1
|
Du Y, Ma J, Wu T, Li F, Pan J, Du L, Zhang M, Diao X, Wu R. Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study. Br J Radiol 2024; 97:1653-1660. [PMID: 39102827 PMCID: PMC11417380 DOI: 10.1093/bjr/tqae136] [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: 11/13/2023] [Revised: 07/23/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024] Open
Abstract
OBJECTIVE To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a-c categories and avoid unnecessary biopsies. METHODS This prospective, multicentre study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 and 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures. RESULTS Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off <2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group. CONCLUSION Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%. ADVANCES IN KNOWLEDGE Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort.
Collapse
Affiliation(s)
- Yu Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, China
| | - Ji Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, China
| | - Tingting Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, China
| | - Fang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, China
| | - Jiazhen Pan
- Department of Ultrasound, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer, Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Liwen Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Manqi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xuehong Diao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200080, China
| |
Collapse
|
2
|
He H, Liu Y, Zhou X, Zhan J, Wang C, Shen Y, Chen H, Chen L, Zhang Q. Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images? Med Biol Eng Comput 2024:10.1007/s11517-024-03188-8. [PMID: 39215783 DOI: 10.1007/s11517-024-03188-8] [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: 05/14/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
Deep learning has been widely used in ultrasound image analysis, and it also benefits kidney ultrasound interpretation and diagnosis. However, the importance of ultrasound image resolution often goes overlooked within deep learning methodologies. In this study, we integrate the ultrasound image resolution into a convolutional neural network and explore the effect of the resolution on diagnosis of kidney tumors. In the process of integrating the image resolution information, we propose two different approaches to narrow the semantic gap between the features extracted by the neural network and the resolution features. In the first approach, the resolution is directly concatenated with the features extracted by the neural network. In the second approach, the features extracted by the neural network are first dimensionally reduced and then combined with the resolution features to form new composite features. We compare these two approaches incorporating the resolution with the method without incorporating the resolution on a kidney tumor dataset of 926 images consisting of 211 images of benign kidney tumors and 715 images of malignant kidney tumors. The area under the receiver operating characteristic curve (AUC) of the method without incorporating the resolution is 0.8665, and the AUCs of the two approaches incorporating the resolution are 0.8926 (P < 0.0001) and 0.9135 (P < 0.0001) respectively. This study has established end-to-end kidney tumor classification systems and has demonstrated the benefits of integrating image resolution, showing that incorporating image resolution into neural networks can more accurately distinguish between malignant and benign kidney tumors in ultrasound images.
Collapse
Affiliation(s)
- Haihao He
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China
| | - Yuhan Liu
- Department of Ultrasound, Huadong Hospital, Fudan University, 211 West Yan'an Road, Shanghai, 200040, China
| | - Xin Zhou
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China
| | - Jia Zhan
- Department of Ultrasound, Huadong Hospital, Fudan University, 211 West Yan'an Road, Shanghai, 200040, China
| | - Changyan Wang
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China
| | - Yiwen Shen
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China
| | - Haobo Chen
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China
| | - Lin Chen
- Department of Ultrasound, Huadong Hospital, Fudan University, 211 West Yan'an Road, Shanghai, 200040, China.
| | - Qi Zhang
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
| |
Collapse
|
3
|
Pesapane F, Gnocchi G, Quarrella C, Sorce A, Nicosia L, Mariano L, Bozzini AC, Marinucci I, Priolo F, Abbate F, Carrafiello G, Cassano E. Errors in Radiology: A Standard Review. J Clin Med 2024; 13:4306. [PMID: 39124573 PMCID: PMC11312890 DOI: 10.3390/jcm13154306] [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: 04/21/2024] [Revised: 07/08/2024] [Accepted: 07/15/2024] [Indexed: 08/12/2024] Open
Abstract
Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy.
Collapse
Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Giulia Gnocchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; (G.G.); (C.Q.); (A.S.); (G.C.)
| | - Cettina Quarrella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; (G.G.); (C.Q.); (A.S.); (G.C.)
| | - Adriana Sorce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; (G.G.); (C.Q.); (A.S.); (G.C.)
| | - Luca Nicosia
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Luciano Mariano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Anna Carla Bozzini
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Irene Marinucci
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Francesca Priolo
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Francesca Abbate
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; (G.G.); (C.Q.); (A.S.); (G.C.)
