Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

作者全名：Yu, Tao; Yu, Renqiang; Liu, Mengqi; Wang, Xingyu; Zhang, Jichuan; Zheng, Yineng; Lv, Fajin

作者地址：[Yu, Tao; Yu, Renqiang; Liu, Mengqi; Wang, Xingyu; Zhang, Jichuan; Zheng, Yineng; Lv, Fajin] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Yu, Tao; Zhang, Jichuan; Zheng, Yineng; Lv, Fajin] Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, Chongqing 400016, Peoples R China; [Zheng, Yineng; Lv, Fajin] Chongqing Med Univ, Med Data Sci Acad, Chongqing 400016, Peoples R China

通信作者：Zheng, YE; Lv, FJ (通讯作者)，1 Youyi Rd, Chongqing 400016, Peoples R China.

来源：EUROPEAN JOURNAL OF RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001256134700001

JCR分区：Q1

影响因子：3.2

年份：2024

卷号：177

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Deep learning; Machine learning; Radiomics; Breast cancer; Magnetic resonance imaging

摘要：Purpose: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists. Materials and Methods: This multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods. Results: The findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors. Conclusion: The fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model's potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis.

基金机构：National Key R&D Program of China [2020YFA0714002]; Joint project of Chongqing Health Commission and Science and Technology Bureau [2022QNXM015, 2022ZDXM006]; Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau [CSTC2021jscx-gksb-N0030]; Youth Innovation in Future Medicine, Chongqing Medical University, Project [W0015]; Chongqing postgraduate mentor team [cqmudstd202222]

基金资助正文：This work was supported by the National Key R&D Program of China (2020YFA0714002), Joint project of Chongqing Health Commission and Science and Technology Bureau (No. 2022QNXM015, 2022ZDXM006), Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau (No. CSTC2021jscx-gksb-N0030), Youth Innovation in Future Medicine, Chongqing Medical University, Project (W0015), and Chongqing postgraduate mentor team under Grant (cqmudstd202222) .