Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249. https://doi.org/10.3322/caac.21660 (2021).
Herbst, R. S., Heymach, J. V. & Lippman, S. M. Lung cancer. N. Engl. J. Med. 359, 1367–1380. https://doi.org/10.1056/NEJMra0802714 (2008).
Herbst, R. S., Morgensztern, D. & Boshoff, C. The biology and management of non-small cell lung cancer. Nature 553, 446–454. https://doi.org/10.1038/nature25183 (2018).
Gelatti, A. C. Z., Drilon, A. & Santini, F. C. Optimizing the sequencing of tyrosine kinase inhibitors (TKIs) in epidermal growth factor receptor (EGFR) mutation-positive non-small cell lung cancer (NSCLC). Lung Cancer 137, 113–122. https://doi.org/10.1016/j.lungcan.2019.09.017 (2019).
Han, B. et al. EGFR mutation prevalence in Asia-Pacific and Russian patients with advanced NSCLC of adenocarcinoma and non-adenocarcinoma histology: The IGNITE study. Lung Cancer 113, 37–44. https://doi.org/10.1016/j.lungcan.2017.08.021 (2017).
Mok, T. S. et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 361, 947–957. https://doi.org/10.1056/NEJMoa0810699 (2009).
Wu, Y. L. et al. Dacomitinib versus gefitinib as first-line treatment for patients with EGFR-mutation-positive non-small-cell lung cancer (ARCHER 1050): A randomised, open-label, phase 3 trial. Lancet. Oncol. 18, 1454–1466. https://doi.org/10.1016/s1470-2045(17)30608-3 (2017).
Wu, Y. L. et al. Afatinib versus cisplatin plus gemcitabine for first-line treatment of Asian patients with advanced non-small-cell lung cancer harbouring EGFR mutations (LUX-Lung 6): An open-label, randomised phase 3 trial. Lancet Oncol. 15, 213–222. https://doi.org/10.1016/S1470-2045(13)70604-1 (2014).
Wu, Y. L. et al. First-line erlotinib versus gemcitabine/cisplatin in patients with advanced EGFR mutation-positive non-small-cell lung cancer: Analyses from the phase III, randomized, open-label, ENSURE study. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 26, 1883–1889. https://doi.org/10.1093/annonc/mdv270 (2015).
Koo, D. H. et al. EGFR-TKI is effective regardless of treatment timing in pulmonary adenocarcinoma with EGFR mutation. Cancer Chemother. Pharmacol. 75, 197–206. https://doi.org/10.1007/s00280-014-2631-5 (2015).
Lim, S. M., Syn, N. L., Cho, B. C. & Soo, R. A. Acquired resistance to EGFR targeted therapy in non-small cell lung cancer: Mechanisms and therapeutic strategies. Cancer Treat. Rev. 65, 1–10. https://doi.org/10.1016/j.ctrv.2018.02.006 (2018).
Yun, C. H. et al. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc. Natl. Acad. Sci. U. S. A. 105, 2070–2075. https://doi.org/10.1073/pnas.0709662105 (2008).
Mok, T. S. et al. Osimertinib or platinum-pemetrexed in EGFR T790M-positive lung cancer. N. Engl. J. Med. 376, 629–640. https://doi.org/10.1056/NEJMoa1612674 (2017).
Soria, J. C. et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N. Engl. J. Med. 378, 113–125. https://doi.org/10.1056/NEJMoa1713137 (2018).
Takeda, M. & Nakagawa, K. First- and second-generation EGFR-TKIs are all replaced to osimertinib in chemo-naive EGFR mutation-positive non-small cell lung cancer?. Int. J. Mol. Sci. https://doi.org/10.3390/ijms20010146 (2019).
Shah, R. & Lester, J. F. Tyrosine kinase inhibitors for the treatment of EGFR mutation-positive non-small-cell lung cancer: A clash of the generations. Clin. Lung Cancer 21, e216–e228. https://doi.org/10.1016/j.cllc.2019.12.003 (2020).
Hochmair, M. J. et al. Sequential afatinib and osimertinib in patients with EGFR mutation-positive non-small-cell lung cancer: Final analysis of the GioTag study. Future Oncol. 16, 2799–2808. https://doi.org/10.2217/fon-2020-0740 (2020).
Ramalingam, S. S. et al. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N. Engl. J. Med. 382, 41–50. https://doi.org/10.1056/NEJMoa1913662 (2020).
