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This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (138) Big Data (441) Precision Medicine and Genetics (107) Secondary Use of Clinical Data for Research and Surveillance (641) Machine Learning (3217) Cancer Epidemiology, Cancer Surveillance and Infodemiology (174) Innovations in Cancer Diagnostic and Decision Support (155) Lung Cancer (115)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64399, first published .
Doctor interacting with a holographic 3D model of human lungs on a futuristic tablet.

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Authors of this article:

Alan James Michael Brnabic1 Author Orcid Image ;   Ilya Lipkovich2 Author Orcid Image ;   Zbigniew Kadziola2 Author Orcid Image ;   Dan He3 Author Orcid Image ;   Peter M Krein2 Author Orcid Image ;   Lisa M Hess2 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

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  2. Aytac A, Ozata B, Erdogdu I, Alkan A, Tanriverdi O. Prognostic significance of RICTOR mutations in EGFR-mutant metastatic lung adenocarcinoma: a retrospective cohort study. Virchows Archiv 2026;489(1):79 View
  3. Marmarelis M, Gilligan A, Sugihara T, Marquart T, Whipple S, Wang Y, Allen-Coyle T, Bhandari N, Aggarwal C. The Waiting Game: How NGS and Guideline-Concordant-Care Timing Shape Survival in Advanced NSCLC. Cancers 2026;18(14):2287 View