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Ye J, Chen Y, Pan J, Qiu Y, Luo Z, Xiong Y, He Y, Chen Y, Xie F, Huang W. US-based radiomics analysis of different machine learning models for differentiating benign and malignant BI-RADS 4A breast lesions. Acad Radiol 2024:S1076-6332(24)00587-7. [PMID: 39191562 DOI: 10.1016/j.acra.2024.08.024] [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: 06/07/2024] [Revised: 08/06/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate and authenticate the effectiveness of various radiomics models in distinguishing between benign and malignant BI-RADS 4A lesions. METHODS A total of 936 patients with pathologically confirmed 4A lesions were included in the study (training cohort: n = 655; test cohort: n = 281). Radiomic features were derived from greyscale US images. Following dimensionality reduction and feature selection, radiomics models were developed using logistic regression (LR), support vector machine (SVM), random forest (RF), eXtreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. Univariate and multivariable logistic regression analyses were employed to investigate clinical-radiological characteristics and determine variables for creating a clinical model. Five combined models integrating radiomic and clinical parameters were constructed by using each algorithm, and comparison with radiologists' performance was performed. SHapley Additive exPlanations (SHAP) approach was used to elucidate the radiomic model by ranking the significance of features based on their contribution to the evaluation. RESULTS A total of 1561 radiomic features were extracted. Thirty-six features were deemed significant by dimensionality reduction and selection. The radiomic models showed good performance with AUCs of 0.829-0.945 in training cohort; and 0.805-0.857 in test cohort. The combined model developed by using LR showed the best performance (AUC, training cohort: 0.909; test cohort: 0.905), which is superior to radiologists' performance. Decision curve analysis (DCA) of this combined model indicated better clinical efficacy than clinical and radiomic models. CONCLUSIONS The combined model integrating radiomic and clinical features demonstrated excellent performance in differentiating between benign and malignant 4A lesions. It may offer a non-invasive and efficient approach to aid in clinical decision-making.
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Affiliation(s)
- Jieyi Ye
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.)
| | - Yinting Chen
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.)
| | - Jiawei Pan
- Department of Information Science, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.P.)
| | - Yide Qiu
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.)
| | - Zhuoru Luo
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.)
| | - Yue Xiong
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.)
| | - Yanping He
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.)
| | - Yingyu Chen
- Department of Radiology and Medical Ultrasonics, Leping Hospital Affiliated to Foshan First People's Hospital, 10 Lenan Road, Foshan 528100, Guangdong, China (Y.C., F.X.)
| | - Fuqing Xie
- Department of Radiology and Medical Ultrasonics, Leping Hospital Affiliated to Foshan First People's Hospital, 10 Lenan Road, Foshan 528100, Guangdong, China (Y.C., F.X.)
| | - Weijun Huang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, 81 Lingnan North Road, Foshan 528000, Guangdong, China (J.Y., Y.C., Y.Q., Z.L., Y.X., Y.H., W.H.).
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Buzatto IPC, Recife SA, Miguel L, Bonini RM, Onari N, Faim ALPA, Silvestre L, Carlotti DP, Fröhlich A, Tiezzi DG. Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features. Breast Cancer Res Treat 2024:10.1007/s10549-024-07429-0. [PMID: 39002069 DOI: 10.1007/s10549-024-07429-0] [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: 10/02/2023] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies. METHODS We included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6 that underwent US-guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset). We tested the performance of the final model with 403 new suspicious lesions. RESULTS The most informative attributes were shape, margin, orientation and size of the lesions, the resistance index of the internal vessel, the age of the patient and the presence of a palpable lump. The highest mean negative predictive value (NPV) was achieved with the K-Nearest Neighbors algorithm (97.9%). Making ensembles did not improve the performance. Tuning the threshold did improve the performance of the models and we chose the algorithm XGBoost with the tuned threshold as the final one. The tested performance of the final model was: NPV 98.1%, false negative 1.9%, positive predictive value 77.1%, false positive 22.9%. Applying this final model, we would have missed 2 of the 231 malignant lesions of the test dataset (0.8%). CONCLUSION Machine learning can help physicians predict malignancy in suspicious breast lesions identified by the US. Our final model would be able to avoid 60.4% of the biopsies in benign lesions missing less than 1% of the cancer cases.