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.N.); (L.M.); (A.C.B.); (I.M.); (F.P.); (F.A.); (E.C.)
| |
Collapse
|
4
|
Bartolotta TV, Militello C, Prinzi F, Ferraro F, Rundo L, Zarcaro C, Dimarco M, Orlando AAM, Matranga D, Vitabile S. Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study. LA RADIOLOGIA MEDICA 2024; 129:977-988. [PMID: 38724697 PMCID: PMC11252191 DOI: 10.1007/s11547-024-01826-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 04/29/2024] [Indexed: 07/17/2024]
Abstract
PURPOSE To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs). MATERIAL AND METHODS Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B). RESULTS A total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively. CONCLUSION AI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers.
Collapse
Affiliation(s)
- Tommaso Vincenzo Bartolotta
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
| | - Carmelo Militello
- Institute for High-Performance Computing and Networking (ICAR-CNR), Italian National Research Council, Palermo, Italy
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Fabiola Ferraro
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA, Italy
| | - Calogero Zarcaro
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | | | | | - Domenica Matranga
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| |
Collapse
|
5
|
Gómez-Flores W, Gregorio-Calas MJ, Coelho de Albuquerque Pereira W. BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems. Med Phys 2024; 51:3110-3123. [PMID: 37937827 DOI: 10.1002/mp.16812] [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: 03/15/2023] [Revised: 10/10/2023] [Accepted: 10/12/2023] [Indexed: 11/09/2023] Open
Abstract
PURPOSE Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI-RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above-mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems. ACQUISITION AND VALIDATION METHODS The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy-proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI-RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5- and 10-fold cross-validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions. DATA FORMAT AND USAGE NOTES The BUS dataset is publicly available for academic and research purposes through an open-access repository under the name BUS-BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma-Separated Value (CSV) files. POTENTIAL APPLICATIONS The BUS-BRA dataset can be used to develop and assess artificial intelligence-based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI-RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.
Collapse
Affiliation(s)
- Wilfrido Gómez-Flores
- Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Tamaulipas, Mexico
| | | | | |
Collapse
|
6
|
Kwon MR, Youn I, Lee MY, Lee HA. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound. Acad Radiol 2024; 31:480-491. [PMID: 37813703 DOI: 10.1016/j.acra.2023.09.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/25/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS). MATERIALS AND METHODS ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared. RESULTS For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing. CONCLUSION AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.
Collapse
Affiliation(s)
- Mi-Ri Kwon
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.)
| | - Inyoung Youn
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.).
| | - Mi Yeon Lee
- Division of Biostatistics, Department of R&D Management, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.Y.L.)
| | - Hyun-Ah Lee
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.)
| |
Collapse
|
7
|
He P, Chen W, Bai MY, Li J, Wang QQ, Fan LH, Zheng J, Liu CT, Zhang XR, Yuan XR, Song PJ, Cui LG. Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions. Heliyon 2024; 10:e24560. [PMID: 38304808 PMCID: PMC10831749 DOI: 10.1016/j.heliyon.2024.e24560] [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: 09/08/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/03/2024] Open
Abstract
Purpose To evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions. Materials and methods 148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated. Results In this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5-6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1-34.3) in the high-risk group with a "possibly malignant" CAD result (p = 0.017). With a "possibly benign" CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0-2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7-89.6), 80.0 % (95 % CI: 73.6-86.4), 87.4 % (95 % CI: 82.0-92.7), 87.2 % (95 % CI: 81.8-92.6), 18.2 % (95 % CI: 2.1-34.3) and 99.2 % (95 % CI: 97.8-100.0), respectively. Conclusions CAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.
