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Shi Z, Ma Y, Ma X, Jin A, Zhou J, Li N, Sheng D, Chang C, Chen J, Li J. Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115099. [PMID: 37299826 DOI: 10.3390/s23115099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/14/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023]
Abstract
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.
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Affiliation(s)
- Zhaoting Shi
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Yebo Ma
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, No. 500, Dongchuan Road, Shanghai 200241, China
| | - Xiaowen Ma
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Anqi Jin
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Jin Zhou
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Na Li
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Danli Sheng
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Cai Chang
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, No. 500, Dongchuan Road, Shanghai 200241, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, No. 1200, Cailun Road, Pudong District, Shanghai 201203, China
| | - Jiawei Li
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
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Ma X, Gong J, Hu F, Tang W, Gu Y, Peng W. Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors. J Magn Reson Imaging 2023; 57:633-645. [PMID: 35657093 DOI: 10.1002/jmri.28286] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE Retrospective. POPULATION A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Feixiang Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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An Update on the General Features of Breast Cancer in Male Patients—A Literature Review. Diagnostics (Basel) 2022; 12:diagnostics12071554. [PMID: 35885460 PMCID: PMC9323942 DOI: 10.3390/diagnostics12071554] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 11/28/2022] Open
Abstract
Male breast cancers are uncommon, as men account for less than 1 percent of all breast carcinomas. Among the predisposing risk factors for male breast cancer, the following appear to be significant: (a) breast/chest radiation exposure, (b) estrogen use, diseases associated with hyper-estrogenism, such as cirrhosis or Klinefelter syndrome, and (c) family health history. Furthermore, there are clear familial tendencies, with a higher incidence among men who have a large number of female relatives with breast cancer and (d) major inheritance susceptibility. Moreover, in families with BRCA mutations, there is an increased risk of male breast cancer, although the risk appears to be greater with inherited BRCA2 mutations than with inherited BRCA1 mutations. Due to diagnostic delays, male breast cancer is more likely to present at an advanced stage. A core biopsy or a fine needle aspiration must be performed to confirm suspicious findings. Infiltrating ductal cancer is the most prevalent form of male breast cancer, while invasive lobular carcinoma is extremely uncommon. Male breast cancer is almost always positive for hormone receptors. A worse prognosis is associated with a more advanced stage at diagnosis for men with breast cancer. Randomized controlled trials which recruit both female and male patients should be developed in order to gain more consistent data on the optimal clinical approach.
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Locicero P, Weingertner N, Noblet V, Mondino M, Mathelin C, Molière S. An integrative ultrasound-pathology approach to improve preoperative phyllodes tumor classification: A pilot study. Breast Dis 2022; 41:221-228. [PMID: 35404267 DOI: 10.3233/bd-210025] [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] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Preoperative diagnosis of phyllodes tumor (PT) is challenging, core-needle biopsy (CNB) has a significant rate of understaging, resulting in suboptimal surgical planification. We hypothesized that the association of imaging data to CNB would improve preoperative diagnostic accuracy compared to biopsy alone. METHODS In this retrospective pilot study, we included 59 phyllodes tumor with available preoperative imaging, CNB and surgical specimen pathology. RESULTS Two ultrasound features: tumor heterogeneity and tumor shape were associated with tumor grade, independently of CNB results. Using a machine learning classifier, the association of ultrasound features with CNB results improved accuracy of preoperative tumor classification up to 84%. CONCLUSION An integrative approach of preoperative diagnosis, associating ultrasound features and CNB, improves preoperative diagnosis and could thus optimize surgical planification.
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Affiliation(s)
- Paola Locicero
- Women's Imaging Unit, University Hospital of Strasbourg, Hautepierre Hospital, Strasbourg Cedex, France
- Radiology Department, Saint Catherine Hospital of Saverne, Saverne, France
| | - Noëlle Weingertner
- Pathology Department, University Hospital of Strasbourg, Hautepierre Hospital, Strasbourg Cedex, France
| | - Vincent Noblet
- ICube - IMAGeS, UMR 7357, Illkirch, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), Strasbourg, France
| | | | - Carole Mathelin
- Surgery Department, ICANS (Strasbourg Europe), Strasbourg, France
- Institute of Genetics and Molecular and Cellular Biology CNRS UMR 7104, INSERM U964, University of Strasbourg, Illkirch, France
| | - Sébastien Molière
- Women's Imaging Unit, University Hospital of Strasbourg, Hautepierre Hospital, Strasbourg Cedex, France
- Institute of Genetics and Molecular and Cellular Biology CNRS UMR 7104, INSERM U964, University of Strasbourg, Illkirch, France
- Breast and Thyroid Imaging Unit, ICANS (Strasbourg Europe), Strasbourg, France
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