{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12"],"submitter":["Lin Q"],"pubmed_abstract":["<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 normaliza"],"journal":["Frontiers in oncology"],"pagination":["937277"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9577189"],"repository":["biostudies-literature"],"pubmed_title":["CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy."],"pmcid":["PMC9577189"],"pubmed_authors":["Song QS","Lin Q","Tang YK","Wu HJ"],"additional_accession":[]},"is_claimable":false,"name":"CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy.","description":"<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 normaliza","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-19T21:21:42.041Z","creation":"2025-04-19T21:21:42.041Z"},"accession":"S-EPMC9577189","cross_references":{"pubmed":["36267975"],"doi":["10.3389/fonc.2022.937277"]}}