Collapse
Affiliation(s)
- Ping He
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Ming-Yu Bai
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Medical College of Shihezi University, 107 North Second Rd., Shihezi, 832008, Xinjiang, China
| | - Qing-Qing Wang
- Department of Breast Ultrasonography, Center for Diagnosis and Treatment of Breast Diseases, Yili Maternity and Child Health Hospital, Sichuan Road, Economic Cooperation Zone, Yili Kazakh Autonomous Prefecture, Xinjiang Uyghur Autonomous Region, China
| | - Li-Hong Fan
- Department of Ultrasound, Jinzhong First People's Hospital, 689 South Huitong Rd. Yuci District 030600, Jinzhong City, Shanxi Province, China
| | - Jian Zheng
- Ultrasound Department of the Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, 518172, China
| | - Chun-Tao Liu
- Department of Ultrasound, Liaocheng Dongchangfu District Maternal and Child Care Service Center, 129 Zhenxing West Rd., Liaocheng, 252000, Shandong, China
| | - Xiao-Rong Zhang
- Department of Ultrasound, Beijing HaiDian Hospital, 29 Zhongguanchun Rd., Beijing, 100080, China
| | - Xi-Rong Yuan
- Department of Ultrasound, The Second People's Hospital of Zhangqiu District, Jinan, Shandong, Ji Nan Zhang Qiu, 250200, China
| | - Peng-Jie Song
- Department of Ultrasound, Port Hospital of Hebei Port Group Co. LTD, 57 Dongshan Street, Haigang District, Qinhuangdao City, Hebei Province, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| |
Collapse
|
8
|
Dan Q, Xu Z, Burrows H, Bissram J, Stringer JSA, Li Y. Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review. NPJ Precis Oncol 2024; 8:21. [PMID: 38280946 PMCID: PMC10821881 DOI: 10.1038/s41698-024-00514-z] [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: 07/27/2023] [Accepted: 12/08/2023] [Indexed: 01/29/2024] Open
Abstract
Deep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.
Collapse
Affiliation(s)
- Qing Dan
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
- Global Women's Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Ziting Xu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Hannah Burrows
- Health Sciences Library, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jennifer Bissram
- Health Sciences Library, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jeffrey S A Stringer
- Global Women's Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Yingjia Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.
| |
Collapse
|
9
|
He P, Chen W, Bai MY, Li J, Wang QQ, Fan LH, Zheng J, Liu CT, Zhang XR, Yuan XR, Song PJ, Cui LG. Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study. World J Surg 2023; 47:3205-3213. [PMID: 37805926 DOI: 10.1007/s00268-023-07207-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2023] [Indexed: 10/10/2023]
Abstract
OBJECTIVES Ultrasound tends to present very high sensitivity but relatively low specificity and positive predictive value (PPV), which would result in unnecessary breast biopsies. The purpose of this study is to analyze the diagnostic performance of computer-aided diagnosis (CAD) (S-Detect) system in differentiating breast lesions and reducing unnecessary biopsies in non-university hospitals in less-developed regions of China. METHODS The study was a prospective multicenter study from 8 hospitals. The ultrasound images, and cine, CAD analysis, and BI-RADS were recorded. The accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the curve (AUC) were analyzed and compared between CAD and radiologists. The Youden Index (YI) was used to determine optimal cut-off for the number of planes to downgrade. RESULTS A total of 491 breast lesions were included in the study. Less-experienced radiologists combined CAD was superior to less-experienced radiologists alone in AUC (0.878 vs 0.712, p < 0.001), and specificity (81.3% vs 44.6%, p < 0.001). There was no statistical difference in AUC (0.891 vs 0.878, p = 0.346), and specificity (82.3% vs 81.3%, p = 0.791) between experienced radiologists and less-experienced radiologists combined CAD. With CAD assistance, the biopsy rate of less-experienced radiologists was significantly decreased (100.0% vs 25.6%, p < 0.001), and malignant rate of biopsy was significantly increased (15.0% vs 43.9%, p < 0.001). CONCLUSIONS CAD system can be an effective auxiliary tool in differentiating breast lesions and reducing unnecessary biopsies for radiologists from non-university hospitals in less-developed regions of China.
