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Tian R, Lu G, Zhao N, Qian W, Ma H, Yang W. Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1386-1400. [PMID: 38381383 DOI: 10.1007/s10278-024-01036-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/28/2024] [Accepted: 02/02/2024] [Indexed: 02/22/2024]
Abstract
The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.
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Affiliation(s)
- Ronghui Tian
- College of Medicine and Biological Information Engineering, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang, 110819, Liaoning Province, China
| | - Guoxiu Lu
- College of Medicine and Biological Information Engineering, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang, 110819, Liaoning Province, China
- Department of Nuclear Medicine, General Hospital of Northern Theatre Command, No. 83 Wenhua Road, Shenhe District, Shenyang, 110016, Liaoning Province, China
| | - Nannan Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No. 44 Xiaoheyan Road, Dadong District, Shenyang, 110042, Liaoning Province, China
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang, 110819, Liaoning Province, China
| | - He Ma
- College of Medicine and Biological Information Engineering, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang, 110819, Liaoning Province, China
| | - Wei Yang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No. 44 Xiaoheyan Road, Dadong District, Shenyang, 110042, Liaoning Province, China.
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Upadhyay N, Wolska J. Imaging the dense breast. J Surg Oncol 2024; 130:29-35. [PMID: 38685673 DOI: 10.1002/jso.27661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024]
Abstract
The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.
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Affiliation(s)
- Neil Upadhyay
- Faculty of Medicine, Imperial College London, London, UK
- Imaging Department, Imperial College Healthcare NHS Trust, London, UK
| | - Joanna Wolska
- Imaging Department, Imperial College Healthcare NHS Trust, London, UK
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Cozzi A, Schiaffino S. Preoperative breast MRI in women with dense breasts: can we keep up with a rapidly changing scenario? Eur Radiol 2023; 33:8077-8079. [PMID: 37646816 DOI: 10.1007/s00330-023-10075-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 05/14/2023] [Accepted: 06/11/2023] [Indexed: 09/01/2023]
Affiliation(s)
- Andrea Cozzi
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland
| | - Simone Schiaffino
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
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