biostudies-literatureUnknown12Lin Q<h4>Objectives</h4>In radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT).<h4>Materials and methods</h4>We reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normalizaFrontiers in oncology937277https://www.ebi.ac.uk/biostudies/studies/S-EPMC9577189biostudies-literatureCT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy.PMC9577189Song QSLin QTang YKWu HJfalseCT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy.<h4>Objectives</h4>In radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT).<h4>Materials and methods</h4>We reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normaliza2022-01-01T00:00:00Z20222025-04-19T21:21:42.041Z2025-04-19T21:21:42.041ZS-EPMC95771893626797510.3389/fonc.2022.937277