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Affiliation(s)
- I P C Buzatto
- Department of Obstetrics and Gynecology - Breast Disease Division, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - S A Recife
- Department of Gynecology & Obstetrics, Women's Health Reference Center of Ribeirão Preto (MATER), Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - L Miguel
- Department of Gynecology & Obstetrics, Women's Health Reference Center of Ribeirão Preto (MATER), Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - R M Bonini
- Department of Radiology, Hospital de Amor de Campo Grande, Campo Grande, Mato Grosso Do Sul, Brazil
| | - N Onari
- Department of Radiology, Hospital de Amor de Barretos, Barretos, Brazil
| | - A L P A Faim
- Department of Radiology, Hospital de Amor de Barretos, Barretos, Brazil
| | - L Silvestre
- Department of Obstetrics and Gynecology - Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - D P Carlotti
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - A Fröhlich
- Department of Mathematics, Federal University of Santa Catarina, Florianópolis, Brazil
| | - D G Tiezzi
- Department of Obstetrics and Gynecology - Breast Disease Division and Laboratory for Translational Data Science, Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3.900, Monte Alegre, Ribeirão Preto, Ribeirão Preto, Brazil.
- Advanced Research Center in Medicine, Union of the Colleges of the Great Lakes (UNILAGO), São José Do Rio Preto, São Paulo, Brazil.
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Ma S, Li Y, Yin J, Niu Q, An Z, Du L, Li F, Gu J. Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance. Front Oncol 2024; 14:1374278. [PMID: 38756651 PMCID: PMC11096442 DOI: 10.3389/fonc.2024.1374278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024] Open
Abstract
Objective In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. Results A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. Conclusions The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.
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Affiliation(s)
- Sai Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Yin
- Department of Ultrasound, Shanghai Fourth People’s Hospital, Shanghai, China
| | - Qinghua Niu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zichen An
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lianfang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiying Gu
- Department of Ultrasound, Shidong Hospital, Yangpu District, Shanghai, China
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Xu Z, Lin Y, Huo J, Gao Y, Lu J, Liang Y, Li L, Jiang Z, Du L, Lang T, Wen G, Li Y. A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment. Eur Radiol 2024; 34:2608-2618. [PMID: 37840099 DOI: 10.1007/s00330-023-10255-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 07/23/2023] [Accepted: 07/30/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments. METHODS This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance. RESULTS Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83-0.91), 0.91 (95% CI, 0.87 - 0.96), and 0.92 (95% CI, 0.86-0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from - 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram. CONCLUSION When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses. CLINICAL RELEVANCE STATEMENT The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice. KEY POINTS • Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions.
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Affiliation(s)
- Ziting Xu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Yue Lin
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Jiekun Huo
- Department of Imaging, Zengcheng Branch of Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Yang Gao
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Jiayin Lu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Yu Liang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Lian Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Zhouyue Jiang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Lingli Du
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Ting Lang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Ge Wen
- Department of Imaging, Zengcheng Branch of Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
| | - Yingjia Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
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Wanderley MC, Soares CMA, Morais MMM, Cruz RM, Lima IRM, Chojniak R, Bitencourt AGV. Application of artificial intelligence in predicting malignancy risk in breast masses on ultrasound. Radiol Bras 2023; 56:229-234. [PMID: 38204896 PMCID: PMC10775818 DOI: 10.1590/0100-3984.2023.0034] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/16/2023] [Accepted: 07/05/2023] [Indexed: 01/12/2024] Open
Abstract
Objective To evaluate the results obtained with an artificial intelligence-based software for predicting the risk of malignancy in breast masses from ultrasound images. Materials and Methods This was a retrospective, single-center study evaluating 555 breast masses submitted to percutaneous biopsy at a cancer referral center. Ultrasonographic findings were classified in accordance with the BI-RADS lexicon. The images were analyzed by using Koios DS Breast software and classified as benign, probably benign, low to intermediate suspicion, high suspicion, or probably malignant. The histological classification was considered the reference standard. Results The mean age of the patients was 51 years, and the mean mass size was 16 mm. The radiologist evaluation had a sensitivity and specificity of 99.1% and 34.0%, respectively, compared with 98.2% and 39.0%, respectively, for the software evaluation. The positive predictive value for malignancy for the BI-RADS categories was similar between the radiologist and software evaluations. Two false-negative results were identified in the radiologist evaluation, the masses in question being classified as suspicious by the software, whereas four false-negative results were identified in the software evaluation, the masses in question being classified as suspicious by the radiologist. Conclusion In our sample, the performance of artificial intelligence-based software was comparable to that of a radiologist.
