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Predicting Disease Progression in Inoperable Localized NSCLC Patients Using ctDNA Machine Learning Model.

Cancer medicine - 1 Oct 2024

Wu Yuqi, Li Canjun, Yang Yin, Zhang Tao, Wang Jianyang, Tang Wanxiangfu, Li Ningyou, Bao Hua, Wang Xin, Bi Nan

Abstract excerpt

INTRODUCTION: There is an urgent clinical need to accurately predict the risk for disease progression in post-treatment NSCLC patients, yet current ctDNA mutation profiling approaches are limited by low sensitivity. We represent a non-invasive liquid biopsy assay utilizing cfDNA neomer profiling for predicting disease progression in 44 inoperable localized NSCLC patients. METHODS: A total of 97 plasma samples...

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Identifiers

PMID: 39445439
DOI: 10.1002/cam4.70316
PMCID: PMC11499892

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