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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Radiomics-based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using

Maya Gilad¹, Savannah C Partridge^2,3, Mami Iima^4,5

¹Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Radiology. Imaging Cancer

|July 18, 2025

Summary

This summary is machine-generated.

A new machine learning model using physiologically decomposed diffusion-weighted MRI data shows superior performance in predicting breast cancer treatment response. This advanced imaging technique improves prediction accuracy for pathologic complete response (pCR) after neoadjuvant chemotherapy.

Keywords:

Breast Experimental Investigations Image Postprocessing MR-Diffusion Weighted Imaging Tumor Response

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Radiology and Medical Imaging
Machine Learning in Oncology
Breast Cancer Treatment Response Assessment

Background:

Accurate prediction of pathologic complete response (pCR) is crucial for optimizing neoadjuvant chemotherapy in breast cancer.
Traditional methods using tumor size and ADC values have limitations in predicting treatment outcomes.
Radiomics analysis of diffusion-weighted imaging (DWI) offers potential for enhanced predictive capabilities.

Purpose of the Study:

To evaluate a machine learning model utilizing radiomics from physiologically decomposed DWI (PD DWI) for predicting pCR in breast cancer.
To compare the performance of the PD DWI model against baseline and benchmark models.

Main Methods:

Retrospective analysis of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge dataset.
Physiologic decomposition of DWI data to extract pseudo-diffusion, pure-diffusion, and pseudo-diffusion fraction components.
Development of a boosted decision tree model using radiomics features from PD DWI for pCR prediction.

Main Results:

The PD DWI model achieved the highest area under the receiver operating characteristic curve (0.89), significantly outperforming baseline models (P < .04).
Decision curve analysis indicated a greater net benefit for the PD DWI model compared to the BMMR2 challenge benchmark model (0.17 vs 0.09, P < .001).
The model demonstrated statistically significant improvements in predicting pCR.

Conclusions:

A machine learning model incorporating radiomics from PD DWI demonstrates superior performance in predicting pCR for breast cancer patients undergoing neoadjuvant chemotherapy.
This approach offers a promising tool for improving treatment response prediction and potentially guiding clinical decisions.
Physiologically decomposed DWI radiomics enhances the predictive power beyond conventional imaging biomarkers.