{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhao J"],"funding":["the grand from Guangzhou Science and Technology Plan Project","the grants from the Sun Yat-sen Clinical Research Cultivating Program of Sun Yat-sen Memorial Hospital, Sun Yat-sen University","the grant from Guangzhou Science and Technology Plan Project","National Natural Science Foundation of China","the grant from GuangDong Basic and Applied Basic Research Foundation"],"pagination":["e006514"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10230987"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(5)"],"pubmed_abstract":["Background
Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).Methods
Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction,"],"journal":["Journal for immunotherapy of cancer"],"pubmed_title":["Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study."],"pmcid":["PMC10230987"],"funding_grant_id":["SYS-5010-202308","2023A03J0719","82071754","82271793","2023A03J0722, 202206010078, 202201020574","2023A1515012412, 2023A1515011214","82102865","SYS-Q-202101","SYS-C-202003","82072906"],"pubmed_authors":["Liu J","Tan Y","Lin Y","Yao H","Wang Y","Zhao J","Sun Z","Yuan Z","Duan X","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study.","description":"Background
Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).Methods
Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction,","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2026-07-14T20:57:17.67Z","creation":"2026-06-24T03:07:02.096Z"},"accession":"S-EPMC10230987","cross_references":{"pubmed":["37217246"],"doi":["10.1136/jitc-2022-006514"]}}