphysema	0		0	1			1	0		0
	Vessels sign							0
	Vessel number					1
	Tracheal signs
	Previous CT scan			0
	Previous X‐ray			0
	Vacuole signs
	Associated pleural effusion														0	0
	Enlarged hilar or mediastinal lymph nodes														0	0
	Visibility in retrospect						0
	Carbohydrate antigen											0
	Neuron‐specific enolase											0

0 depicts the inclusion of a variable into the model as a candidate variable; 1 depicts retention in the final model.

bBMI, body mass index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; NSE, neuron‐specific enolase; CEA, carcinoembryonic antigen; CEFRA21‐1, cytokeratin fragment antigen 21‐1; MiR(NA), MicroRNA.

The AUCs of the models ranged from 0.676 to 0.970. Most models (77.8%, 14/18) performed well on discrimination, with an AUC higher or equal to 0.8. Calibration was assessed in nine models, and the results indicated a good fit. Most studies (61.1%, 11/18) had an EPV higher than 10, suggesting sufficient statistical power. Only six of the 18 models were validated using external datasets. However, five of these models were validated using external data from a similar population from the same countries, and only one model was verified using data of participants from different origins. The latter model achieved good discrimination with an AUC of 0.970 (Tables 1 and 3).

Compared with the European and American models, the Chinese models lack external validation. Most of the data used to develop the Chinese models were obtained from a single‐center or small sample retrospective cohort studies and only two of these studies were validated using an external dataset. However, the discrimination ability of the Chinese models was good, with seven of eight models achieving an AUC higher than 0.8, whereas two models reported excellent calibration. In addition, all Chinese models had an EPV higher than 10. More details can be found in Tables 1, 3, and Figures 2 and 3.

Development and performance of the deep learning algorithms

The first study reporting on the development and performance of a deep learning algorithm for the discrimination of pulmonary nodules was published in 2013. Only biomarkers were included in the development of this model, and the prediction ability was limited, with an AUC of 0.676. The majority of the deep learning models (84%, 21/25) were developed after 2018 and were based on the imaging features of the nodules. This improved the models' prediction ability, especially when the model was supplemented by epidemiological parameters and biomarkers (Figure 3).

The AUC of the deep learning models was reported in 21 of 25. However, only half of these models (12 of 21) reported the confidence intervals (Table 4). The reported AUCs ranged from 0.676 to 0.970. Most of the deep learning models (68.0%, 17/25) had a good discrimination ability with an AUC higher than 0.8, whereas the other four models (16.0%) had an AUC below 0.8. The majority of the models (84.0%, 21/25 were not validated externally [Table 2]).

Only seven of 18 deep learning models were developed in Asia. Furthermore, all Asian models achieved high discrimination with an AUC above 0.8. However, the sample size of the Asian models was generally small, and only one of these models was validated using an external dataset (Tables 2 and 4).

Comparison of deep learning models with traditional models

The discrimination ability of 60.0% (15/25) of the deep learning models was compared with traditional methods. All deep learning models achieved higher or similar discrimination abilities when compared with traditional methods (Table 6).

TABLE 6

Comparison between existing methods and models based on the deep learning algorithm

First author	Objects for comparison	Indicators for comparison	Superior methods
Yogan and Balagurunathan 14	None
Gerard A. Silvestri 15	Traditional models	AUCa	Deep learning
Gerard A. Silvestri 15	Clinician	AUC	Deep learning
Chao Zhang 16	Clinician	Accuracy, sensitivity, and specificity	Deep learning
Johanna Uthoff 17	None
Ilaria Bonavita 18	Clinician	F1 score	Deep learning
Parnian Afshar 19	None
Huafeng Wang 20	None
Jason L. Causey 21	Clinician	AUC	Similar
Samuel Hawkins 1,2 39	Lung‐RADS	AUC	Deep learning
Samuel Hawkins 1,2 39	Traditional models	AUC	Similar
Andrew V. Kossenkov 23	Traditional models	AUC	Deep learning
G. A. Soardi 24	None
Zuohong Wu 25	Traditional models	AUC	Deep learning
Stéphane Chauvie 26	Lung‐RADS	PPVa, sensitivity, and specificity	Deep learning
Stéphane Chauvie 26	Traditional models	PPV, sensitivity, and specificity	Deep learning
Shulong Li 27	None
Rekka Mastouri 28	None
Yin‐Chen Hsu 29	Lung‐RADS	AUC	Deep learning
Jiabao Liu 30	Clinician	AUC	Deep learning
Rahul Paul 31	None
Muahammad Bilal Zia 32	None
Yi‐Ming Xu 33	Clinician	Sensitivity	Deep learning
Subba R. Digumarthy 34	None
Yangwei Xiang 35	Traditional models	AUC	Deep learning
Liting Mao 36	ACR‐lung RADSa	Accuracy, sensitivity, and specificity	Deep learning
Shaun Daly 37	Traditional models	AUC	Deep learning

AUC, area under curve; ACR‐Lung‐RADS, American College of Radiology Lung Imaging Reporting and Data System; PPV, positive predictive value.

