Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non-Small-Cell Lung Cancer.
Journal:
JCO clinical cancer informatics
Published Date:
Sep 1, 2019
Abstract
PURPOSE: The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with advanced non-small-cell lung cancer (NSCLC)-objective response (OR), progression-free survival (PFS), and overall survival (OS)-using routinely collected patient and disease variables.
Authors
Keywords
Algorithms
Carcinoma, Non-Small-Cell Lung
Combined Modality Therapy
Humans
Kaplan-Meier Estimate
Lung Neoplasms
Machine Learning
Models, Biological
Molecular Targeted Therapy
Neoplasm Metastasis
Neoplasm Staging
Prognosis
Randomized Controlled Trials as Topic
Stochastic Processes
Treatment Outcome
Tumor Burden