Collapse
Affiliation(s)
- Ping He
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Ming-Yu Bai
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Medical College of Shihezi University, 107 North Second Rd., Shihezi, 832008, Xinjiang, China
| | - Qing-Qing Wang
- Department of Breast Ultrasonography, Center for Diagnosis and Treatment of Breast Diseases, Yili Maternity and Child Health Hospital, Sichuan Road, Economic Cooperation Zone, Yili Kazakh Autonomous Prefecture, Xinjiang Uyghur Autonomous Region, Yili, China
| | - Li-Hong Fan
- Department of Ultrasound, Jinzhong First People's Hospital, 689 South Huitong Rd., Yuci District, Jinzhong, 030600, Shanxi, China
| | - Jian Zheng
- Ultrasound Department of the Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, 518172, China
| | - Chun-Tao Liu
- Department of Ultrasound, Liaocheng Dongchangfu District Maternal and Child Care Service Center, 129 Zhenxing West Rd., Liaocheng, 252000, Shandong, China
| | - Xiao-Rong Zhang
- Department of Ultrasound, Beijing Haidian Hospital, 29 Zhongguanchun Rd., Beijing, 100080, China
| | - Xi-Rong Yuan
- Department of Ultrasound, The Second People's Hospital of Zhangqiu District, Jinan, 250200, Shandong, China
| | - Peng-Jie Song
- Department of Ultrasound, Port Hospital of Hebei Port Group Co. Ltd., 57 Dongshan Street, Haigang District, Qinhuangdao, Hebei, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China.
| |
Collapse
|
10
|
Gómez-Flores W, Pereira WCDA. Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks. Med Biol Eng Comput 2023; 61:3193-3207. [PMID: 37713158 DOI: 10.1007/s11517-023-02928-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/29/2023] [Indexed: 09/16/2023]
Abstract
Breast ultrasound (BUS) image classification in benign and malignant classes is often based on pre-trained convolutional neural networks (CNNs) to cope with small-sized training data. Nevertheless, BUS images are single-channel gray-level images, whereas pre-trained CNNs learned from color images with red, green, and blue (RGB) components. Thus, a gray-to-color conversion method is applied to fit the BUS image to the CNN's input layer size. This paper evaluates 13 gray-to-color conversion methods proposed in the literature that follow three strategies: replicating the gray-level image to all RGB channels, decomposing the image to enhance inherent information like the lesion's texture and morphology, and learning a matching layer. Besides, we introduce an image decomposition method based on the lesion's structural information to describe its inner and outer complexity. These gray-to-color conversion methods are evaluated under the same experimental framework using a pre-trained CNN architecture named ResNet-18 and a BUS dataset with more than 3000 images. In addition, the Matthews correlation coefficient (MCC), sensitivity (SEN), and specificity (SPE) measure the classification performance. The experimental results show that decomposition methods outperform replication and learning-based methods when using information from the lesion's binary mask (obtained from a segmentation method), reaching an MCC value greater than 0.70 and specificity up to 0.92, although the sensitivity is about 0.80. On the other hand, regarding the proposed method, the trade-off between sensitivity and specificity is better balanced, obtaining about 0.88 for both indices and an MCC of 0.73. This study contributes to the objective assessment of different gray-to-color conversion approaches in classifying breast lesions, revealing that mask-based decomposition methods improve classification performance. Besides, the proposed method based on structural information improves the sensitivity, obtaining more reliable classification results on malignant cases and potentially benefiting clinical practice.
Collapse
Affiliation(s)
- Wilfrido Gómez-Flores
- Centro de Investigación y de Estudios Avanzados del IPN, Unidad Tamaulipas, Ciudad Victoria, 87138, Tamaulipas, Mexico.