Mayerhoefer, M. E. et al. Introduction to radiomics. J. Nucl. Med. 61, 488–495. https://doi.org/10.2967/jnumed.118.222893 (2020).
Thawani, R. et al. Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer 115, 34–41. https://doi.org/10.1016/j.lungcan.2017.10.015 (2018).
Binczyk, F., Prazuch, W., Bozek, P. & Polanska, J. Radiomics and artificial intelligence in lung cancer screening. Transl. Lung Cancer Res. 10, 1186–1199. https://doi.org/10.21037/tlcr-20-708 (2021).
Avanzo, M., Stancanello, J., Pirrone, G. & Sartor, G. Radiomics and deep learning in lung cancer. Strahlenther. Onkol. 196, 879–887. https://doi.org/10.1007/s00066-020-01625-9 (2020).
Kawahara, D. et al. Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis. Sci. Rep. 11, 16232. https://doi.org/10.1038/s41598-021-95643-x (2021).
Rossi, G. et al. Radiomic detection of EGFR mutations in NSCLC. Cancer Res. 81, 724–731. https://doi.org/10.1158/0008-5472.CAN-20-0999 (2021).
Wen, Q., Yang, Z., Dai, H., Feng, A. & Li, Q. Radiomics study for predicting the expression of PD-L1 and Tumor mutation burden in non-small cell lung cancer based on CT images and clinicopathological features. Front. Oncol. 11, 620246. https://doi.org/10.3389/fonc.2021.620246 (2021).
Chetan, M. R. & Gleeson, F. V. Radiomics in predicting treatment response in non-small-cell lung cancer: Current status, challenges and future perspectives. Eur. Radiol. 31, 1049–1058. https://doi.org/10.1007/s00330-020-07141-9 (2021).
Lu, J. et al. Machine learning-based radiomics for prediction of epidermal growth factor receptor mutations in lung adenocarcinoma. Dis. Markers 2022, 2056837. https://doi.org/10.1155/2022/2056837 (2022).
Yang, X. et al. Can CT radiomics detect acquired T790M mutation and predict prognosis in advanced lung adenocarcinoma with progression after first- or second-generation EGFR TKIs?. Front. Oncol. 12, 904983. https://doi.org/10.3389/fonc.2022.904983 (2022).
Detterbeck, F. C., Boffa, D. J., Kim, A. W. & Tanoue, L. T. The eighth edition lung cancer stage classification. Chest 151, 193–203. https://doi.org/10.1016/j.chest.2016.10.010 (2017).
Zhou, J. et al. Re-biopsy and liquid biopsy for patients with non-small cell lung cancer after EGFR-tyrosine kinase inhibitor failure. Thorac. Cancer 10, 957–965. https://doi.org/10.1111/1759-7714.13035 (2019).
Zhang, Y. et al. Next-generation sequencing of tissue and circulating tumor DNA: Resistance mechanisms to EGFR targeted therapy in a cohort of patients with advanced non-small cell lung cancer. Cancer Med. 10, 4697–4709. https://doi.org/10.1002/cam4.3948 (2021).
She, Y. et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur. Radiol. 28, 5121–5128. https://doi.org/10.1007/s00330-018-5509-9 (2018).
Hong, D., Xu, K., Zhang, L., Wan, X. & Guo, Y. Radiomics signature as a predictive factor for EGFR mutations in advanced lung adenocarcinoma. Front. Oncol. 10, 28. https://doi.org/10.3389/fonc.2020.00028 (2020).
Yushkevich, P. A. & Gerig, G. ITK-SNAP: An intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse 8, 54–57. https://doi.org/10.1109/MPUL.2017.2701493 (2017).
Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31, 1116–1128. https://doi.org/10.1016/j.neuroimage.2006.01.015 (2006).
Leijenaar, R. T. et al. Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability. Acta Oncol. 52, 1391–1397. https://doi.org/10.3109/0284186X.2013.812798 (2013).
Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 (2016).
Zwanenburg, A. et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328–338. https://doi.org/10.1148/radiol.2020191145 (2020).
Ren, M. et al. MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients. Med. Phys. 48, 5142–5151. https://doi.org/10.1002/mp.15137 (2021).
Morgado, J. et al. Machine learning and feature selection methods for EGFR mutation status prediction in lung cancer. Appl. Sci. Basel 11, 3273 (2021).
