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

TL;DR

This study introduces a novel two-stage dual-task deep learning approach that enhances early prediction of pathological complete response in breast cancer patients undergoing neoadjuvant chemotherapy using dynamic contrast-enhanced MRI data.

Contribution

The paper presents a new two-stage dual-task learning strategy that improves early prediction accuracy of pCR without needing late-treatment images, validated on multi-institutional clinical data.

Findings

AUROC improved from 0.799 to 0.820 with the new method

Significant performance enhancement (p=0.0025) for early prediction

Potential for early intervention and personalized treatment planning

Abstract

Rationale and Objectives: Early prediction of pathological complete response (pCR) can facilitate personalized treatment for breast cancer patients. To improve prediction accuracy at the early time point of neoadjuvant chemotherapy, we proposed a two-stage dual-task learning strategy to train a deep neural network for early prediction of pCR using early-treatment magnetic resonance images. Methods: We developed and validated the two-stage dual-task learning strategy using the dataset from the national-wide, multi-institutional I-SPY2 clinical trial, which included dynamic contrast-enhanced magnetic resonance images acquired at three time points: pretreatment (T0), after 3 weeks (T1), and after 12 weeks of treatment (T2). First, we trained a convolutional long short-term memory network to predict pCR and extract the latent space image features at T2. At the second stage, we trained a dual-task network to simultaneously predict pCR and the image features at T2 using images from T0 and T1. This allowed us to predict pCR earlier without using images from T2. Results: The conventional single-stage single-task strategy gave an area under the receiver operating characteristic curve (AUROC) of 0.799 for pCR prediction using all the data at time points T0 and T1. By using the proposed two-stage dual-task learning strategy, the AUROC was improved to 0.820. Conclusions: The proposed two-stage dual-task learning strategy can improve model performance significantly (p=0.0025) for predicting pCR at the early stage (3rd week) of neoadjuvant chemotherapy. The early prediction model can potentially help physicians to intervene early and develop personalized plans at the early stage of chemotherapy.