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.

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,

SUBMITTER: Zhao J 

PROVIDER: S-EPMC10230987 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature