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Zhou H, Zhao Q, Huang W, Liang Z, Cui C, Ma H, Luo C, Li S, Ruan G, Chen H, Zhu Y, Zhang G, Liu S, Liu L, Li H, Yang H, Xie H. A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma. Radiother Oncol 2024; 197:110367. [PMID: 38834152 DOI: 10.1016/j.radonc.2024.110367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 05/28/2024] [Accepted: 06/01/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. METHODS We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). RESULTS In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. CONCLUSIONS The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.
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Affiliation(s)
- Haoyang Zhou
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, PR China.
| | - Qin Zhao
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Wenjie Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Zhiying Liang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Chunyan Cui
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Huali Ma
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Chao Luo
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Shuqi Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Guangying Ruan
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Hongbo Chen
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, PR China.
| | - Yuliang Zhu
- Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, ZhongShan, PR China.
| | - Guoyi Zhang
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People's Hospital of Foshan, Foshan, PR China.
| | - Shanshan Liu
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, PR China.
| | - Lizhi Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Haojiang Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Hui Yang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Hui Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
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Zhou H, Hua Z, Gao J, Lin F, Chen Y, Zhang S, Zheng T, Wang Z, Shao H, Li W, Liu F, Li Q, Chen J, Wang X, Zhao F, Qu N, Xie H, Ma H, Zhang H, Mao N. Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study. J Magn Reson Imaging 2024; 59:1710-1722. [PMID: 37497811 DOI: 10.1002/jmri.28913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE Retrospective. POPULATION 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Heng Zhou
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Zhen Hua
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Yuqian Chen
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Shijie Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Tiantian Zheng
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Huafei Shao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Wenjuan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Fengjie Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jingjing Chen
- Department of Radiology, Qingdao University Affiliated Hospital, Qingdao, Shandong, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Nina Qu
- Department of Ultrasound, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
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Grøvik E. Editorial for "MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model". J Magn Reson Imaging 2024. [PMID: 38217385 DOI: 10.1002/jmri.29226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/09/2023] [Indexed: 01/15/2024] Open
Affiliation(s)
- Endre Grøvik
- Department of Radiology, Division of Diagnostics, Møre and Romsdal Hospital Trust, Ålesund, Norway
- Department of Physics, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim Ålesund Gjøvik, Norway
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Guo YJ, Yin R, Zhang Q, Han JQ, Dou ZX, Wang PB, Lu H, Liu PF, Chen JJ, Ma WJ. MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model. J Magn Reson Imaging 2024. [PMID: 38205712 DOI: 10.1002/jmri.29225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. PURPOSE To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. STUDY TYPE Retrospective. SUBJECTS 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. FIELD STRENGTH/SEQUENCE 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. ASSESSMENT Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. STATISTICAL TESTS Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. RESULTS The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. DATA CONCLUSION A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yi-Jun Guo
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Rui Yin
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China
| | - Qian Zhang
- Department of Radiology, Baoding No. 1 Central Hospital, Baoding, China
| | - Jun-Qi Han
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhao-Xiang Dou
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Peng-Bo Wang
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Hong Lu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Pei-Fang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jing-Jing Chen
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen-Juan Ma
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [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: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Xu Z, Rauch DE, Mohamed RM, Pashapoor S, Zhou Z, Panthi B, Son JB, Hwang KP, Musall BC, Adrada BE, Candelaria RP, Leung JWT, Le-Petross HTC, Lane DL, Perez F, White J, Clayborn A, Reed B, Chen H, Sun J, Wei P, Thompson A, Korkut A, Huo L, Hunt KK, Litton JK, Valero V, Tripathy D, Yang W, Yam C, Ma J. Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer. Cancers (Basel) 2023; 15:4829. [PMID: 37835523 PMCID: PMC10571741 DOI: 10.3390/cancers15194829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/10/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.
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Affiliation(s)
- Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - David E. Rauch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - Rania M. Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - Benjamin C. Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
| | - Beatriz E. Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rosalind P. Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jessica W. T. Leung
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Huong T. C. Le-Petross
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Deanna L. Lane
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Frances Perez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Alyson Clayborn
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Brandy Reed
- Department of Clinical Research Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Alastair Thompson
- Section of Breast Surgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Anil Korkut
- Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kelly K. Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jennifer K. Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (Z.X.)
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Chen M, Kong C, Lin G, Chen W, Guo X, Chen Y, Cheng X, Chen M, Shi C, Xu M, Sun J, Lu C, Ji J. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study. EClinicalMedicine 2023; 63:102176. [PMID: 37662514 PMCID: PMC10474371 DOI: 10.1016/j.eclinm.2023.102176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023] Open
Abstract
Background For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.
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Affiliation(s)
- Mingzhen Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Xinyu Guo
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Yaning Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Xue Cheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Changsheng Shi
- Department of Interventional Radiology, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, Zhejiang, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Junhui Sun
- Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
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8
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Starodubtseva NL, Tokareva AO, Rodionov VV, Brzhozovskiy AG, Bugrova AE, Chagovets VV, Kometova VV, Kukaev EN, Soares NC, Kovalev GI, Kononikhin AS, Frankevich VE, Nikolaev EN, Sukhikh GT. Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study. Biomedicines 2023; 11:1786. [PMID: 37509426 PMCID: PMC10376786 DOI: 10.3390/biomedicines11071786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/30/2023] Open
Abstract
Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.
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Affiliation(s)
- Natalia L Starodubtseva
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Department of Chemical Physics, Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - Alisa O Tokareva
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Valeriy V Rodionov
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Alexander G Brzhozovskiy
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Anna E Bugrova
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vitaliy V Chagovets
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Vlada V Kometova
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Evgenii N Kukaev
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Nelson C Soares
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Grigoriy I Kovalev
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Alexey S Kononikhin
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Vladimir E Frankevich
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Translational Medicine, Siberian State Medical University, 634050 Tomsk, Russia
| | - Evgeny N Nikolaev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Gennady T Sukhikh
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
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9
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Vrdoljak J, Krešo A, Kumrić M, Martinović D, Cvitković I, Grahovac M, Vickov J, Bukić J, Božic J. The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review. Cancers (Basel) 2023; 15:cancers15082400. [PMID: 37190328 DOI: 10.3390/cancers15082400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023] Open
Abstract
Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.
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Affiliation(s)
- Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Ante Krešo
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Marko Kumrić
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Dinko Martinović
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Ivan Cvitković
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, 21000 Split, Croatia
| | - Josip Vickov
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Josipa Bukić
- Department of Pharmacy, University of Split School of Medicine, 21000 Split, Croatia
| | - Joško Božic
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
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Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions. Diagnostics (Basel) 2023; 13:diagnostics13061007. [PMID: 36980315 PMCID: PMC10047866 DOI: 10.3390/diagnostics13061007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/28/2023] [Accepted: 03/05/2023] [Indexed: 03/09/2023] Open
Abstract
Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.
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