Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.

Article  PubMed  Google Scholar 

Duma N, Santana-Davila R, Molina JR. Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment. Mayo Clin Proc. 2019;94(8):1623-40.

Pao W, Girard N. New driver mutations in non-small-cell lung cancer. The Lancet Oncology. 2011;12(2):175–80.

Article  PubMed  CAS  Google Scholar 

Aerts HJ. The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. JAMA Oncol. 2016;2(12):1636–42.

Article  PubMed  Google Scholar 

Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

Article  PubMed  CAS  Google Scholar 

Li H, Zhang R, Wang S, Fang M, Zhu Y, Hu Z, et al. CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study. Front Oncol. 2020;10:57.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Sugai Y, Kadoya N, Tanaka S, Tanabe S, Umeda M, Yamamoto T, et al. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol. 2021;16(1):80.

Article  PubMed  PubMed Central  Google Scholar 

Sun F, Chen Y, Chen X, Sun X, Xing L. CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer. Eur J Radiol. 2021;134:109411.

Article  PubMed  Google Scholar 

van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Oberije C, Monshouwer R, et al. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol. 2017;123(3):363–9.

Article  PubMed  Google Scholar 

Yang L, Yang J, Zhou X, Huang L, Zhao W, Wang T, et al. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. Eur Radiol. 2019;29(5):2196–206.

Article  PubMed  Google Scholar 

van Timmeren JE, van Elmpt W, Leijenaar RTH, Reymen B, Monshouwer R, Bussink J, et al. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiother Oncol. 2019;136:78–85.

Article  PubMed  PubMed Central  Google Scholar 

Astaraki M, Wang C, Buizza G, Toma-Dasu I, Lazzeroni M, Smedby O. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Phys Med. 2019;60:58–65.

Article  PubMed  Google Scholar 

Park S, Lee SM, Kim S, Choi S, Kim W, Do KH, et al. Performance of radiomics models for survival prediction in non-small-cell lung cancer: influence of CT slice thickness. Eur Radiol. 2021;31(5):2856–65.

Article  PubMed  Google Scholar 

Avesani G, Tran HE, Cammarata G, Botta F, Raimondi S, Russo L, et al. CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset. Cancers (Basel). 2022;14(11).

Hao D, Li Q, Feng QX, Qi L, Liu XS, Arefan D, et al. Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction. Front Oncol. 2021;11:725889.

Article  PubMed  Google Scholar 

Liu G, Poon M, Zapala MA, Temple WC, Vo KT, Matthay KK, et al. Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma. J Digit Imaging. 2022;35(3):605–12.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Wang J, Zeng J, Li H, Yu X. A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer. J Healthc Eng. 2022;2022:4034404.

PubMed  PubMed Central  Google Scholar 

Yang Y, Zhou Y, Zhou C, Ma X. Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma. Eur J Surg Oncol. 2022;48(5):1068–77.

Article  PubMed  Google Scholar 

Zhang Y, Lobo-Mueller EM, Karanicolas P, Gallinger S, Haider MA, Khalvati F. Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. Front Artif Intell. 2020;3:550890.

Article  PubMed  PubMed Central  Google Scholar 

Zhang Y, Lobo-Mueller EM, Karanicolas P, Gallinger S, Haider MA, Khalvati F. CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. BMC Med Imaging. 2020;20(1):11.

Article  PubMed  PubMed Central  Google Scholar 

Zhang Y, Lobo-Mueller EM, Karanicolas P, Gallinger S, Haider MA, Khalvati F. Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images. Sci Rep. 2021;11(1):1378.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO, et al. Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma. Tomography. 2016;2(4):388–95.

Article  PubMed  PubMed Central  Google Scholar 

Wang S, Liu Z, Chen X, Zhu Y, Zhou H, Tang Z, et al., editors. Unsupervised deep learning features for lung cancer overall survival analysis. 2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2018: IEEE.

Braghetto A, Marturano F, Paiusco M, Baiesi M, Bettinelli A. Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset. Sci Rep. 2022;12(1):14132.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Afshar P, Mohammadi A, Tyrrell PN, Cheung P, Sigiuk A, Plataniotis KN, et al. [Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer. Sci Rep. 2020;10(1):12366.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Baek S, He Y, Allen BG, Buatti JM, Smith BJ, Tong L, et al. Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep. 2019;9(1):17286.

Article  PubMed  PubMed Central  Google Scholar 

He BX, Zhong YF, Zhu YB, Deng JJ, Fang MJ, She YL, et al. Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk. Transl Lung Cancer Res. 2022;11(4):670–85.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11):e1002711.

Article  PubMed  PubMed Central  Google Scholar 

Kim H, Goo JM, Lee KH, Kim YT, Park CM. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas. Radiology. 2020;296(1):216–24.

Article  PubMed  Google Scholar 

Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18(1):24.

Article  PubMed  PubMed Central  Google Scholar 

Hou KY, Chen JR, Wang YC, Chiu MH, Lin SP, Mo YH, et al. Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography. Cancers (Basel). 2022;14(15).

Yin M, Lin J, Liu L, Gao J, Xu W, Yu C, et al. Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study. Diagnostics (Basel). 2022;12(5).

She Y, Jin Z, Wu J, Deng J, Zhang L, Su H, et al. Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival. JAMA Netw Open. 2020;3(6):e205842.

Article  PubMed  PubMed Central  Google Scholar 

Moradmand H, Aghamiri SMR, Ghaderi R, Emami H. The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma. Cancer Med. 2021;10(20):7048–59.

Article  PubMed  PubMed Central  Google Scholar 

Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, et al. A radiogenomic dataset of non-small cell lung cancer. Sci Data. 2018;5:180202.

Article