Research progress on radiomics and deep learning in predicting the efficacy of neoadjuvant immunotherapy for non-small cell lung cancer
HUANG Shiyang
SHI Lei
Abstract：Preoperative prediction of the efficacy of neoadjuvant immunotherapy(NIT)in non-small cell lung cancer(NSCLC)helps identify patients who are likely to benefit,reduce the risk of postoperative recurrence and metastasis,and improve prognosis.Radiomics and deep learning can be used to explore imaging biomarkers for predicting NIT efficacy in NSCLC.Radiomics,through global feature analysis or habitat analysis methods,can effectively quantify the temporal and spatial heterogeneity of tumors,providing a quantitative basis for efficacy prediction.Deep learning,on the other hand,adaptively extracts deep imaging features to evaluate treatment response.This review summarizes recent research progress in radiomics and deep learning technologies for predicting NIT efficacy in NSCLC patients,and discusses the associated technical challenges and corresponding solutions.
Keywords：Non-small cell lung cancerNeoadjuvant therapyImmunotherapyRadiomicsDeep learningBiomarker
Publication Date：2025-05-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 312-318 )