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Wu H, Jiang Y, Tian H, Ye X, Cui C, Shi S, Chen M, Ding Z, Li S, Huang Z, Luo Y, Peng Q, Xu J, Dong F. Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108039. [PMID: 38266556 DOI: 10.1016/j.cmpb.2024.108039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.
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Affiliation(s)
- Huaiyu Wu
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Yitao Jiang
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China; Research and Development Department, Microport Prophecy, Shanghai 201203, China
| | - Hongtian Tian
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Xiuqin Ye
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Chen Cui
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Siyuan Shi
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Ming Chen
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Zhimin Ding
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Shiyu Li
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Zhibin Huang
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Yuwei Luo
- Department of Breast Surgery, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China; Department of General Surgery, Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Quanzhou Peng
- Department of Pathology, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China.
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