Deep Learning Models Based on Pretreatment MRI and Clinicopathological Data to Predict Responses to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer
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Zhan Xu
1,
Zijian Zhou
1,
Jong Bum Son
1,
Haonan Feng
2,
Beatriz E. Adrada
3,
Tanya W. Moseley
3,
Rosalind P. Candelaria
3,
Mary S. Guirguis
3,
Miral M. Patel
3,
Gary J. Whitman
3
,
Jessica W. T. Leung
3,
Huong T. C. Le-Petross
3,
Rania M. Mohamed
3,
Bikash Panthi
1,
Deanna L. Lane
3,
Huiqin Chen
2,
Peng Wei
2,
Debu Tripathy
4,
Jennifer K. Litton
4,
Vicente Valero
4,
Lei Huo
5,
Kelly K. Hunt
6,
Anil Korkut
7,
Alastair Thompson
8,
Wei Yang
3,
Clinton Yam
4,
Gaiane M. Rauch
3,9,† and
Jingfei Ma
1,*,†add
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1
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
2
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
3
Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
4
Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
5
Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
6
Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
7
Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
8
Section of Breast Surgery, Baylor College of Medicine, 7200 Cambridge St., Houston, TX 77030, USA
9
Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2025, 17(6), 966; https://doi.org/10.3390/cancers17060966
Submission received: 6 February 2025
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Revised: 27 February 2025
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Accepted: 4 March 2025
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Published: 13 March 2025
(This article belongs to the Special Issue Advances in Triple-Negative Breast Cancer)
Simple Summary
Deep learning models based on pretreatment MRI and clinicopathological data were constructed to predict responses to neoadjuvant systemic therapy in triple-negative breast cancer. The best-performing deep learning model developed from the pretreatment multiparametric breast MRI and clinicopathological data acquired from 282 patients with stage I–III triple-negative breast cancer enrolled in a prospective clinical trial achieved an AUC of 0.76 in the internal testing dataset. The deep learning model achieved an AUC of 0.72 in the external I-SPY 2 trial testing dataset. Tumor volume preprocessing affected the model performance; the 3D model frameworks, tumor ROI selection, and data inputs had minimal impact on the model performance.
Abstract
Purpose: To develop deep learning models for predicting the pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in patients with triple-negative breast cancer (TNBC) based on pretreatment multiparametric breast MRI and clinicopathological data. Methods: The prospective institutional review board-approved study [NCT02276443] included 282 patients with stage I–III TNBC who had multiparametric breast MRI at baseline and underwent NAST and surgery during 2016–2021. Dynamic contrast-enhanced MRI (DCE), diffusion-weighted imaging (DWI), and clinicopathological data were used for the model development and internal testing. Data from the I-SPY 2 trial (2010–2016) were used for external testing. Four variables with a potential impact on model performance were systematically investigated: 3D model frameworks, tumor volume preprocessing, tumor ROI selection, and data inputs. Results: Forty-eight models with different variable combinations were investigated. The best-performing model in the internal testing dataset used DCE, DWI, and clinicopathological data with the originally contoured tumor volume, the tight bounding box of the tumor mask, and ResNeXt50, and achieved an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.60–0.88). The best-performing models in the external testing dataset achieved an AUC of 0.72 (95% CI: 0.57–0.84) using only DCE images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNeXt50) and an AUC of 0.72 (95% CI: 0.56–0.86) using only DWI images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNet18). Conclusions: We developed 3D deep learning models based on pretreatment data that could predict pCR to NAST in TNBC patients.
