Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.
Authors
Affiliations (8)
Affiliations (8)
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China (T.J., D.X., L.Z., D.O., L.W.).
- School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China (X.L.); Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou 317502, China (X,L., Y.Y., L.S., Z.J.).
- Zhejiang Chinese Medical University, Hangzhou 310014, China (J.S).
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou 317502, China (X,L., Y.Y., L.S., Z.J.); Center of Intelligent Diagnosis and Therapy (Taizhou), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Taizhou 317502, China (Y.Y., L.S., Z.J.); Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou 317502, China (Y.Y., Z.J., D.X.).
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China (T.J., D.X., L.Z., D.O., L.W.); Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou 317502, China (X,L., Y.Y., L.S., Z.J.).
- Department of Medical Ultrasound, Peking University Cancer Hospital Yunnan Hospital, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650118, China (Z.C., D.X.).
- Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China (M.J.).
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China (T.J., D.X., L.Z., D.O., L.W.); Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou 317502, China (Y.Y., Z.J., D.X.); Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310000, China (D.X); Wenzhou Medical University, Wenzhou, Zhejiang 325035, China (D.X.). Electronic address: [email protected].
Abstract
To evaluate the diagnostic performance of a longitudinal ultrasound (US)-based stack-model for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer, as well as its practicality in assisting radiologists with diagnostic ability. A total of 974 patients who underwent NAC were retrospectively included between January 2017 and March 2022 from three different institutions. For all patients, US imaging was performed before NAC and after two cycles of NAC. The patients from the first hospital were used as the training dataset (n=653) and patients from hospital 2 and 3 were used as the test dataset (n=321)to test five deep learning (DL) models based on different feature sets. The optimal model was selected according to the area under the receiver operating characteristic curve (AUC). A model combining US imaging features with clinical factors was also investigated. Furthermore, the applicability of this model to provide clinical assistance was examined by radiologists with varying degrees of seniority. The Swin Transformer model based on the stacked-feature set achieved the highest AUC. Upon incorporating clinical factors, the combined model demonstrated superior performance in predicting pCR, achieving AUC of 0.935. Diagnostic performance in the early prediction of pCR improved for radiologists across all experience levels when assisted by the combined model. The longitudinal US-based model enables early prediction of pCR. Additionally, the model provided positive diagnostic assistance to radiologists with different experience levels. The longitudinal US-based model enable non-invasive early prediction of response to neoadjuvant chemotherapy in breast cancer while enhancing diagnostic performance across radiologists with varying experience levels.