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Affiliation(s)
| | | | | | | | | | - Rubens Chojniak
- Department of Imaging, A.C.Camargo Cancer Center, São Paulo,
SP, Brazil
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Ji H, Zhu Q, Ma T, Cheng Y, Zhou S, Ren W, Huang H, He W, Ran H, Ruan L, Guo Y, Tian J, Chen W, Chen L, Wang Z, Zhou Q, Niu L, Zhang W, Yang R, Chen Q, Zhang R, Wang H, Li L, Liu M, Nie F, Zhou A. Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3-5 nodule classification among radiologists: a multiple center study. Quant Imaging Med Surg 2023; 13:3671-3687. [PMID: 37284087 PMCID: PMC10240028 DOI: 10.21037/qims-22-1091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 04/07/2023] [Indexed: 06/08/2023]
Abstract
Background Significant differences exist in the classification outcomes for radiologists using ultrasonography-based Breast Imaging Reporting and Data Systems for diagnosing category 3-5 (BI-RADS 3-5) breast nodules, due to a lack of clear and distinguishing image features. Consequently, this retrospective study investigated the improvement of BI-RADS 3-5 classification consistency using a transformer-based computer-aided diagnosis (CAD) model. Methods Independently, 5 radiologists performed BI-RADS annotations on 21,332 breast ultrasonographic images collected from 3,978 female patients from 20 clinical centers in China. All images were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity (SEN), specificity (SPE), accuracy (ACC), area under the curve (AUC), and calibration curve were evaluated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS classification results for the sampling test set provided by CAD to determine whether classification consistency (the k value), SEN, SPE, and ACC could be improved. Results After the training set (11,238 images) and validation set (2,996 images) were learned by the CAD model, the classification ACC of the CAD model applied to the test set (7,098 images) was 94.89% in category 3, 96.90% in category 4A, 95.49% in category 4B, 92.28% in category 4C, and 95.45% in category 5 nodules. Based on pathological results, the AUC of the CAD model was 0.924 and the predicted probability of CAD was a little higher than the actual probability in the calibration curve. After referencing BI-RADS classification results, the adjustments were made to 1,583 nodules, of which 905 were classified to a lower category and 678 to a higher category in the sampling test set. As a result, the ACC (72.41-82.65%), SEN (32.73-56.98%), and SPE (82.46-89.26%) of the classification by each radiologist were significantly improved on average, with the consistency (k values) in almost all of them increasing to >0.6. Conclusions The radiologist's classification consistency was markedly improved with almost all the k values increasing by a value greater than 0.6, and the diagnostic efficiency was also improved by approximately 24% (32.73% to 56.98%) and 7% (82.46% to 89.26%) for SEN and SPE, respectively, of the total classification on average. The transformer-based CAD model can help to improve the radiologist's diagnostic efficacy and consistency with others in the classification of BI-RADS 3-5 nodules.
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Affiliation(s)
- Hongtao Ji
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qiang Zhu
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Teng Ma
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yun Cheng
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Shuai Zhou
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wei Ren
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Huilian Huang
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wen He
- Department of Ultrasonography, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Haitao Ran
- Department of Ultrasound, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Litao Ruan
- Department of Medical Ultrasound, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Yanli Guo
- Department of Ultrasound, The Southwest Hospital, Army Medical University, Chongqing, China
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Wu Chen
- Department of Ultrasound, The First Hospital, Shanxi Medical University, Taiyuan, China
| | - Luzeng Chen
- Department of Ultrasound, The First Hospital, Peking University, Beijing, China
| | - Zhiyuan Wang
- Department of Ultrasound, Diagnosis Center of Ultrasound, Hunan Province Cancer Hospital, Changsha, China
| | - Qi Zhou
- Department of Ultrasound, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Lijuan Niu
- Department of Ultrasound, Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Zhang
- Department of Ultrasonography, The Third Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Ruimin Yang
- Department of Ultrasound, The Frist Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Qin Chen
- Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruifang Zhang
- Department of Ultrasound, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Hui Wang
- Department of Ultrasound, China-Japan Union Hospital, Jilin University, Changchun, China
| | - Li Li
- Department of Ultrasound, Qilu Hospital of Shandong University, Qingdao, China
| | - Minghui Liu
- Department of Ultrasound Diagnosis, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Fang Nie
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Aiyun Zhou
- Department of Ultrasound, The First Affiliated Hospital, Nanchang University, Nanchang, China
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Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review. Diagnostics (Basel) 2022; 13:diagnostics13010058. [PMID: 36611350 PMCID: PMC9818181 DOI: 10.3390/diagnostics13010058] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. METHODS Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. RESULTS A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. CONCLUSIONS US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.
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Discrimination between phyllodes tumor and fibro-adenoma: Does artificial intelligence-aided mammograms have an impact? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00734-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The indulgence of artificial intelligence (AI) has been considered recently in the work up for the detection and diagnosis of breast cancer through algorithms that could supply diagnosis as the radiologist do. The algorithm learns from a supervised and continuous input of large and new data sets unlike the standard programming, which requires clear step-by-step instructions. The aim of this study is to assess the ability of AI scanned mammograms to aid the ultrasound in the discrimination between phyllodes tumors and fibro-adenomas.
Results
This is a retrospective analysis included 374 proven phyllodes tumors (PT) and fibro-adenomas (FA). Digital mammogram and breast ultrasound was performed for all the cases and each breast was given a “Breast Imaging Reporting and Data System” (BI-RADS) score. Included mammograms were scanned by AI with resultant a qualitative heatmap and a quantitative abnormality scoring of suspicion percentage.
The study included 164 PT (43.9%) and 210 FA (56.1%). BI-RADS category 2 was assigned in 40.1%, category 3 in 38.2%, category 4 in 18.5% and category 5 in 3.2% with median value of the AI abnormality scoring of 23%, 44%, 65% and 90% respectively. Sensitivity and specificity of the conventional imaging were 59.2% and 75.8% respectively. The AI abnormality scoring of 49.5% upgraded the sensitivity to 89.6% and specificity to 94.8% in the ability to discriminate PT from FA masses.
Conclusion
Artificial intelligence-aided mammograms could be used as method of distinction between PT from FA detected on sono-mammogram. The color hue and the quantification of the abnormality scoring percentage could be used as a one setting method for specification and so guide clinicians in their decision of conservative management or the choice of the surgical procedure.
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Zhao Z, Hou S, Li S, Sheng D, Liu Q, Chang C, Chen J, Li J. Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2267-2275. [PMID: 36055860 DOI: 10.1016/j.ultrasmedbio.2022.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/31/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.
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Affiliation(s)
- Zhijin Zhao
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Size Hou
- Department of Applied Mathematics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Shuang Li
- International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Danli Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Liu
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
| | - Jiawei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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10
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Lan Z, Peng Y. Artificial intelligence diagnosis based on breast ultrasound imaging. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:1009-1015. [PMID: 36097768 PMCID: PMC10950100 DOI: 10.11817/j.issn.1672-7347.2022.220110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Indexed: 06/15/2023]
Abstract
Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.
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Affiliation(s)
- Zihan Lan
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China.
| | - Yulan Peng
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China.
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11
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Gu J, Jiang T. Ultrasound radiomics in personalized breast management: Current status and future prospects. Front Oncol 2022; 12:963612. [PMID: 36059645 PMCID: PMC9428828 DOI: 10.3389/fonc.2022.963612] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/01/2022] [Indexed: 11/18/2022] Open
Abstract
Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.
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Affiliation(s)
- Jionghui Gu
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
- Zhejiang University Cancer Center, Hangzhou, China
| | - Tian'an Jiang
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
- Zhejiang University Cancer Center, Hangzhou, China
- *Correspondence: Tian'an Jiang,
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12
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Jiang X, Xu W, Zhao Y. Application of CT Imaging in Differential Diagnosis and Nursing of Endocrine Tumors. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:4071081. [PMID: 36043145 PMCID: PMC9377953 DOI: 10.1155/2022/4071081] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/13/2022] [Accepted: 07/21/2022] [Indexed: 11/18/2022]
Abstract
In order to investigate the value of preoperative X-ray computed tomography (CT) in predicting the pathological grade of pancreatic neuroendocrine tumors. This paper retrospectively analyzed the CT image examination of pancreatic neuroendocrine tumors, the image characteristics of G-NEC detected by CT image, and the detection of GST by spiral CT. In order to clearly diagnose and evaluate the size and scope of the focus, whether there is adjacent tissue invasion, metastasis, and treatment effect, CT, MR, PET-CT, nuclide specific imaging, and other imaging methods are widely used in the medical treatment of pNEN patients. These imaging methods have the advantages of noninvasive, rapid imaging, objective image medium, and strong repeatability. If the pathological grade of pNEN patients can be obtained by imaging examination before operation, it will be of great benefit to the formulation of treatment strategies and the prediction of clinical outcomes. Combining CT image performance with imaging omics characteristics to establish a prediction model that can develop a better auxiliary decision-making tool for clinical practice. Different pathological grades prompt clinicians to provide personalized and accurate medical treatment for patients, and reduce excessive medical treatment or wrong judgment caused by unclear preoperative diagnostic information.
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Affiliation(s)
- Xue Jiang
- Department of Endocrinology, The Second Hospital of Jilin University, Changchun, Jinlin 130000, China
| | - Weiwei Xu
- Department of Oncology and Hematology, The Second Hospital of Jilin University, Changchun 130000, China
| | - Ying Zhao
- Department of Endocrinology, The Second Hospital of Jilin University, Changchun, Jinlin 130000, China
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13
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Wei Q, Zeng SE, Wang LP, Yan YJ, Wang T, Xu JW, Zhang MY, Lv WZ, Dietrich CF, Cui XW. The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1355-1363. [PMID: 34432320 DOI: 10.1002/jum.15816] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/20/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists. METHODS From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed. RESULTS After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05). CONCLUSIONS Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.
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Affiliation(s)
- Qi Wei
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shu-E Zeng
- Department of Medical Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li-Ping Wang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu-Jing Yan
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Wang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian-Wei Xu
- Department of Medical Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meng-Yi Zhang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Hirslanden Beau Site, Salem und Permancence, Bern, Switzerland
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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14
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Yao R, Zhang Y, Wu K, Li Z, He M, Fengyue B. Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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15
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Solitary Fibrous Tumor of the Spine: Imaging Grading Diagnosis and Prognosis. J Comput Assist Tomogr 2022; 46:638-644. [PMID: 35405722 DOI: 10.1097/rct.0000000000001319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study aimed to investigate the imaging features and prognosis of spinal solitary fibrous tumors (SFTs) of different pathological grades. METHODS The clinical features, computed tomography and magnetic resonance (MR) images, and follow-up data of 23 patients with SFTs were reviewed. The patients were divided into 3 groups according to their pathological manifestations: grade 1 (n = 3), grade 2 (n = 14), and grade 3 (n = 6). The following imaging features were recorded: location, computed tomography density/MR intensity, enhancement pattern, dural tail sign, adjacent bone remodeling, lobulation, and tumor size. The immunohistochemical (Ki-67/MIB-1) levels were also investigated. All parameters were statistically analyzed between grade 2 and 3 tumors. RESULTS The Ki-67/MIB-1 index was markedly higher in grade 3 tumors than in grade 2 tumors (P < 0.001). All grade 1 lesions appeared hypointense on T2-weighted image, whereas grade 2 and 3 lesions appeared isointense or mildly hyperintense. There were significant differences in enhancement type and osteolytic bony destruction between grade 2 and 3 tumors (P < 0.05). However, no marked differences were found in the distribution of age, sex, location, MR signal, degree of enhancement, compressive bony absorption, dural tail sign, or maximum vertical/traverse diameter ratio. Malignant progression occurred less frequently in patients with grade 2 tumors than in those with grade 3 tumors, but the difference was not statistically significant. CONCLUSIONS Different grades of spinal SFTs have different degrees of proliferation and imaging features, especially grade 3 tumors, which show a heterogeneous enhancement pattern, osteolytic bony destruction, and a higher possibility of recurrence and metastasis.
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16
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Ayana G, Park J, Jeong JW, Choe SW. A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification. Diagnostics (Basel) 2022; 12:135. [PMID: 35054303 PMCID: PMC8775102 DOI: 10.3390/diagnostics12010135] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/24/2021] [Accepted: 12/30/2021] [Indexed: 12/31/2022] Open
Abstract
Breast cancer diagnosis is one of the many areas that has taken advantage of artificial intelligence to achieve better performance, despite the fact that the availability of a large medical image dataset remains a challenge. Transfer learning (TL) is a phenomenon that enables deep learning algorithms to overcome the issue of shortage of training data in constructing an efficient model by transferring knowledge from a given source task to a target task. However, in most cases, ImageNet (natural images) pre-trained models that do not include medical images, are utilized for transfer learning to medical images. Considering the utilization of microscopic cancer cell line images that can be acquired in large amount, we argue that learning from both natural and medical datasets improves performance in ultrasound breast cancer image classification. The proposed multistage transfer learning (MSTL) algorithm was implemented using three pre-trained models: EfficientNetB2, InceptionV3, and ResNet50 with three optimizers: Adam, Adagrad, and stochastic gradient de-scent (SGD). Dataset sizes of 20,400 cancer cell images, 200 ultrasound images from Mendeley and 400 ultrasound images from the MT-Small-Dataset were used. ResNet50-Adagrad-based MSTL achieved a test accuracy of 99 ± 0.612% on the Mendeley dataset and 98.7 ± 1.1% on the MT-Small-Dataset, averaging over 5-fold cross validation. A p-value of 0.01191 was achieved when comparing MSTL against ImageNet based TL for the Mendeley dataset. The result is a significant improvement in the performance of artificial intelligence methods for ultrasound breast cancer classification compared to state-of-the-art methods and could remarkably improve the early diagnosis of breast cancer in young women.
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Affiliation(s)
- Gelan Ayana
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea
| | - Jinhyung Park
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea
| | - Jin-Woo Jeong
- Department of Data Science, Seoul National University of Science and Technology, Seoul 01811, Korea
| | - Se-Woon Choe
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea
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17
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Karakaya E, Erkent M, Turnaoğlu H, Şirinoğlu T, Akdur A, Kavasoğlu L. The effect of the use of the Gail model on breast cancer diagnosis in BIRADs 4a cases. Turk J Surg 2021; 37:394-399. [DOI: 10.47717/turkjsurg.2021.5436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 11/16/2021] [Indexed: 11/23/2022]
Abstract
Objective: The BI-RADS classification system and the Gail Model are the scoring systems that contribute to the diagnosis of breast cancer. The aim of the study was to determine the contribution of Gail Model to the diagnosis of breast lesions that were radiologically categorized as BI-RADS 4A.
Material and Methods: We retrospectively examined the medical records of 320 patients between January 2011 and December 2020 whose lesions had been categorized as BI-RADS 4A. Radiological parameters of breast lesions and clinical parameters according to the Gail Model were collected. The relationship between malignant BI-RADS 4A lesions and radiological and clinical parameters was evaluated. In addition, the effect of the Gail Model on diagnosis in malignant BI-RADS 4A lesions was evaluated.
Results: Among radiological features, there were significant differences between lesion size, contour, microcalcification content, echogenicity, and presence of ectasia with respect to the pathological diagnosis (p< 0.05). No significant difference was found between the lesions’ pathological diagnosis and the patients’ Gail score (p> 0.05). An analysis of the features of the Gail model revealed that there was no significant difference between the age of menarche, age at first live birth, presence of a first-degree relative with breast cancer, and a history of breast biopsy and the pathological diagnosis (p> 0.05).
Conclusion: As a conclusion Gail Model does not contribute to the diagnosis of BC, especially in patients with BI-RADS 4A lesions.
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18
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Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021; 142:109882. [PMID: 34392105 PMCID: PMC8387447 DOI: 10.1016/j.ejrad.2021.109882] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 12/22/2022]
Abstract
Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.