| | | |
Collapse
|
11
|
He P, Chen W, Bai MY, Li J, Wang QQ, Fan LH, Zheng J, Liu CT, Zhang XR, Yuan XR, Song PJ, Cui LG. Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise. AJR Am J Roentgenol 2023; 221:450-459. [PMID: 37222275 DOI: 10.2214/ajr.23.29328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
BACKGROUND. Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. OBJECTIVE. The purpose of this study was to evaluate the usefulness of deep learning-based CAD software on the diagnostic performance of radiologists without breast ultrasound expertise at secondary or rural hospitals in the differentiation of benign and malignant breast lesions measuring up to 2.0 cm on ultrasound. METHODS. This prospective study included patients scheduled to undergo biopsy or surgical resection at any of eight participating secondary or rural hospitals in China of a breast lesion classified as BI-RADS category 3-5 on prior breast ultrasound from November 2021 to September 2022. Patients underwent an additional investigational breast ultrasound, performed and interpreted by a radiologist without breast ultrasound expertise (hybrid body/breast radiologists, either who lacked breast imaging subspecialty training or for whom the number of breast ultrasounds performed annually accounted for less than 10% of all ultrasounds performed annually by the radiologist), who assigned a BI-RADS category. CAD results were used to upgrade reader-assigned BI-RADS category 3 lesions to category 4A and to downgrade reader-assigned BI-RADS category 4A lesions to category 3. Histologic results of biopsy or resection served as the reference standard. RESULTS. The study included 313 patients (mean age, 47.0 ± 14.0 years) with 313 breast lesions (102 malignant, 211 benign). Of BI-RADS category 3 lesions, 6.0% (6/100) were upgraded by CAD to category 4A, of which 16.7% (1/6) were malignant. Of category 4A lesions, 79.1% (87/110) were downgraded by CAD to category 3, of which 4.6% (4/87) were malignant. Diagnostic performance was significantly better after application of CAD, in comparison with before application of CAD, in terms of accuracy (86.6% vs 62.6%, p < .001), specificity (82.9% vs 46.0%, p < .001), and PPV (72.7% vs 46.5%, p < .001) but not significantly different in terms of sensitivity (94.1% vs 97.1%, p = .38) or NPV (96.7% vs 97.0%, p > .99). CONCLUSION. CAD significantly improved radiologists' diagnostic performance, showing particular potential to reduce the frequency of benign breast biopsies. CLINICAL IMPACT. The findings indicate the ability of CAD to improve patient care in settings with incomplete access to breast imaging expertise.
Collapse
Affiliation(s)
- Ping He
- Department of Ultrasound, Peking University Third Hospital, 49 N Garden Rd, Beijing 100191, China
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, 49 N Garden Rd, Beijing 100191, China
| | - Ming-Yu Bai
- Department of Ultrasound, Peking University Third Hospital, 49 N Garden Rd, Beijing 100191, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Medical College of Shihezi University, Xinjiang, China
| | - Qing-Qing Wang
- Department of Breast Sonography, Center for Diagnosis and Treatment of Breast Diseases, Yili Maternity and Child Health Hospital, Xinjiang, China
| | - Li-Hong Fan
- Department of Ultrasound, Jinzhong First People's Hospital, Jinzhong City, China
| | - Jian Zheng
- Ultrasound Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China
| | - Chun-Tao Liu
- Department of Ultrasound, Liaocheng Dongchangfu District Maternal and Child Care Service Center, Shandong, China
| | - Xiao-Rong Zhang
- Department of Ultrasound, Beijing HaiDian Hospital, Beijing, China
| | - Xi-Rong Yuan
- Department of Ultrasound, The Second People's Hospital of Zhangqiu District, Jinan, China
| | - Peng-Jie Song
- Department of Ultrasound, Port Hospital of Hebei Port Group Co. LTD, Qinhuangdao City, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, 49 N Garden Rd, Beijing 100191, China
| |
Collapse
|
12
|
Nissan N, Massasa EEM, Bauer E, Halshtok-Neiman O, Shalmon A, Gotlieb M, Faermann R, Samoocha D, Yagil Y, Ziv-Baran T, Anaby D, Sklair-Levy M. MRI can accurately diagnose breast cancer during lactation. Eur Radiol 2023; 33:2935-2944. [PMID: 36348090 DOI: 10.1007/s00330-022-09234-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/27/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE To test the diagnostic performance of breast dynamic contrast-enhanced (DCE) MRI during lactation. MATERIALS AND METHODS Datasets of 198 lactating patients, including 66 pregnancy-associated breast cancer (PABC) patients and 132 controls, who were scanned by DCE on 1.5-T MRI, were retrospectively evaluated. Six blinded, expert radiologists independently read a single DCE maximal intensity projection (MIP) image for each case and were asked to determine whether malignancy was suspected and the background-parenchymal-enhancement (BPE) grade. Likewise, computer-aided diagnosis CAD MIP images were independently read by the readers. Contrast-to-noise ratio (CNR) analysis was measured and compared among four consecutive acquisitions of DCE subtraction images. RESULTS For MIP-DCE images, the readers achieved the following means: sensitivity 93.3%, specificity 80.3%, positive-predictive-value 70.4, negative-predictive-value 96.2, and diagnostic accuracy of 84.6%, with a substantial inter-rater agreement (Kappa = 0.673, p value < 0.001). Most false-positive interpretations were attributed to either the MIP presentation, an underlying benign lesion, or an asymmetric appearance due to prior treatments. CAD's derived diagnostic accuracy was similar (p = 0.41). BPE grades were significantly increased in the healthy controls compared to the PABC cohort (p < 0.001). CNR significantly