Wilcoxin, F. Probability tables for individual comparisons by ranking methods. Biometrics 3, 119–122 (1947).
Tibshirani, R. The lasso method for variable selection in the Cox model. Stat. Med. 16, 385–395. https://doi.org/10.1002/(sici)1097-0258(19970228)16:4%3c385::aid-sim380%3e3.0.co;2-3 (1997).
Li, Z. & Sillanpaa, M. J. Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theor. Appl. Genet. 125, 419–435. https://doi.org/10.1007/s00122-012-1892-9 (2012).
Zhang, Z. Variable selection with stepwise and best subset approaches. Ann. Transl. Med. 4, 136. https://doi.org/10.21037/atm.2016.03.35 (2016).
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14. https://doi.org/10.3389/fninf.2014.00014 (2014).
Chapman, A. M., Sun, K. Y., Ruestow, P., Cowan, D. M. & Madl, A. K. Lung cancer mutation profile of EGFR, ALK, and KRAS: Meta-analysis and comparison of never and ever smokers. Lung Cancer 102, 122–134. https://doi.org/10.1016/j.lungcan.2016.10.010 (2016).
Wang, S. et al. Value of serum tumor markers for predicting EGFR mutations and positive ALK expression in 1089 Chinese non-small-cell lung cancer patients: A retrospective analysis. Eur. J. Cancer 124, 1–14. https://doi.org/10.1016/j.ejca.2019.10.005 (2020).
Balachandran, V. P., Gonen, M., Smith, J. J. & DeMatteo, R. P. Nomograms in oncology: More than meets the eye. Lancet Oncol. 16, e173-180. https://doi.org/10.1016/S1470-2045(14)71116-7 (2015).
Liu, Z. et al. The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges. Theranostics 9, 1303–1322. https://doi.org/10.7150/thno.30309 (2019).
Kumar, V. et al. Radiomics: The process and the challenges. Magn. Reson. Imaging 30, 1234–1248. https://doi.org/10.1016/j.mri.2012.06.010 (2012).
Shi, L. et al. Radiomics for response and outcome assessment for non-small cell lung cancer. Technol. Cancer Res. Treat. 17, 1533033818782788. https://doi.org/10.1177/1533033818782788 (2018).
Zhou, M. et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 286, 307–315. https://doi.org/10.1148/radiol.2017161845 (2018).
Kim, H. et al. Repeat biopsy of patients with acquired resistance to EGFR TKIs: Implications of biopsy-related factors on T790M mutation detection. Eur. Radiol. 28, 861–868. https://doi.org/10.1007/s00330-017-5006-6 (2018).
Hou, D. et al. Different clinicopathologic and computed tomography imaging characteristics of primary and acquired EGFR T790M mutations in patients with non-small-cell lung cancer. Cancer Manag. Res. 13, 6389–6401. https://doi.org/10.2147/CMAR.S323972 (2021).
Koo, H. J. et al. Non-small cell lung cancer with resistance to EGFR-TKI therapy: CT characteristics of T790M mutation-positive cancer. Radiology 289, 227–237. https://doi.org/10.1148/radiol.2018180070 (2018).
Yoshida, T. et al. Standardized uptake value on (18)F-FDG-PET/CT is a predictor of EGFR T790M mutation status in patients with acquired resistance to EGFR-TKIs. Lung Cancer 100, 14–19. https://doi.org/10.1016/j.lungcan.2016.07.022 (2016).
Tang, X. et al. Machine learning-based CT radiomics analysis for prognostic prediction in metastatic non-small cell lung cancer patients with EGFR-T790M mutation receiving third-generation EGFR-TKI osimertinib treatment. Front. Oncol. 11, 719919. https://doi.org/10.3389/fonc.2021.719919 (2021).
Kawamura, T. et al. Clinical factors predicting detection of T790M mutation in rebiopsy for EGFR-mutant non-small-cell lung cancer. Clin. Lung Cancer 19, e247–e252. https://doi.org/10.1016/j.cllc.2017.07.002 (2018).
Oya, Y. et al. Association between EGFR T790M status and progression patterns during initial EGFR-TKI treatment in patients harboring EGFR mutation. Clin. Lung Cancer 18, 698–705. https://doi.org/10.1016/j.cllc.2017.05.004 (2017).
