TY - JOUR
T1 - Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs
AU - Zhou, Zijian
AU - Adrada, Beatriz E.
AU - Candelaria, Rosalind P.
AU - Elshafeey, Nabil
AU - Boge, Medine
AU - Mohamed, Rania M.
AU - Pashapoor, Sanaz
AU - Sun, Jia
AU - Xu, Zhan
AU - Panthi, Bikash
AU - Son, Jong Bum
AU - Guirguis, Mary S.
AU - Patel, Miral M.
AU - Whitman, Gary J.
AU - Moseley, Tanya W.
AU - Scoggins, Marion E.
AU - White, Jason B.
AU - Litton, Jennifer K.
AU - Valero, Vincente
AU - Hunt, Kelly K.
AU - Tripathy, Debu
AU - Yang, Wei
AU - Wei, Peng
AU - Yam, Clinton
AU - Pagel, Mark D.
AU - Rauch, Gaiane M.
AU - Ma, Jingfei
PY - 2023/7/1
Y1 - 2023/7/1
N2 - We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.
AB - We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.
UR - http://www.scopus.com/inward/record.url?scp=85179648062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179648062&partnerID=8YFLogxK
U2 - 10.1109/EMBC40787.2023.10340987
DO - 10.1109/EMBC40787.2023.10340987
M3 - Article
C2 - 38083160
AN - SCOPUS:85179648062
SN - 2694-0604
VL - 2023
SP - 1
EP - 4
JO - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
JF - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
ER -