Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

Journal: JCO precision oncology
PMID: 38976829

Abstract

PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC).

Authors

  • Haolun Ding
    Beijing Wodong Tianjun Information Technology Co., Ltd, Chengdu Branch, Chengdu 610041, Sichuan, China.
  • Xu Steven Xu
    Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, USA. sxu@genmab.com.
  • Yaning Yang
    Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Min Yuan
    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

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