DISCUSSION

LDCT can be used to diagnose lung cancer at an early stage via the identification and classification of pulmonary nodules into different risk categories. However, current pulmonary nodules classification guidelines are based solely on nodule size and density. Other important biomarkers and patient characteristics are mostly ignored, resulting in a very high false‐positive rate, over diagnosis, and unnecessary treatment. , , Various traditional and deep learning models based on clinical, biological, and epidemiological factors have been developed to overcome this problem. To our knowledge, in this manuscript, we present the first systemic review comparing the development, validation, and performance of these models in the characterization of pulmonary nodules identified on LDCT.

In this systemic review, we evaluated the performance of 43 models derived from 41 research articles based on over 20 000 subjects. Our findings indicate that the majority of the traditional and deep learning models achieved an AUC higher than 0.8, suggesting that these models can be used to identify the high‐risk population effectively and hence, reduce the false‐positive rate and the harms of over diagnosis and treatment.

Since 1997, the development of pulmonary nodule risk prediction models has increased rapidly. Most early models were developed using statistical methods such as regression analysis. Although imaging features such as nodule size, type, location, shape, and margin provide valuable information on the pathological characteristics of the nodules, our findings indicate that the incorporation of clinical characteristics such as age and smoking status can significantly improve the performance of these models. The first study confirming this finding was performed at the Mayo Clinic. Since then, various traditional statistic‐based models incorporating both imaging and patient characteristics have been developed. Subsequent models also incorporated clinical indicators such as forced vital capacity (FVC) and forced expiratory volume (FEV)1, and serum biomarkers such as CEA and NSE, to further improve the prediction efficacy on the models. , , , , Variables including age, size of the nodules, and margin of the nodules should be considered as a priory in machine‐learning analyses, as they were consistently considered as predictors of lung cancer in traditional studies.

A limited number of studies incorporated other risk factors such as exposure of asbestos, satellite lesions, bronchus sign, and volume of nodules (Table 5). However, the main limitation of these risk factors is the limited sample size that limits the generalizability of the model. A large number of models were based on single‐center and retrospective studies with small sample sizes or data obtained from old studies. Biomarkers were not commonly used in the development of the predictive risk factor model (Table 5, Figure 2(e)). Nodule volume might have been an effective predictor, , but was generally not taken into consideration by current models. Because most studies were retrospective, it was not possible to incorporate time‐dependent variables such as variations in biomarkers and nodule size over time into the model. Therefore, time‐dependent factors, such as the nodule volume growth rate, were also ignored by most studies.

Deep learning models can learn from various heterogeneous variables to generate homogeneous groups with similar features. These features can be mapped with similar survival models to obtain accurate predictions. Various studies , , , also suggest that compared with the traditional pulmonary nodule prediction models or expert judgment by clinicians, the use of deep learning algorithms has obvious advantages on discrimination (Table 6). However, although pulmonary nodule risk models based on deep learning algorithms have been used as early as 1993, they have not been widely used to predict pulmonary nodules until recent years as they still have several limitations. One of the main limitations of deep learning algorithms is that they require large amounts of data, advanced imaging equipment, top‐ranked statisticians, and research funds to develop. Despite the high discrimination ability of the deep learning algorithm models evaluated in our systemic review, the GRADE scores of these models were generally low because of their limited sample size, high level of bias, inaccuracy, and indirectness (Table S2). Furthermore, it is difficult to identify the specific variables used to develop the deep learning prediction model, potentially limiting the quality and authenticity of these models.

Few studies were based on the Asian population. The majority of the Asian studies were based on a single center, had a limited sample size, and lacked external validation, which limited the quality of evidence (Tables 3 and 4, Figure 2). It is important to note that the accepted European and United States models may not be suitable for the Asian and Chinese populations because of large population differences, as suggested by Uthoff et al. and Nair et al.

Our systemic review has several limitations that have to be acknowledged. First of all, variations between studies, including sample size, research design, data source, and imaging acquisition criteria, made it difficult to quantify, integrate, and extrapolate the results of the different studies. Some of the studies included in our analysis had high publication bias, particularly those that lacked external validity. Additionally, cultural and social risk factors were ignored by most models. Studies evaluating a single risk factor were also excluded from this analysis although these variables were highly predictive of lung cancer and represent the latest trend in the field.

Furthermore, most of the existing models were based on the entire population. Therefore, subgroup analysis based on important risk factors such as smoking status and tumor histology is recommended to improve the prediction performance of current models and adapt these tools according to the specific characteristics of the population being studied. However, this type of research requires large datasets, highlighting the need for further large‐scale multicenter prospective studies. Future studies should also focus on developing deep learning based models based on decentralized and deparametric data. These methods process the raw data directly and therefore, reduce the heterogeneity while improving the models' performance compared with traditional models.

CONCLUSION

The incidence of lung cancer is increasing, particularly in developing countries. The models evaluated in our study were all developed in Europe, Asia, and the United States. These models showed good discrimination for identifying high‐risk pulmonary nodules, particularly when these models combined imaging features with clinical, behavioral characteristics, and other biomarkers. This highlights the need to develop models based on the unique characteristics of different populations, particularly those in developing countries, to reduce the global lung cancer burden. The use of deep learning algorithms increased significantly during the last few years and generally performed better than traditional models. However, more research is required to improve the quality of the deep learning models, particularly for the Asian population, because these models were often based on single‐center studies and lacked external validation. Further research should also focus on improving the quality of current screening guidelines by incorporating clinical and epidemiological factors into the evaluation of pulmonary nodules.

CONFLICT OF INTEREST

The author declares that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Appendix S1 Supporting Information

Click here for additional data file.