A Two-Stage Dual-Task Learning Strategy for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy for Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Images

Authors

Bowen Jing
Jing Wang

Language

English

Publication Date

7-18-2025

Journal

Physics in Medicine and Biology

DOI

10.1088/1361-6560/adee73

PMID

40639409

PMCID

PMC12272044

PubMedCentral® Posted Date

7-18-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Early prediction of treatment response can facilitate personalized treatment for breast cancer patients. Studies on the I-SPY 2 clinical trial demonstrate that multi-time point dynamic contrast-enhanced magnetic resonance (DCEMR) imaging improves the accuracy of predicting pathological complete response (pCR) to chemotherapy. However, previous image-based prediction models usually rely on mid- or post-treatment images to ensure the accuracy of prediction, which may outweigh the benefit of response-based adaptive treatment strategy. Accurately predicting the pCR at the early time point is desired yet remains challenging. To improve prediction accuracy at the early time point of treatment, we proposed a two-stage dual-task learning strategy to train a deep neural network for early prediction using only early-treatment data. We developed and evaluated our proposed method using the I-SPY 2 dataset, which included DCEMR images acquired at three time points: pretreatment (T0), after 3 weeks (T1) and 12 weeks of treatment (T2). At the first stage, we trained a convolutional long short-term memory model using all the data to predict pCR and extract the latent space image representation at T2. At the second stage, we trained a dual-task model to simultaneously predict pCR and the image representation at T2 using images from T0 and T1. This allowed us to predict pCR earlier without using images from T2. By using the conventional single-stage single-task strategy, the area under the receiver operating characteristic curve (AUROC) was 0.799. By using the proposed two-stage dual-task learning strategy, the AUROC was improved to 0.820. Our proposed two-stage dual-task learning strategy can improve model performance significantly (p = 0.0025) for predicting pCR at the early time point (3rd week) of neoadjuvant chemotherapy for high-risk breast cancer patients. The early prediction model can potentially help physicians to intervene early and develop personalized plans at the early stage of chemotherapy. Investigation of Serial studies to Predict Your Therapeutic Response with Imaging and Molecular analysis 2 (I-SPY2) trial is registered on ClinicalTrials.gov with the identifier NCT01042379.

Keywords

Female, Humans, Breast Neoplasms, Contrast Media, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neoadjuvant Therapy, Treatment Outcome, Datasets as Topic, breast cancer, dynamic contrast-enhanced magnetic resonance image, multi-task learning, neoadjuvant chemotherapy, pathological complete response

Published Open-Access

yes

Recommended Citation

Bowen Jing and Jing Wang, "A Two-Stage Dual-Task Learning Strategy for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy for Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Images" (2025). Faculty, Staff and Student Publications. 6666.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6666