Howlader, N. et al. SEER Most cancers Statistics Assessment, 1975–2017 Vol. 4 (Nationwide Most cancers Institute, 2020).
Liao, G. et al. Prognostic position of soluble programmed demise ligand 1 in non-small cell lung most cancers: A scientific overview and meta-analysis. Entrance. Oncol. 11, 774131 (2021).
Tubin, S., Khan, M. Okay., Gupta, S. & Jeremic, B. Biology of NSCLC: Interaction between most cancers cells, radiation and tumor immune microenvironment. Cancers 13, 775 (2021).
Barta, J. A., Powell, C. A. & Wisnivesky, J. P. International epidemiology of lung most cancers. Ann. Glob. Well being 85, 2419 (2019).
Howlader, N. et al. The impact of advances in lung-cancer therapy on inhabitants mortality. N. Engl. J. Med. 383, 640–649 (2020).
Siegel, R. L., Miller, Okay. D. & Jemal, A. Most cancers statistics, 2019. CA Most cancers J. Clin. 69, 7–34 (2019).
Varela, G. & Thomas, P. A. Surgical administration of superior non-small cell lung most cancers. J. Thorac. Dis. 6, S217 (2014).
Miller, Okay. D. et al. Most cancers therapy and survivorship statistics, 2019. CA Most cancers J. Clin. 69, 363–385 (2019).
Goldstraw, P. et al. Non-small-cell lung most cancers. The Lancet 378, 1727–1740 (2011).
Tang, C. et al. Improvement of an immune-pathology knowledgeable radiomics mannequin for non-small cell lung most cancers. Sci. Rep. 8, 1–9 (2018).
Azuma, Okay. et al. Affiliation of PD-L1 overexpression with activating EGFR mutations in surgically resected nonsmall-cell lung most cancers. Ann. Oncol. 25, 1935–1940 (2014).
Meyers, D., Bryan, P., Banerji, S. & Morris, D. Focusing on the PD-1/PD-L1 axis for the therapy of non-small-cell lung most cancers. Curr. Oncol. 25, 324–334 (2018).
Garon, E. B. et al. Pembrolizumab for the therapy of non–small-cell lung most cancers. N. Engl. J. Med. 372, 2018–2028 (2015).
Brahmer, J. R. et al. Security and exercise of anti–PD-L1 antibody in sufferers with superior most cancers. N. Engl. J. Med. 366, 2455–2465 (2012).
Glatzel-Plucinska, N. et al. SATB1 degree correlates with Ki-67 expression and is a constructive prognostic think about non-small cell lung carcinoma. Anticancer Res. 38, 723–736 (2018).
Pawelczyk, Okay. et al. Position of PD-L1 expression in non-small cell lung most cancers and their prognostic significance based on clinicopathological components and diagnostic markers. Int. J. Mol. Sci. 20, 824 (2019).
Shimoji, M. et al. Scientific and pathologic options of lung most cancers expressing programmed cell demise ligand 1 (PD-L1). Lung Most cancers 98, 69–75 (2016).
Solar, J.-M. et al. Prognostic significance of PD-L1 in sufferers with non–small cell lung most cancers: A big cohort examine of surgically resected circumstances. J. Thorac. Oncol. 11, 1003–1011 (2016).
Zhou, C. et al. PD-L1 expression as poor prognostic think about sufferers with non-squamous non-small cell lung most cancers. Oncotarget 8, 58457 (2017).
Cooper, W. A. et al. PD-L1 expression is a positive prognostic think about early stage non-small cell carcinoma. Lung Most cancers 89, 181–188 (2015).
Teng, M. W., Ngiow, S. F., Ribas, A. & Smyth, M. J. Classifying cancers based mostly on T-cell infiltration and PD-L1. Most cancers Res. 75, 2139–2145 (2015).
Guo, W., Ji, Y. & Catenacci, D. V. A subgroup cluster-based Bayesian adaptive design for precision medication. Biometrics 73, 367–377 (2017).
Fisher, R., Pusztai, L. & Swanton, C. Most cancers heterogeneity: Implications for focused therapeutics. Br. J. Most cancers 108, 479–485 (2013).
Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to most cancers therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).
Yu, D. et al. Machine studying prediction of the opposed final result for nontraumatic subarachnoid hemorrhage sufferers. Ann. Clin. Transl. Neurol. 7, 2178–2185 (2020).
Luo, W. et al. Pointers for creating and reporting machine studying predictive fashions in biomedical analysis: A multidisciplinary view. J. Med. Web Res. 18, e323 (2016).
Solar, W., Jiang, M., Dang, J., Chang, P. & Yin, F.-F. Impact of machine studying strategies on predicting NSCLC total survival time based mostly on Radiomics evaluation. Radiat. Oncol. 13, 1–8 (2018).
Ou, F.-S., Michiels, S., Shyr, Y., Adjei, A. A. & Oberg, A. L. Biomarker discovery and validation: Statistical issues. J. Thorac. Oncol. 16, 537–545 (2021).
Heiden, B. T. et al. Evaluation of delayed surgical therapy and oncologic outcomes in scientific stage I non–small cell lung most cancers. JAMA Netw. Open 4, e2111613–e2111613 (2021).
Andersen, P. Okay. & Gill, R. D. Cox’s regression mannequin for counting processes: A big pattern examine. Ann. Stat. 10, 1100–1120 (1982).
Kalbfleisch, J. D. & Prentice, R. L. The Statistical Evaluation of Failure Time Knowledge (Wiley, 2011).
Binder, H., Allignol, A., Schumacher, M. & Beyersmann, J. Boosting for high-dimensional time-to-event knowledge with competing dangers. Bioinformatics 25, 890–896 (2009).
Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841–860 (2008).
Jaeger, B. C. et al. Indirect random survival forests. Ann. Appl. Stat. 13, 1847–1883 (2019).
Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, Okay. L. & Rosati, R. A. Evaluating the yield of medical assessments. Jama 247, 2543–2546 (1982).
Lang, M. et al. mlr3: A contemporary object-oriented machine studying framework in R. J. Open Supply Softw. 4, 1903 (2019).
Stekhoven, D. J. & Stekhoven, M. D. J. Package deal ‘missForest’. R package deal model 1 (2013).