Machine studying identifies prognostic subtypes of the tumor microenvironment of NSCLC


  • Howlader, N. et al. SEER Most cancers Statistics Assessment, 1975–2017 Vol. 4 (Nationwide Most cancers Institute, 2020).


    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barta, J. A., Powell, C. A. & Wisnivesky, J. P. International epidemiology of lung most cancers. Ann. Glob. Well being 85, 2419 (2019).


    Google Scholar
     

  • Howlader, N. et al. The impact of advances in lung-cancer therapy on inhabitants mortality. N. Engl. J. Med. 383, 640–649 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Siegel, R. L., Miller, Okay. D. & Jemal, A. Most cancers statistics, 2019. CA Most cancers J. Clin. 69, 7–34 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Varela, G. & Thomas, P. A. Surgical administration of superior non-small cell lung most cancers. J. Thorac. Dis. 6, S217 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Miller, Okay. D. et al. Most cancers therapy and survivorship statistics, 2019. CA Most cancers J. Clin. 69, 363–385 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Goldstraw, P. et al. Non-small-cell lung most cancers. The Lancet 378, 1727–1740 (2011).

    Article 

    Google Scholar
     

  • 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).

    ADS 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • Garon, E. B. et al. Pembrolizumab for the therapy of non–small-cell lung most cancers. N. Engl. J. Med. 372, 2018–2028 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guo, W., Ji, Y. & Catenacci, D. V. A subgroup cluster-based Bayesian adaptive design for precision medication. Biometrics 73, 367–377 (2017).

    Article 
    MathSciNet 
    PubMed 

    Google Scholar
     

  • Fisher, R., Pusztai, L. & Swanton, C. Most cancers heterogeneity: Implications for focused therapeutics. Br. J. Most cancers 108, 479–485 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to most cancers therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Andersen, P. Okay. & Gill, R. D. Cox’s regression mannequin for counting processes: A big pattern examine. Ann. Stat. 10, 1100–1120 (1982).

    Article 
    MathSciNet 

    Google Scholar
     

  • Kalbfleisch, J. D. & Prentice, R. L. The Statistical Evaluation of Failure Time Knowledge (Wiley, 2011).


    Google Scholar
     

  • Binder, H., Allignol, A., Schumacher, M. & Beyersmann, J. Boosting for high-dimensional time-to-event knowledge with competing dangers. Bioinformatics 25, 890–896 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841–860 (2008).

    Article 
    MathSciNet 

    Google Scholar
     

  • Jaeger, B. C. et al. Indirect random survival forests. Ann. Appl. Stat. 13, 1847–1883 (2019).

    Article 
    MathSciNet 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Lang, M. et al. mlr3: A contemporary object-oriented machine studying framework in R. J. Open Supply Softw. 4, 1903 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Stekhoven, D. J. & Stekhoven, M. D. J. Package deal ‘missForest’. R package deal model 1 (2013).

  • Hot Topics

    Related Articles