Dal Maso, A. et al. Clinical features and progression pattern of acquired T790M-positive compared With T790M-negative EGFR mutant non-small-cell lung cancer: Catching tumor and clinical heterogeneity over time through liquid biopsy. Clin. Lung Cancer 21, 1–14. https://doi.org/10.1016/j.cllc.2019.07.009 (2020).
Perez-Callejo, D., Romero, A., Provencio, M. & Torrente, M. Liquid biopsy based biomarkers in non-small cell lung cancer for diagnosis and treatment monitoring. Transl. Lung Cancer Res. 5, 455–465. https://doi.org/10.21037/tlcr.2016.10.07 (2016).
Rolfo, C. et al. Liquid biopsies in lung cancer: The new ambrosia of researchers. Biochim. Biophys. Acta 539–546, 2014. https://doi.org/10.1016/j.bbcan.2014.10.001 (1846).
Del Re, M. et al. Understanding the mechanisms of resistance in EGFR-positive NSCLC: From tissue to liquid biopsy to guide treatment strategy. Int. J. Mol. Sci. https://doi.org/10.3390/ijms20163951 (2019).
Cecchini, M. J. & Yi, E. S. Liquid biopsy is a valuable tool in the diagnosis and management of lung cancer. J. Thorac. Dis. 12, 7048–7056. https://doi.org/10.21037/jtd.2020.04.20 (2020).
Minari, R. et al. Detection of EGFR-activating and T790M mutations using liquid biopsy in patients With EGFR-mutated Non-small-cell lung cancer whose disease has progressed during treatment with first- and second-generation tyrosine kinase inhibitors: A multicenter real-life retrospective study. Clin. Lung Cancer 21, e464–e473. https://doi.org/10.1016/j.cllc.2020.02.021 (2020).
Buder, A. et al. Cell-free plasma DNA-guided treatment with osimertinib in patients with advanced EGFR-mutated NSCLC. J. Thorac. Oncol. 13, 821–830. https://doi.org/10.1016/j.jtho.2018.02.014 (2018).
Merker, J. D. et al. Circulating tumor DNA analysis in patients with cancer: American society of clinical oncology and college of American pathologists joint review. J. Clin. Oncol. 36, 1631–1641. https://doi.org/10.1200/JCO.2017.76.8671 (2018).
Sundaresan, T. K. et al. Detection of T790M, the acquired resistance EGFR Mutation, by tumor biopsy versus noninvasive blood-based analyses. Clin. Cancer Res. 22, 1103–1110. https://doi.org/10.1158/1078-0432.CCR-15-1031 (2016).
Oxnard, G. R. et al. Association between plasma genotyping and outcomes of treatment with osimertinib (AZD9291) in advanced non-small-cell lung cancer. J. Clin. Oncol. 34, 3375–3382. https://doi.org/10.1200/JCO.2016.66.7162 (2016).
Cucchiara, F. et al. Integrating liquid biopsy and radiomics to monitor clonal heterogeneity of EGFR-positive non-small cell lung cancer. Front. Oncol. 10, 593831. https://doi.org/10.3389/fonc.2020.593831 (2020).
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. & Fotiadis, D. I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17. https://doi.org/10.1016/j.csbj.2014.11.005 (2015).
Gu, Q. et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur. J. Radiol. 118, 32–37. https://doi.org/10.1016/j.ejrad.2019.06.025 (2019).
Parmar, C., Grossmann, P., Bussink, J., Lambin, P. & Aerts, H. Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 5, 13087. https://doi.org/10.1038/srep13087 (2015).
Saini, R., Fatima, S. & Agarwal, S. M. TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors. Chem. Biol. Drug Des. 96, 921–930. https://doi.org/10.1111/cbdd.13697 (2020).
Savargiv, M., Masoumi, B. & Keyvanpour, M. R. A new random forest algorithm based on learning automata. Comput. Intell. Neurosci. 2021, 5572781. https://doi.org/10.1155/2021/5572781 (2021).
Marchese Robinson, R. L. et al. Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets. J. Chem. Inf. Model. 28, 1773–1792. https://doi.org/10.1021/acs.jcim.6b00753 (2017).
Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577. https://doi.org/10.1148/radiol.2015151169 (2016).
Lohmann, P., Bousabarah, K., Hoevels, M. & Treuer, H. Radiomics in radiation oncology-basics, methods, and limitations. Strahlenther. Onkol. 196, 848–855. https://doi.org/10.1007/s00066-020-01663-3 (2020).