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Affiliation(s)
- Almir Bitencourt
- Department of Imaging, A.C.Camargo Cancer Center, Sao Paulo, SP, Brazil; Dasa, Sao Paulo, SP, Brazil
| | - Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Guy's and St. Thomas' NHS Trust, Great Maze Pond, London, UK
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Niu S, Huang J, Li J, Liu X, Wang D, Wang Y, Shen H, Qi M, Xiao Y, Guan M, Li D, Liu F, Wang X, Xiong Y, Gao S, Wang X, Yu P, Zhu J. Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data. Quant Imaging Med Surg 2021; 11:2052-2061. [PMID: 33936986 DOI: 10.21037/qims-20-919] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background It is challenging to differentiate between phyllodes tumors (PTs) and fibroadenomas (FAs). Artificial intelligence (AI) can provide quantitative information regarding the morphology and textural features of lesions. This study attempted to use AI to evaluate the ultrasonic images of PTs and FAs and to explore the diagnostic performance of AI features in the differential diagnosis of PTs and FAs. Methods A total of 40 PTs and 290 FAs <5 cm in maximum diameter found in female patients were retrospectively analyzed. All tumors were segmented by doctors, and the features of the lesions were collated, including circularity, height-to-width ratio, margin spicules, margin coarseness (MC), margin indistinctness, margin lobulation (ML), internal calcification, angle between the long axis of the lesion and skin, energy, grey entropy, and grey mean. The differences between PTs and FAs were analyzed, and the diagnostic performance of AI features in the differential diagnosis of PTs and FAs was evaluated. Results Statistically significant differences (P<0.05) were found in the height-to-width ratio, ML, energy, and grey entropy between the PTs and FAs. Receiver operating characteristic (ROC) curve analysis of single features showed that the area under the curve [(AUC) 0.759] of grey entropy was the largest among the four features with statistically significant differences, and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.925, 0.459, 0.978, and 0.190, respectively. When considering the combinations of the features, the combination of height-to-width ratio, margin indistinctness, ML, energy, grey entropy, and internal calcification was the most optimal of the combinations of features with an AUC of 0.868, and a sensitivity, specificity, PPV, and NPV of 0.734, 0.900, 0.982, and 0.316, respectively. Conclusions Quantitative analysis of AI can identify subtle differences in the morphology and textural features between small PTs and FAs. Comprehensive consideration of multiple features is important for the differential diagnosis of PTs and FAs.
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Affiliation(s)
- Sihua Niu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Jianhua Huang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jia Li
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Xueling Liu
- Department of Ultrasound, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning, China
| | - Dan Wang
- Department of Ultrasound, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning, China
| | - Yingyan Wang
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Huiming Shen
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Min Qi
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Yi Xiao
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Mengyao Guan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Diancheng Li
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Feifei Liu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Xiuming Wang
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Yu Xiong
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Siqi Gao
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Xue Wang
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Ping Yu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Jia'an Zhu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
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Jubair F, Al-Karadsheh O, Malamos D, Al Mahdi S, Saad Y, Hassona Y. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 2021; 28:1123-1130. [PMID: 33636041 DOI: 10.1111/odi.13825] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/30/2021] [Accepted: 02/06/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or potentially malignant using standard real-time clinical images. METHODS A small deep CNN, that uses a pretrained EfficientNet-B0 as a lightweight transfer learning model, was proposed. A data set of 716 clinical images was used to train and test the proposed model. Accuracy, specificity, sensitivity, receiver operating characteristics (ROC) and area under curve (AUC) were used to evaluate performance. Bootstrapping with 120 repetitions was used to calculate arithmetic means and 95% confidence intervals (CIs). RESULTS The proposed CNN model achieved an accuracy of 85.0% (95% CI: 81.0%-90.0%), a specificity of 84.5% (95% CI: 78.9%-91.5%), a sensitivity of 86.7% (95% CI: 80.4%-93.3%) and an AUC of 0.928 (95% CI: 0.88-0.96). CONCLUSIONS Deep CNNs can be an effective method to build low-budget embedded vision devices with limited computation power and memory capacity for diagnosis of oral cancer. Artificial intelligence (AI) can improve the quality and reach of oral cancer screening and early detection.
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Affiliation(s)
- Fahed Jubair
- Computer Engineering Department, School of Engineering, The University of Jordan, Amman, Jordan
| | - Omar Al-Karadsheh
- Department of Oral and Maxillofacial Surgery, Oral Medicine, and Periodontics, School of Dentistry, The University of Jordan, Amman, Jordan
| | - Dimitrios Malamos
- Oral Medicine Clinic, 1st Regional Health District of Attica, National Organization for the Provision of Health Services, Athens, Greece
| | - Samara Al Mahdi
- Department of Oral and Maxillofacial Surgery, Oral Medicine, and Periodontics, School of Dentistry, The University of Jordan, Amman, Jordan
| | - Yusser Saad
- Department of Oral and Maxillofacial Surgery, Oral Medicine, and Periodontics, School of Dentistry, The University of Jordan, Amman, Jordan
| | - Yazan Hassona
- Department of Oral and Maxillofacial Surgery, Oral Medicine, and Periodontics, School of Dentistry, The University of Jordan, Amman, Jordan
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