Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Temporal Deep Learning on DWI
Authors: Pablo García Marcos, Md. Tarequl Islam, Paula Puerta González, Guillermo Lorenzo, Héctor Gómez, Covadonga del Camino, Angel Rio-Alvarez, Víctor M. González
Organizations: Computer Sciences Department and the Biomedical Engineering Center (BME), University of Oviedo, 33003 Asturias, Spain · Rabindra University, Sirajganj-6770, Bangladesh · Electrical Engineering Department and the Biomedical Engineering Center (BME), University of Oviedo, 33003 Asturias, Spain · Group of Numerical Methods in Engineering, Department of Mathematics, University of A Coruña, 15071 A Coruña, Spain · Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712 USA · School of Mechanical Engineering, the Weldon School of Biomedical Engineering, and the Purdue Center for Cancer Research, Purdue University, West Lafayette, IN 47907 USA · Radiodiagnostic Service, Asturias Central University Hospital (HUCA), 33011 Oviedo, Spain
Early identification of non-responders to neoadjuvant chemotherapy (NACT) is crucial for timely treatment adaptation in breast cancer. However, many existing predictive models rely on multiparametric magnetic resonance imaging (MRI), late treatment time points, or extensive clinical data, which limits their applicability. This study proposes a deep learning framework for early prediction of pathological complete response (pCR) using only diffusion-weighted MRI (DW-MRI) acquired at baseline and after the first NACT cycle. This framework feeds cropped tumor-centered patches to an EfficientNet-based temporal model that directly learns tumor shape and local tissue characteristics without explicit radiomic feature engineering. The model, trained with 10-fold cross-validation, achieved an area under the receiver operating characteristic curve (AUC) of 0.90 for pCR prediction after one cycle, providing actionable information after a single treatment cycle while avoiding gadolinium administration and reducing dependence on heterogeneous clinical data. By focusing on the baseline-to-first-cycle window instead of later stages, the approach supports earlier escalation or de-escalation of NACT, and its exclusive reliance on DW-MRI facilitates protocol standardization, multi-centre deployment and privacy-preserving data sharing. These results demonstrate that DW-MRI-based deep learning on tumor-centered patches constitutes a minimally invasive, clinically deployable strategy for early pCR prediction, with direct implications for personalized treatment adaptation in neoadjuvant breast cancer therapy.
Figures & tables
Fig. 1 : Slice of a study of the dataset in DW (left), ADC (center) and mask (right) formats.
Fig. 2 : Pre-processing pipeline. Values of the arrows represent Patients×Time points×Slices×Width×Height .
Fig. 3 : NACT response prediction model architecture.
Backbone
Temporal
Acc (%)
F1
EfficientNet-B0
LSTM
76.0
0.77
EfficientNet-B0
Non-recurrent
75.0
0.76
EffNet-B3 (RA)
LSTM
74.0
0.74
EffNet-B3 (RA)
Non-recurrent
71.0
0.72
EffNetV2-B3
LSTM
72.0
0.72
EffNetV2-B3
Non-recurrent
72.0
0.72
TABLE I : Backbone Ablation on the T0–T1 Setting Using 5-Fold Cross-Validation on 50×50 Tumor Patches. Mean Validation Accuracy and F1-Score Across Folds for Different Backbones and Temporal Modules (No Quality Indices).
LSTM configuration
Acc (%)
F1
32 units, dropout 0.30
74.0
0.74
32 units, dropout 0.40
75.0
0.76
64 units, dropout 0.30
75.0
0.76
64 units, dropout 0.40
76.0
0.77
TABLE II : Ablation on LSTM Hidden Size and Dropout Rate for the EfficientNet-B0 Backbone in the T0–T1 Setting Using 5-Fold Cross-Validation on 50×50 Tumor Patches.
Feature set
Acc (%)
F1
Imaging features only
76.0
0.77
Imaging + quality indices
77.0
0.77
TABLE III : Effect of Adding Image Quality Indices (QC) to the Imaging Features Extracted from 50×50 Tumor-Centered Patches (T0–T1, EfficientNet-B0 + LSTM), Using 5-Fold Cross-Validation.
Fold
Acc (%)
F1
AUC
1
88.24
0.88
0.92
2
94.12
0.94
0.97
3
88.24
0.88
0.86
4
70.59
0.71
0.76
5
88.24
0.88
0.91
6
82.35
0.83
0.94
TABLE IV : Per-Fold Validation Accuracy (Acc), F1-Score and AUC for the Final T0–T1 Model with Image Quality Indices (10-Fold Cross-Validation, 50×50 Tumor-Centered Patches).
Fig. 4 : ROC curve showing the average performance across the 10 cross-validation folds.
TABLE V : Comparison of Previous Studies and the Proposed Approach for Early Prediction of pCR to NACT in Breast Cancer. Only MRI-Related Metrics Are Reported for Papers Employing Multiple Approaches.
Computer Science Department, University of Oviedo, Asturias, Spain · Biomedical Engineering Center (BME), University of Oviedo, Asturias, Spain · Electrical Engineering Department, University of Oviedo, Asturias, Spain +7
Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands · The Netherlands Cancer Institute, Amsterdam, The Netherlands · Faculty of Applied Sciences, Macao Polytechnic University, Macau, China +5