decreased by 11-13% in each of the four post-contrast images (p < 0.001). CONCLUSION Breast DCE MRI maintains its high efficiency among the lactating population, probably due to a vascular-steal phenomenon, which causes a significant reduction of BPE in cancer cases. Upon validation by prospective, multicenter trials, this study could open up the opportunity for breast MRI to be indicated in the screening and diagnosis of lactating patients, with the aim of facilitating an earlier diagnosis of PABC. KEY POINTS • A single DCE MIP image was sufficient to reach a mean sensitivity of 93.3% and NPV of 96.2%, to stress the high efficiency of breast MRI during lactation. • Reduction in BPE among PABC patients compared to the lactating controls suggests that several factors, including a possible vascular steal phenomenon, may affect cancer patients. • Reduction in CNR along four consecutive post-contrast acquisitions highlights the differences in breast carcinoma and BPE kinetics and explains the sufficient conspicuity on the first subtracted image.
Collapse
Affiliation(s)
- Noam Nissan
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Efi Efraim Moss Massasa
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
| | - Ethan Bauer
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Osnat Halshtok-Neiman
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anat Shalmon
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michael Gotlieb
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Renata Faermann
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - David Samoocha
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yael Yagil
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Tomer Ziv-Baran
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Debbie Anaby
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Miri Sklair-Levy
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
13
|
Wang Y, Tang L, Chen P, Chen M. The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies. Clin Breast Cancer 2023; 23:e112-e121. [PMID: 36653206 DOI: 10.1016/j.clbc.2022.12.016] [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: 09/18/2022] [Revised: 11/27/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Ultrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system and ultrasound elastography with conventional ultrasound (US) in increasing specificity and reducing unnecessary breast lesions biopsies. MATERIALS AND METHODS Conventional US, CAD system, and strain elastography (SE) were retrospectively performed on 216 breast lesions before biopsy or surgery. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and biopsy rate were compared between conventional US and the combination of conventional US, SE, and CAD system. RESULTS Of 216 lesions, 54 were malignant and 162 were benign. The addition of CAD system and SE to conventional US increased the AUC from 0.716 to 0.910 and specificity from 46.9% to 85.8% without a loss in sensitivity while 89.2% (66 of 74) of benign lesions in Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions would avoid unnecessary biopsies. CONCLUSION The addition of CAD system and SE to conventional US improved specificity and AUC without loss of sensitivity, and reduced unnecessary biopsies.
Collapse
Affiliation(s)
- Yuqun Wang
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai China
| | - Lei Tang
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai China
| | - Pingping Chen
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai China
| | - Man Chen
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai China.
| |
Collapse
|
14
|
Gu Y, Xu W, Liu Y, An X, Li J, Cong L, Zhu L, He X, Wang H, Jiang Y. The feasibility of a novel computer-aided classification system for the characterisation and diagnosis of breast masses on ultrasound: a single-centre preliminary test study. Clin Radiol 2023:S0009-9260(23)00130-7. [PMID: 37069025 DOI: 10.1016/j.crad.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/07/2023] [Accepted: 03/15/2023] [Indexed: 04/19/2023]
Abstract
AIM To introduce a novel computer-aided classification (CAC) system and investigate the feasibility of characterising and diagnosing breast masses on ultrasound (US). MATERIALS AND METHODS A total of 246 breast masses were included. US features and the final assessment categories of the breast masses were analysed by a radiologist and the CAC system according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CAC system evaluated the BI-RADS assessment from the fusion of multi-view and colour Doppler US images without (SmartBreast) or with combining clinical variables (m-CAC system). The diagnostic performance and agreement of US characteristics between the radiologist and the CAC system were compared. RESULTS The agreement between the radiologist and the CAC system was substantial for mass shape (κ = 0.673), orientation (κ = 0.682), margin (κ = 0.622), posterior features (κ = 0.629), calcifications in a mass (κ = 0.709) and vascularity (κ = 0.745), fair for echo pattern (κ = 0.379), and moderate for BI-RADS assessment (κ = 0.575). With BI-RADS 4a as the cut-off value, the specificity (52.5% versus 25%, p<0.0001) and accuracy (73.98% versus 62.6%, p=0.0002) of the m-CAC system were improved without significant loss of sensitivity (94.44% versus 98.41%, p=0.1250) compared with the SmartBreast. The m-CAC system showed similar specificity (52.5% versus 45.83%, p=0.2430) and accuracy (73.98% versus 73.58%, p=1.0000) as the radiologist, but a lower sensitivity (94.44% versus 100%, p=0.0156). CONCLUSION The CAC system showed an acceptable agreement with the radiologist for characterisation of breast lesions. It has the potential to mimic the decision-making behaviour of radiologists for the classification of breast lesions.
