Deep learning models for predicting responses to neoadjuvant systemic therapy in triple-negative breast cancer using pre-treatment MRI
Motivation: Neoadjuvant systemic therapy (NAST) followed by surgery is the standard of care for triple-negative breast cancer (TNBC) patients. However, only approximately half of these patients achieve pathological complete response (pCR). Goal(s): To build a prediction model to identify non-pCR patients before the initiation of NAST. Approach: We evaluated multiple prediction models using pretreatment multi-parametric MRI from a cohort of 282 TNBC patients. Results: Our findings revealed that combined with clinical information, the best-performing model achieved an AUC of 0.74 on an independent testing set. We further observed that the performance of our models is not sensitive to the voxel selections in tumor segmentation. Impact: Deep learning models for predicting pathological complete response to neoadjuvant systemic therapy of triple-negative breast cancer were developed using baseline multi-parametric MRI data and clinical information and achieved an AUC of 0.74 on the independent testing dataset.
Authors
- Gary J. Whitman (ORCID: https://orcid.org/0000-0003-2002-7457)
- Gaiane M. Rauch (ORCID: https://orcid.org/0000-0003-2079-5202)
- Frances Perez (ORCID: https://orcid.org/0000-0002-4919-1843)
- Rania M. Mohamed (ORCID: https://orcid.org/0000-0002-6487-3965)
- Miral Patel (ORCID: https://orcid.org/0000-0002-0942-2731)
- Deanna L. Lane (ORCID: https://orcid.org/0000-0001-8221-4333)
- Bikash Panthi
- Sanaz Pashapoor
- Jong Bum Son (ORCID: https://orcid.org/0000-0003-2548-5895)
- Mary S. Guirguis (ORCID: https://orcid.org/0000-0001-5874-8861)
- Clinton Yam (ORCID: https://orcid.org/0000-0002-0809-0851)
- Rosalind P. Candelaria (ORCID: https://orcid.org/0000-0002-7897-7896)
- Peng Wei (ORCID: https://orcid.org/0009-0000-3514-6921)
- Tanya W. Moseley (ORCID: https://orcid.org/0000-0002-2018-8253)
- Jessica Leung (ORCID: https://orcid.org/0000-0002-5164-7555)
- Beatriz E. Adrada (ORCID: https://orcid.org/0000-0002-5539-5332)
- Zhan Xu (ORCID: https://orcid.org/0000-0002-5892-0227)
- Huong T. Le‐Petross (ORCID: https://orcid.org/0000-0003-0356-498X)
- Wei Yang (ORCID: https://orcid.org/0000-0003-2936-2972)
- Jingfei Ma (ORCID: https://orcid.org/0000-0003-2827-8891)
- Jia Sun (ORCID: https://orcid.org/0000-0002-1303-4803)
- Huiqin Chen (ORCID: https://orcid.org/0000-0003-2477-5436)
- Debu Tripathy (ORCID: https://orcid.org/0000-0002-5711-2404)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/0856
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00