Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. CA A Cancer J Clin 73(1):17–48. https://doi.org/10.3322/caac.21763

Article  Google Scholar 

Galluzzi L, Aryankalayil MJ, Coleman CN, Formenti SC (2023) Emerging evidence for adapting radiotherapy to immunotherapy. Nat Rev Clin Oncol 20(8):543–557. https://doi.org/10.1038/s41571-023-00782-x

Article  PubMed  Google Scholar 

Pichert MD, Canavan ME, Maduka RC et al (2022) Immunotherapy after chemotherapy and radiation for clinical stage III lung cancer. JAMA Netw Open 5(8):e2224478. https://doi.org/10.1001/jamanetworkopen.2022.24478

Article  PubMed  PubMed Central  Google Scholar 

Park CJ, Choi SH, Kim D et al (2023) MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma. Eur Radiol. https://doi.org/10.1007/s00330-023-10389-6

Article  PubMed  PubMed Central  Google Scholar 

Shi Z, Huang X, Cheng Z et al (2023) MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer. Radiology 308(1):e222830. https://doi.org/10.1148/radiol.222830

Article  PubMed  Google Scholar 

Huang W, Xiong W, Tang L et al (2023) Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study. J Immunother Cancer 11(11):e007807. https://doi.org/10.1136/jitc-2023-007807

Article  PubMed  PubMed Central  Google Scholar 

Wu S, Zhan W, Liu L et al (2023) Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO study): a multicenter retrospective study. J Immunother Cancer 11(10):e007369. https://doi.org/10.1136/jitc-2023-007369

Article  PubMed  PubMed Central  Google Scholar 

Liu T, Dong D, Zhao X et al (2023) Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study. BMC Med 21(1):464. https://doi.org/10.1186/s12916-023-03164-3

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu Z, Wang S, Dong D et al (2019) The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9(5):1303–1322. https://doi.org/10.7150/thno.30309

Article  PubMed  PubMed Central  Google Scholar 

Zhao J, He Y, Yang X et al (2023) Assessing treatment outcomes of chemoimmunotherapy in extensive-stage small cell lung cancer: an integrated clinical and radiomics approach. J Immunother Cancer 11(9):e007492. https://doi.org/10.1136/jitc-2023-007492

Article  PubMed  PubMed Central  Google Scholar 

Dercle L, Fronheiser M, Rizvi NA et al (2023) Baseline radiomic signature to estimate overall survival in patients with NSCLC. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 18(5):587–598. https://doi.org/10.1016/j.jtho.2022.12.019

Article  CAS  Google Scholar 

Hindocha S, Charlton TG, Linton-Reid K et al (2022) Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC. NPJ Precis Oncol 6(1):77. https://doi.org/10.1038/s41698-022-00322-3

Article  PubMed  PubMed Central  Google Scholar 

Mu W, Jiang L, Shi Y et al (2021) Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. J Immunother Cancer 9(6):e002118. https://doi.org/10.1136/jitc-2020-002118

Article  PubMed  PubMed Central  Google Scholar 

Huang B, Sollee J, Luo YH et al (2022) Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT. EBioMedicine 82:104127. https://doi.org/10.1016/j.ebiom.2022.104127

Article  CAS  PubMed  PubMed Central  Google Scholar 

Khorrami M, Khunger M, Zagouras A et al (2019) Combination of peri- and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1(2):e180012. https://doi.org/10.1148/ryai.2019180012

Article  PubMed  Google Scholar 

Mauclet C, Dupont MV, Roelandt K et al (2023) Treatment and prognosis of patients with lung cancer and combined interstitial lung disease. Cancers (Basel) 15(15):3876. https://doi.org/10.3390/cancers15153876

Article  CAS  PubMed  Google Scholar 

Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Advances in neural information processing systems, vol 30. Curran Associates Inc., Scotland

Google Scholar 

Goyal K, Dumancic S, Blockeel H (2020) Feature interactions in XGBoost. arXiv e-prints. Published online July 1. https://doi.org/10.48550/arXiv.2007.05758

Aerts HJWL, Wee L, Rios Velazquez E et al (2014) Data from NSCLC-radiomics (version 4). Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI

Article  Google Scholar 

Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006

Article  CAS  PubMed  Google Scholar 

Clark K, Vendt B, Smith K et al (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Article  PubMed  PubMed Central  Google Scholar 

Bradley JD, Forster K (2018) Data from NSCLC-cetuximab. Cancer Imaging Arch. https://doi.org/10.7937/TCIA.2018.jze75u7v

Article  Google Scholar 

Shi Z, Zhang Z, Liu Z et al (2022) Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 49(8):2462–2481. https://doi.org/10.1007/s00259-021-05658-9

Article  PubMed  Google Scholar 

Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G (2020) Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp 4(1):50. https://doi.org/10.1186/s41747-020-00173-2

Article  PubMed  PubMed Central  Google Scholar 

3D Slicer image computing platform. 3D Slicer. https://slicer.org/

Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

Article  PubMed  PubMed Central  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li C, Liu M, Zhang Y et al (2023) Novel models by machine learning to predict prognosis of breast cancer brain metastases. J Transl Med 21(1):404. https://doi.org/10.1186/s12967-023-04277-2

Article  PubMed  PubMed Central  Google Scholar 

Akiba T, Sano S, Yanase T, Ohta T, Koyama M. (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. KDD ’19. Association for computing machinery; pp 2623–2631. https://doi.org/10.1145/3292500.3330701

Turner MC, Chen Y, Krewski D, Calle EE, Thun MJ (2007) Chronic obstructive pulmonary disease is associated with lung cancer mortality in a prospective study of never smokers. Am J Respir Crit Care Med 176(3):285–290. https://doi.org/10.1164/rccm.200612-1792OC

Article  PubMed  Google Scholar 

Kashihara T, Nakayama Y, Okuma K et al (2023) Impact of interstitial lung abnormality on survival after adjuvant durvalumab with chemoradiotherapy for locally advanced non-small cell lung cancer. Radiother Oncol J Eur Soc Therapeutic Radiol Oncol 180:109454. https://doi.org/10.1016/j.radonc.2022.109454

Article  CAS  Google Scholar 

Yasuura Y, Terada Y, Mizuno K et al (2022) Quantitative severity of emphysema is related to the prognostic outcome of early-stage lung cancer. Eur J Cardio-Thorac Surg Off J Eur Assoc Cardio-Thorac Surg. 62(5):ezac499. https://doi.org/10.1093/ejcts/ezac499

Article  Google Scholar