Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study
- 1Ultrasound Medical Center, The Second Hospital of Lanzhou University, Lanzhou 730030, China
- 2Department of Ultrasound Diagnosis, Gansu Provincial Maternity and Child-Care Hospital (Gansu Provincial Central Hospital), Lanzhou 730050, China
- 3Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
Abstract
Objective: To evaluate the incremental predictive value of pretreatment tri-modal ultrasound deep learning integrating two-dimensional ultrasound (2DUS), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) for predicting breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC), and to assess performance stability across independent centers. Methods: This retrospective multicenter study included 1089 patients with primary invasive breast cancer. Center 1 provided the training cohort (n = 572) and internal validation cohort (n = 246), whereas centers 2 (n = 160) and 3 (n = 111) served as independent external validation cohorts. Using pretreatment 2DUS, CDFI, and CEUS within a standardized intratumoral and 10-mm peritumoral framework, single-modality, all three dual-modality combinations, and tri-modal models were developed. The best-performing dual-modality model was identified and locked during training as the primary imaging comparator. A Tri-modal + Clinical model further integrated pretreatment clinical and biomarker variables. Performance was assessed using AUC, Brier score, calibration, decision curve analysis, and ablation experiments. Results: Overall, 383 patients (35.2%) achieved bpCR. CEUS was the strongest single modality, and 2DUS + CEUS was the best-performing dual-modality model. Compared with 2DUS + CEUS, the tri-modal model increased the AUC from 0.895 to 0.919 in the combined external cohort (ΔAUC, +0.023; 95% CI, +0.010 to +0.038; p = 0.001), with corresponding improvements in Brier score (0.124 to 0.105) and F1 score (0.747 to 0.786). The AUC point estimate increased in both external centers, although the 95% CI for the incremental AUC included zero in external validation cohort 2. Despite modest standalone discrimination, CDFI provided measurable incremental information when added to 2DUS + CEUS. Further integration of pretreatment clinical and biomarker variables increased the combined external AUC to 0.935. Reduced-input retraining showed performance decreases after omission of any modality, with the largest reduction after CEUS omission. Conclusions: Pretreatment tri-modal ultrasound deep learning provided incremental predictive value for bpCR beyond the best dual-modality model, with a consistent direction of benefit across two independent external centers. Clinical and biomarker integration provided additional predictive information, supporting further evaluation of this multimodal framework for individualized pretreatment bpCR prediction.
Related
- Pretreatment Body Composition and Its Association with Pathologic Complete Response and Survival in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy: A Retrospective Cohort Study
- Multimodal prediction of neoadjuvant chemotherapy response in locally advanced gastric cancer: a multicenter cohort study
- Development and validation of a nomogram predicting pathologic complete response to neoadjuvant chemotherapy in luminal type breast cancer patients using MammaPrint® and clinicopathological factor
- Development and validation of a machine learning-based prediction model for anemia risk in patients with primary lung cancer receiving platinum-based chemotherapy: a multicenter study
- A pretreatment ECM-high/immune-low transcriptomic score is associated with pathological complete response after neoadjuvant therapy in breast cancer
BibTeX
@article{wei2026incremental,
title = {{Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study}},
author = {Chuanmin Wei and Ting Pan and Tiangang Li and Chunyao Zheng and Ying Duan and Fang Nie},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18203326},
url = {https://www.mdpi.com/2072-6694/18/20/3326},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study
AU - Chuanmin Wei
AU - Ting Pan
AU - Tiangang Li
AU - Chunyao Zheng
AU - Ying Duan
AU - Fang Nie
PY - 2026
DA - 2026/10/01
JO - Cancers
DO - 10.3390/cancers18203326
UR - https://www.mdpi.com/2072-6694/18/20/3326
AB - Objective: To evaluate the incremental predictive value of pretreatment tri-modal ultrasound deep learning integrating two-dimensional ultrasound (2DUS), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) for predicting breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC), and to assess performance stability across independent centers. Methods: This retrospective multicenter study included 1089 patients with primary invasive breast cancer. Center 1 provided the training cohort (n = 572) and internal validation cohort (n = 246), whereas centers 2 (n = 160) and 3 (n = 111) served as independent external validation cohorts. Using pretreatment 2DUS, CDFI, and CEUS within a standardized intratumoral and 10-mm peritumoral framework, single-modality, all three dual-modality combinations, and tri-modal models were developed. The best-performing dual-modality model was identified and locked during training as the primary imaging comparator. A Tri-modal + Clinical model further integrated pretreatment clinical and biomarker variables. Performance was assessed using AUC, Brier score, calibration, decision curve analysis, and ablation experiments. Results: Overall, 383 patients (35.2%) achieved bpCR. CEUS was the strongest single modality, and 2DUS + CEUS was the best-performing dual-modality model. Compared with 2DUS + CEUS, the tri-modal model increased the AUC from 0.895 to 0.919 in the combined external cohort (ΔAUC, +0.023; 95% CI, +0.010 to +0.038; p = 0.001), with corresponding improvements in Brier score (0.124 to 0.105) and F1 score (0.747 to 0.786). The AUC point estimate increased in both external centers, although the 95% CI for the incremental AUC included zero in external validation cohort 2. Despite modest standalone discrimination, CDFI provided measurable incremental information when added to 2DUS + CEUS. Further integration of pretreatment clinical and biomarker variables increased the combined external AUC to 0.935. Reduced-input retraining showed performance decreases after omission of any modality, with the largest reduction after CEUS omission. Conclusions: Pretreatment tri-modal ultrasound deep learning provided incremental predictive value for bpCR beyond the best dual-modality model, with a consistent direction of benefit across two independent external centers. Clinical and biomarker integration provided additional predictive information, supporting further evaluation of this multimodal framework for individualized pretreatment bpCR prediction.
SN - 2072-6694
ER -