Collapse
Affiliation(s)
- Y Gu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China
| | - W Xu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China
| | - Y Liu
- Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Beijing, China
| | - X An
- Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Beijing, China
| | - J Li
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China
| | - L Cong
- Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Beijing, China
| | - L Zhu
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, China
| | - X He
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, China
| | - H Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.
| | - Y Jiang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.
| |
Collapse
|
15
|
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.
Collapse
|
16
|
Li Y, Ren W, Wang X, Xiao Y, Feng Y, Shi P, Sun L, Wang X, Yang H, Song G. The diagnostic accuracy of contrast echocardiography in patients with suspected cardiac masses: A preliminary multicenter, cross-sectional study. Front Cardiovasc Med 2022; 9:1011560. [PMID: 36187014 PMCID: PMC9523017 DOI: 10.3389/fcvm.2022.1011560] [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: 08/04/2022] [Accepted: 08/29/2022] [Indexed: 12/04/2022] Open
Abstract
Background To evaluate the diagnostic accuracy of contrast echocardiography (CE) in patients with suspected cardiac masses. Methods A multicenter, prospective study involving 108 consecutive patients with suspected cardiac masses based on transthoracic echocardiography performed between November 2019 and December 2020 was carried out. CE examinations were performed in all patients. The echocardiographic diagnosis was established according to the qualitative (echogenicity, boundary, morphology of the base, mass perfusion, pericardial effusion, and motility) and quantitative (area of the masses and peak intensity ratio of the masses and adjacent myocardium A1/A2) evaluations. Results Final confirmed diagnoses were as follows: no cardiac mass (n = 3), pseudomass (n = 3), thrombus (n = 36), benign tumor (n = 30), and malignant tumor (n = 36). ROC analysis revealed the optimal A1/A2 with cutoff value of 0.295 for a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.958 (95% confidence interval (CI): 0.899–0.988), 100, 91.7, 95.7, and 100%, respectively. CE was able to distinguish malignant from benign tumors with an AUC of 0.953 (95% CI: 0.870–0.990). Multivariate logistic regression analysis revealed that tumor area, base, and A1/A2 were associated with the risk of malignant tumor (OR = 1.003, 95% CI: 1.00003–1.005; OR = 22.64, 95% CI: 1.30–395.21; OR = 165.39, 95% CI: 4.68–5,850.94, respectively). When using A1/A2 > 1.28 as the only diagnostic criterion to identify the malignant tumor, AUC, sensitivity, specificity, PPV, and NPV were 0.886 (95% CI: 0.784–0.951), 80.6, 96.7, 96.7, and 80.7%, respectively. Conclusion CE has the potential to accurately differentiate cardiac masses by combining qualitative and quantitative analyses. However, more studies with a large sample size should be conducted to further confirm these findings. Clinical trial registration http://www.chictr.org.cn/, identifier: ChiCTR1900026809.
Collapse
Affiliation(s)
- Ying Li
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Weidong Ren
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xin Wang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yangjie Xiao
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yueqin Feng
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, China
| | - Pengli Shi
- Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Lijuan Sun
- Department of Ultrasound, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Xiao Wang
- Department of Ultrasound, Anshan Central Hospital, Anshan, China
| | - Huan Yang
- Department of Ultrasound, Yingkou Central Hospital, Yingkou, China
| | - Guang Song
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
- *Correspondence: Guang Song
| |
Collapse
|