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

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.

[1]  P. Mazzone,et al.  Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. , 2021, Chest.

[2]  Andreas Keller,et al.  Swarm Learning for decentralized and confidential clinical machine learning , 2021, Nature.

[3]  A. Jemal,et al.  Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.

[4]  Nawres Khlifa,et al.  A bilinear convolutional neural network for lung nodules classification on CT images , 2020, International Journal of Computer Assisted Radiology and Surgery.

[5]  Ho Yun Lee,et al.  Predicting the Risk of Malignancy of Lung Nodules Diagnosed as Indeterminate on Radial Endobronchial Ultrasound-Guided Biopsy , 2020, Journal of clinical medicine.

[6]  Wei-min Li,et al.  A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population , 2020, Journal of Cancer Research and Clinical Oncology.

[7]  Wen He,et al.  Estimation of malignancy of pulmonary nodules at CT scans: Effect of computer‐aided diagnosis on diagnostic performance of radiologists , 2020, Asia-Pacific journal of clinical oncology.

[8]  Robert J. Gillies,et al.  Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future , 2020, Comput. Biol. Medicine.

[9]  C. la Vecchia,et al.  Progress in cancer mortality, incidence, and survival: a global overview. , 2020, European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation.

[10]  K. Plataniotis,et al.  3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction , 2020, Scientific Reports.

[11]  Y. Tsai,et al.  Artificial neural networks improve LDCT lung cancer screening: a comparative validation study , 2020, BMC Cancer.

[12]  D. Gao,et al.  Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network , 2020, Cancer management and research.

[13]  G. Melloni,et al.  Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial , 2020, European Radiology.

[14]  Jiawen Wang,et al.  Classification of Malignant and Benign Lung Nodule and Prediction of Image Label Class using Multi-Deep Model , 2020 .

[15]  Ke Zhao,et al.  Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations , 2019, Cancer management and research.

[16]  Mario Ceresa,et al.  Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline , 2019, Comput. Methods Programs Biomed..

[17]  P. Mazzone,et al.  Development of a Risk Prediction Model to Estimate the Probability of Malignancy in Pulmonary Nodules Being Considered for Biopsy. , 2019, Chest.

[18]  B. Nemesure,et al.  Will That Pulmonary Nodule Become Cancerous? A Risk Prediction Model for Incident Lung Cancer , 2019, Cancer Prevention Research.

[19]  R. Gillies,et al.  Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules , 2019, Scientific Reports.

[20]  M. Kalra,et al.  Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? , 2019, Cancer Imaging.

[21]  Johanna Uthoff,et al.  Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. , 2019, Medical physics.

[22]  S. Duffy,et al.  Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial , 2019, Thorax.

[23]  Yi-long Wu,et al.  Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network. , 2019, The oncologist.

[24]  Samantha K. N. Dilger,et al.  Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant? , 2019, European Radiology.

[25]  J. Leader,et al.  Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models , 2019, Thorax.

[26]  Xin Li,et al.  Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening. , 2019, Quantitative imaging in medicine and surgery.

[27]  B. Han,et al.  Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods. , 2019, Journal of thoracic disease.

[28]  B. van Ginneken,et al.  Predicting Malignancy Risk of Screen‐Detected Lung Nodules–Mean Diameter or Volume , 2019, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[29]  Rehman Qureshi,et al.  A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT. , 2018, Cancer research.

[30]  T. Henry,et al.  ACR Appropriateness Criteria® Lung Cancer Screening. , 2018, Journal of the American College of Radiology : JACR.

[31]  Jing Wang,et al.  Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features , 2018, Physics in medicine and biology.

[32]  Rozemarijn Vliegenthart,et al.  Characteristics of new solid nodules detected in incidence screening rounds of low-dose CT lung cancer screening: the NELSON study , 2018, Thorax.

[33]  D. Ettinger,et al.  Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. , 2018, Journal of the National Comprehensive Cancer Network : JNCCN.

[34]  P. Mazzone,et al.  Screening for Lung Cancer: CHEST Guideline and Expert Panel Report , 2018, Chest.

[35]  Paul Kearney,et al.  Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules , 2018, Chest.

[36]  Shiqian Ma,et al.  Highly accurate model for prediction of lung nodule malignancy with CT scans , 2018, Scientific Reports.

[37]  Witold Rzyman,et al.  European position statement on lung cancer screening. , 2017, The Lancet. Oncology.

[38]  M. Desai,et al.  Accuracy of Models to Identify Lung Nodule Cancer Risk in the National Lung Screening Trial , 2017, American journal of respiratory and critical care medicine.

[39]  Tingting Zhao,et al.  A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation. , 2017, Journal of X-ray science and technology.

[40]  Lihua Cao,et al.  Combining serum miRNAs, CEA, and CYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules: a pilot study , 2017, World Journal of Surgical Oncology.

[41]  Xuequan Huang,et al.  Assessment of the cancer risk factors of solitary pulmonary nodules , 2017, Oncotarget.

[42]  Chuan Zhou,et al.  Deep Learning in Medical Image Analysis. , 2017, Advances in experimental medicine and biology.

[43]  Samuel H. Hawkins,et al.  Predicting Malignant Nodules from Screening CT Scans , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[44]  J. Crowley,et al.  The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[45]  Wenhua Liang,et al.  Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules. , 2015, Journal of thoracic disease.

[46]  Qing Duan,et al.  A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules: An Asia Cohort Study. , 2015, The Annals of thoracic surgery.

[47]  M. Callister,et al.  British Thoracic Society guidelines for the investigation and management of pulmonary nodules. , 2015 .

[48]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[49]  G. Guyatt,et al.  Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients , 2015, BMJ : British Medical Journal.

[50]  Yibo Gao,et al.  Development and validation of clinical diagnostic models for the probability of malignancy in solitary pulmonary nodules , 2014, Thoracic cancer.

[51]  S. Lam,et al.  Probability of cancer in pulmonary nodules detected on first screening CT. , 2013, The New England journal of medicine.

[52]  D. Aberle,et al.  Results of initial low-dose computed tomographic screening for lung cancer. , 2013, The New England journal of medicine.

[53]  M. Yoder,et al.  Development and Validation of a Plasma Biomarker Panel for Discerning Clinical Significance of Indeterminate Pulmonary Nodules , 2013, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[54]  Jun Wang,et al.  A Mathematical Model for Predicting Malignancy of Solitary Pulmonary Nodules , 2012, World Journal of Surgery.

[55]  C. Gatsonis,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[56]  M. Gould,et al.  A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. , 2007, Chest.

[57]  S. Swensen,et al.  Screening for lung cancer with low-dose spiral computed tomography. , 2000, American journal of respiratory and critical care medicine.

[58]  S. Swensen,et al.  The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. , 1997, Archives of internal medicine.

[59]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[60]  J. Gurney.  Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory. , 1993, Radiology.

[61]  J. Gurney,et al.  Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part II. Application. , 1993, Radiology.

[62]  Matthew P. Goetz,et al.  NCCN CLINICAL PRACTICE GUIDELINES IN ONCOLOGY , 2019 .

[63]  M. Motton,et al.  Assessing probability of malignancy in solid solitary pulmonary nodules with a new Bayesian calculator: improving diagnostic accuracy by means of expanded and updated features , 2014, European Radiology.