Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models -- dual-center study.

ABSTRACT:

Purpose

The aim of this study is to develop and validate a deep learning fusion model based on early (two-cycle) ultrasound images, using stacking fusion technology for the intratumoral and peritumoral regions, to predict early tumor response in breast cancer patients receiving neoadjuvant chemotherapy (NAC).

Materials and methods

This study enrolled breast cancer patients who received 6 to 8 cycles of neoadjuvant chemotherapy (NAC) between May 2019 and September 2023 from two medical centers. In the training set, the ResNet deep learning method was employed to construct a deep learning model based on ultrasound image data from intratumoral and peritumoral regions. A deep learning fusion model was established using stacking technology, which was tested on internal and extern

SUBMITTER: Liu J 

PROVIDER: S-EPMC12604160 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature