1
|
Zhang P, Zhang M, Lu M, Jin C, Wang G, Lin X. Comparative Analysis of the Diagnostic Value of S-Detect Technology in Different Planes Versus the BI-RADS Classification for Breast Lesions. Acad Radiol 2024:S1076-6332(24)00568-3. [PMID: 39138111 DOI: 10.1016/j.acra.2024.08.005] [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: 04/15/2024] [Revised: 07/28/2024] [Accepted: 08/02/2024] [Indexed: 08/15/2024]
Abstract
RATIONALE AND OBJECTIVES S-Detect, a deep learning-based Computer-Aided Detection system, is recognized as an important tool for diagnosing breast lesions using ultrasound imaging. However, it may exhibit inconsistent findings across multiple imaging planes. This study aims to evaluate the diagnostic performance of S-Detect in different planes and identify factors contributing to these inconsistencies. MATERIALS AND METHODS A retrospective cohort study was conducted on 711 patients with 756 breast lesions between January 2019 and January 2022. S-Detect was utilized to assess lesions in radial and anti-radial planes. BI-RADS classifications were employed for comparative analysis. The diagnostic performance was compared within each group, and p-values were computed for intergroup comparisons. Univariable and multivariable analyses were conducted to identify factors contributing to diagnostic inconsistency in S-Detect across planes. RESULTS Among 756 breast lesions, 668 (88.4%) exhibited consistent S-Detect outcomes across planes while 88 (11.6%) were inconsistent. In the consistent group, the diagnostic accuracy and area under the curve (AUC) of S-Detect were significantly higher than those of BI-RADS (accuracy: 91.2% vs. 84.9%, p = 0.045; AUC: 0.916 vs. 0.859, p = 0.036). In the inconsistent group, the diagnostic accuracy and AUC of S-Detect in radial and anti-radial planes were lower than those of BI-RADS (accuracy: 47.7% for radial, 52.2% for anti-radial vs. 69.3% for BI-RADS, p = 0.014, p-anti = 0.039; AUC: 0.503 for radial, 0.497 for anti-radial vs. 0.739 for BI-RADS, p = 0.042, p-anti <0.001). Diagnostic inconsistency in S-Detect across planes was significantly associated with lesion size, indistinct or angular margins, and enhancement posterior acoustic features (p < 0.05). CONCLUSION S-Detect has outperformed BI-RADS in diagnostic precision under conditions of inter-planar concordance. However, its diagnostic efficacy is compromised in scenarios of inter-planar discordance. Under these circumstances, the results of S-Detect should be carefully referenced.
Collapse
Affiliation(s)
- Panpan Zhang
- Department of Ultrasound, The Affiliated Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China
| | - Min Zhang
- Department of Ultrasound, The Affiliated Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China
| | - Menglin Lu
- Department of Ultrasound, The Affiliated Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China
| | - Chaoying Jin
- Department of Ultrasound, The Affiliated Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China
| | - Gang Wang
- Department of Ultrasound, The Affiliated Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China
| | - Xianfang Lin
- Department of Ultrasound, The Affiliated Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China.
| |
Collapse
|
2
|
Xie H, Zhang Y, Dong L, Lv H, Li X, Zhao C, Tian Y, Xie L, Wu W, Yang Q, Liu L, Sun D, Qiu L, Shen L, Zhang Y. Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes. Front Oncol 2024; 14:1361694. [PMID: 38846984 PMCID: PMC11153704 DOI: 10.3389/fonc.2024.1361694] [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: 12/26/2023] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving. Objectives The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients. Methods We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study. Results The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01). Conclusion The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.
Collapse
Affiliation(s)
- Haiqin Xie
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Yudi Zhang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Licong Dong
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Heng Lv
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Xuechen Li
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
| | - Chenyang Zhao
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Yun Tian
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Lu Xie
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Wangjie Wu
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Qi Yang
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Li Liu
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Desheng Sun
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Li Qiu
- West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Linlin Shen
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Yusen Zhang
- Shenzhen Hospital, Peking University, Shenzhen, China
| |
Collapse
|
3
|
Song P, Zhang L, Bai L, Wang Q, Wang Y. Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer. Heliyon 2023; 9:e20712. [PMID: 37860526 PMCID: PMC10582378 DOI: 10.1016/j.heliyon.2023.e20712] [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: 05/05/2023] [Revised: 09/23/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
Purpose This study aims to examine the performance of breast ultrasound with a computer-aided diagnostic (CAD) system in detecting malignant breast cancer compared to conventional ultrasound and investigate the effects on smaller tumor sizes (≤20 mm). Methods This retrospective analysis included 123 patients with breast masses between March 2021 and July 2023. By using pathology results from biopsies or surgeries as the gold standard, we calculated and compared the diagnostic performances of conventional ultrasound and CAD, including sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve (AUC). A subgroup analysis of masses ≤20 mm in size was performed. Results Twenty-seven patients were pathologically diagnosed with malignant breast cancer. CAD had a higher specificity (92.71 % vs. 62.5 %) and accuracy (93.5 % vs. 69.92 %) than conventional ultrasound. The AUC of CAD was significantly greater than that of conventional ultrasonography (0.9450 vs. 0.7940, p < 0.0001). The agreement between the CAD and pathology results was almost perfect (kappa = 0.82, p < 0.0001). In patients with masses ≤20 mm, the effect was consistent: CAD had higher specificity (91.43 % vs. 51.43 %), higher accuracy (90.70 % vs. 58.14 %), and a higher AUC (0.8946 vs. 0.6946, p < 0.0001) than conventional ultrasound. Thirty-one downgrades were observed in BI-RADS 4A and 4B based on CAD, all of which were proven to be benign. Conclusion Compared to conventional breast ultrasound, CAD had better diagnostic performance, with higher specificity, accuracy, and AUC. CAD can help recognize benign lesions, especially in patients with BI-RADS 4A, and avoid unnecessary invasive procedures.
Collapse
Affiliation(s)
- Pengjie Song
- Department of Ultrasound, Gangkou Hospital, Qinhuangdao, Hebei, 066000, China
| | - Li Zhang
- Department of Ultrasound, Gangkou Hospital, Qinhuangdao, Hebei, 066000, China
| | - Longmei Bai
- Department of Ultrasound, Gangkou Hospital, Qinhuangdao, Hebei, 066000, China
| | - Qing Wang
- Department of Ultrasound, Gangkou Hospital, Qinhuangdao, Hebei, 066000, China
| | - Yanlei Wang
- Department of Ultrasound, Peking University Third Hospital Qinhuangdao Hospital, Qinhuangdao, Hebei, 066000, China
| |
Collapse
|
4
|
Xia M, Song F, Zhao Y, Xie Y, Wen Y, Zhou P. Ultrasonography-based radiomics and computer-aided diagnosis in thyroid nodule management: performance comparison and clinical strategy optimization. Front Endocrinol (Lausanne) 2023; 14:1140816. [PMID: 37251675 PMCID: PMC10213653 DOI: 10.3389/fendo.2023.1140816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/01/2023] [Indexed: 05/31/2023] Open
Abstract
Objectives To compare ultrasonography (US) feature-based radiomics and computer-aided diagnosis (CAD) models for predicting malignancy in thyroid nodules, and to evaluate their utility for thyroid nodule management. Methods This prospective study included 262 thyroid nodules obtained between January 2022 and June 2022. All nodules previously underwent standardized US image acquisition, and the nature of the nodules was confirmed by the pathological results. The CAD model exploited two vertical US images of the thyroid nodule to differentiate the lesions. The least absolute shrinkage and operator algorithm (LASSO) was applied to choose radiomics features with excellent predictive properties for building a radiomics model. Ultimately, the area under the receiver operating characteristic curve (AUC) and calibration curves were assessed to compare diagnostic performance between the models. DeLong's test was used to analyze the difference between groups. Both models were used to revise the American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR TI-RADS) to provide biopsy recommendations, and their performance was compared with the original recommendations. Results Of the 262 thyroid nodules, 157 were malignant, and the remaining 105 were benign. The diagnostic performance of radiomics, CAD, and ACR TI-RADS models had an AUC of 0.915 (95% confidence interval (CI): 0.881-0.947), 0.814 (95% CI: 0.766-0.863), and 0.849 (95% CI: 0.804-0.894), respectively. DeLong's test showed a statistically significant between the AUC values of models (p < 0.05). Calibration curves showed good agreement in each model. When both models were applied to revise the ACR TI-RADS, our recommendations significantly improved the performance. The revised recommendations based on radiomics and CAD showed an increased sensitivity, accuracy, positive predictive value, and negative predictive value, and decreased unnecessary fine-needle aspiration rates. Furthermore, the radiomics model's improvement scale was more pronounced (33.3-16.7% vs. 33.3-9.7%). Conclusion The radiomics strategy and CAD system showed good diagnostic performance for discriminating thyroid nodules and could be used to optimize the ACR TI-RADS recommendation, which successfully reduces unnecessary biopsies, especially in the radiomics model.
Collapse
Affiliation(s)
- Mengwen Xia
- Department of Ultrasonography, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Fulong Song
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yongfeng Zhao
- Department of Ultrasonography, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yongzhi Xie
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yafei Wen
- Department of Ultrasonography, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Ping Zhou
- Department of Ultrasonography, The Third Xiangya Hospital of Central South University, Changsha, China
| |
Collapse
|
5
|
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
|
6
|
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
|
7
|
Catalano O, Fusco R, De Muzio F, Simonetti I, Palumbo P, Bruno F, Borgheresi A, Agostini A, Gabelloni M, Varelli C, Barile A, Giovagnoni A, Gandolfo N, Miele V, Granata V. Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice. Diagnostics (Basel) 2023; 13:diagnostics13050980. [PMID: 36900124 PMCID: PMC10000574 DOI: 10.3390/diagnostics13050980] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D US, automated US, S-Detect, nomograms, images fusion, and virtual navigation. In the subsequent section, we discuss the broadened current application of US in breast clinical scenarios, distinguishing among primary US, complementary US, and second-look US. Finally, we mention the still ongoing limitations and the challenging aspects of breast US.
Collapse
Affiliation(s)
- Orlando Catalano
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, 80131 Naples, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Carlo Varelli
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, 80131 Naples, Italy
| |
Collapse
|
8
|
Villa-Camacho JC, Baikpour M, Chou SHS. Artificial Intelligence for Breast US. JOURNAL OF BREAST IMAGING 2023; 5:11-20. [PMID: 38416959 DOI: 10.1093/jbi/wbac077] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Indexed: 03/01/2024]
Abstract
US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently employed as a supplemental screening tool via either whole breast handheld US or automated breast US among women with dense breasts. In recent years, a variety of artificial intelligence systems have been developed to assist radiologists with the detection and diagnosis of breast lesions on US. This article reviews the background and evidence supporting the use of artificial intelligence tools for breast US, describes implementation strategies and impact on clinical workflow, and discusses potential emerging roles and future directions.
Collapse
Affiliation(s)
| | - Masoud Baikpour
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| | - Shinn-Huey S Chou
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| |
Collapse
|
9
|
程 扬, 夏 群, 王 俊, 解 红, 余 奕, 刘 海, 姚 志, 胡 金. [Value of ultrasonic S-Detect technique in diagnosis of breast masses]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2022; 42:1044-1049. [PMID: 35869768 PMCID: PMC9308870 DOI: 10.12122/j.issn.1673-4254.2022.07.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses. METHODS A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard. RESULTS When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05). CONCLUSION S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.
Collapse
Affiliation(s)
- 扬眉 程
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 群 夏
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 俊 王
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 红娟 解
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 奕 余
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 海华 刘
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 志正 姚
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 金花 胡
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| |
Collapse
|
10
|
Li C, Guo Y, Jia L, Yao M, Shao S, Chen J, Xu Y, Wu R. A Convolutional Neural Network Based on Ultrasound Images of Primary Breast Masses: Prediction of Lymph-Node Metastasis in Collaboration With Classification of Benign and Malignant Tumors. Front Physiol 2022; 13:882648. [PMID: 35721528 PMCID: PMC9205241 DOI: 10.3389/fphys.2022.882648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/10/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: A convolutional neural network (CNN) can perform well in either of two independent tasks [classification and axillary lymph-node metastasis (ALNM) prediction] based on breast ultrasound (US) images. This study is aimed to investigate the feasibility of performing the two tasks simultaneously. Methods: We developed a multi-task CNN model based on a self-built dataset containing 5911 breast US images from 2131 patients. A hierarchical loss (HL) function was designed to relate the two tasks. Sensitivity, specificity, accuracy, precision, F1-score, and analyses of receiver operating characteristic (ROC) curves and heatmaps were calculated. A radiomics model was built by the PyRadiomics package. Results: The sensitivity, specificity and area under the ROC curve (AUC) of our CNN model for classification and ALNM tasks were 83.5%, 71.6%, 0.878 and 76.9%, 78.3%, 0.836, respectively. The inconsistency error of ALNM prediction corrected by HL function decreased from 7.5% to 4.2%. Predictive ability of the CNN model for ALNM burden (≥3 or ≥4) was 77.3%, 62.7%, and 0.752, and 66.6%, 76.8%, and 0.768, respectively, for sensitivity, specificity and AUC. Conclusion: The proposed multi-task CNN model highlights its novelty in simultaneously distinguishing breast lesions and indicating nodal burden through US, which is valuable for “personalized” treatment.
Collapse
Affiliation(s)
- Chunxiao Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanfan Guo
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Liqiong Jia
- Department of Ultrasound, Zhongshan Hospital Wusong Branch, Fudan University, Shanghai, China
| | - Minghua Yao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sihui Shao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Chen
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Xu
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Rong Wu, ; Yi Xu,
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Rong Wu, ; Yi Xu,
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Zhao C, Xiao M, Ma L, Ye X, Deng J, Cui L, Guo F, Wu M, Luo B, Chen Q, Chen W, Guo J, Li Q, Zhang Q, Li J, Jiang Y, Zhu Q. Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study. Front Oncol 2022; 12:804632. [PMID: 35223484 PMCID: PMC8867611 DOI: 10.3389/fonc.2022.804632] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/07/2022] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions. METHODS Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites. RESULTS A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157). CONCLUSIONS Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.
Collapse
Affiliation(s)
- Chenyang Zhao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mengsu Xiao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Ma
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinhua Ye
- Department of Ultrasound, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Deng
- Department of Ultrasound, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Fajin Guo
- Department of Ultrasound, Beijing Hospital, Beijing, China
| | - Min Wu
- Department of Ultrasound, Nanjing Drum Tower Hospital, Nanjing, China
| | - Baoming Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Qin Chen
- Department of Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Wu Chen
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jun Guo
- Department of Ultrasound, Aero Space Central Hospital, Beijing, China
| | - Qian Li
- Department of Ultrasound, Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Qing Zhang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianchu Li
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxin Jiang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qingli Zhu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
13
|
Zhu Y, Zhan W, Jia X, Liu J, Zhou J. Clinical Application of Computer-Aided Diagnosis for Breast Ultrasonography: Factors That Lead to Discordant Results in Radial and Antiradial Planes. Cancer Manag Res 2022; 14:751-760. [PMID: 35237075 PMCID: PMC8882474 DOI: 10.2147/cmar.s348463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/27/2022] [Indexed: 01/30/2023] Open
Affiliation(s)
- Ying Zhu
- Department of Ultrasound, Shanghai Ruijin Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, People’s Republic of China
| | - Weiwei Zhan
- Department of Ultrasound, Shanghai Ruijin Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, People’s Republic of China
| | - Xiaohong Jia
- Department of Ultrasound, Shanghai Ruijin Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, People’s Republic of China
| | - Juan Liu
- Department of Ultrasound, Shanghai Ruijin Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, People’s Republic of China
| | - Jianqiao Zhou
- Department of Ultrasound, Shanghai Ruijin Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, People’s Republic of China
- Correspondence: Jianqiao Zhou, Department of Ultrasound, Shanghai Ruijin Hospital Affiliated to Medical School of Shanghai Jiaotong University, 197 Ruijin Er Road, Shanghai, 200025, People’s Republic of China, Email
| |
Collapse
|
14
|
The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol 2022; 32:4046-4055. [PMID: 35066633 DOI: 10.1007/s00330-021-08452-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/11/2021] [Accepted: 10/31/2021] [Indexed: 12/31/2022]
|
15
|
Ying Z, Xiaohong J, Yijie D, Juan L, Yilai C, Congcong Y, Weiwei Z, Jianqiao Z. Using S-Detect to Improve Breast Ultrasound: The Different Combined Strategies Based on Radiologist Experienc. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2022. [DOI: 10.37015/audt.2022.220007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
|
16
|
Nicosia L, Addante F, Bozzini AC, Latronico A, Montesano M, Meneghetti L, Tettamanzi F, Frassoni S, Bagnardi V, De Santis R, Pesapane F, Fodor CI, Mastropasqua MG, Cassano E. Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists. Clin Imaging 2021; 82:150-155. [PMID: 34826773 DOI: 10.1016/j.clinimag.2021.11.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/11/2021] [Accepted: 11/07/2021] [Indexed: 11/15/2022]
Abstract
PURPOSE To evaluate if a computer-aided diagnosis (CAD) system on ultrasound (US) can improve the diagnostic performance of inexperienced radiologists. METHODS We collected ultrasound images of 256 breast lesions taken between March and May 2020. We asked two experienced and two inexperienced radiologists to retrospectively review the US features of each breast lesion according to the Breast Imaging Reporting and Data System (BI-RADS) categories. A CAD examination with S-Detect™ software (Samsung Healthcare, Seoul, South Korea) was conducted retrospectively by another uninvolved radiologist blinded to the BIRADS values previously attributed to the lesions. Diagnostic performances of experienced and inexperienced radiologists and CAD were compared and the inter-observer agreement among radiologists was calculated. RESULTS The diagnostic performance of the experienced group in terms of sensitivity was significantly higher than CAD (p < 0.001). Conversely, the diagnostic performance of inexperienced group in terms of both sensitivity and specificity was significantly lower than CAD (p < 0.001). We obtained an excellent agreement in the evaluation of the lesions among the two expert radiologists (Kappa coefficient: 88.7%), and among the two non-expert radiologists (Kappa coefficient: 84.9%). CONCLUSION The US CAD system is a useful additional tool to improve the diagnostic performance of the inexperienced radiologists, eventually reducing the number of unnecessary biopsies. Moreover, it is a valid second opinion in case of experienced radiologists.
Collapse
Affiliation(s)
- Luca Nicosia
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy.
| | - Francesca Addante
- Department of Emergency and Organ Transplantation, Section of Anatomic Pathology, School of Medicine, University "Aldo Moro", 70124 Bari, Italy
| | - Anna Carla Bozzini
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Antuono Latronico
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Marta Montesano
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Lorenza Meneghetti
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Francesca Tettamanzi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Samuele Frassoni
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy
| | - Rossella De Santis
- Postgraduate School in Radiology, University of Milan, 20122 Milan, Italy
| | - Filippo Pesapane
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Cristiana Iuliana Fodor
- Division of Radiation Oncology, IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Mauro Giuseppe Mastropasqua
- Department of Emergency and Organ Transplantation, Section of Anatomic Pathology, School of Medicine, University "Aldo Moro", 70124 Bari, Italy
| | - Enrico Cassano
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| |
Collapse
|
17
|
Zhang D, Jiang F, Yin R, Wu GG, Wei Q, Cui XW, Zeng SE, Ni XJ, Dietrich CF. A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses. Med Sci Monit 2021; 27:e931957. [PMID: 34552043 PMCID: PMC8477643 DOI: 10.12659/msm.931957] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/10/2021] [Indexed: 12/24/2022] Open
Abstract
Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.
Collapse
Affiliation(s)
- Di Zhang
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Rui Yin
- Department of Ultrasound, Affiliated Renhe Hospital of China Three Gorges University, Yichang, Hubei, PR China
| | - Ge-Ge Wu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Qi Wei
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Shu-E Zeng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Xue-Jun Ni
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, Jiangsu, PR China
| | | |
Collapse
|
18
|
Li C, Li J, Tan T, Chen K, Xu Y, Wu R. Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification. Diagn Interv Radiol 2021; 27:315-322. [PMID: 34003119 DOI: 10.5152/dir.2021.20018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE We aimed to compare the diagnostic performance and interobserver variability in breast tumor classification with or without the aid of an innovative dual-mode artificial intelligence (AI) architecture, which can automatically integrate information from ultrasonography (US) and shear-wave elastography (SWE). METHODS Diagnostic performance assessment was performed with a test subset, containing 599 images (from September 2018 to February 2019) from 91 patients including 64 benign and 27 malignant breast tumors. Six radiologists (three inexperienced, three experienced) were assigned to read images independently (independent diagnosis) and then make a secondary diagnosis with the knowledge of AI results. Sensitivity, specificity, accuracy, receiver-operator characteristics (ROC) curve analysis and Cohen's κ statistics were calculated. RESULTS In the inexperienced radiologists' group, the average area under the ROC curve (AUC) for diagnostic performance increased from 0.722 to 0.765 (p = 0.050) with secondary diagnosis using US-mode and from 0.794 to 0.834 (p = 0.019) with secondary diagnosis using dual-mode compared with independent diagnosis. In the experienced radiologists' group, the average AUC for diagnostic performance was significantly higher with AI system using the US-mode (0.812 vs. 0.833, p = 0.039), but not for dual-mode (0.858 vs. 0.866, p = 0.458). Using the US-mode, interobserver agreement among all radiologists improved from fair to moderate (p = 0.003). Using the dual-mode, substantial agreement was seen among the experienced radiologists (0.65 to 0.74, p = 0.017) and all radiologists (0.62 to 0.73, p = 0.001). CONCLUSION AI assistance provides a more pronounced improvement in diagnostic performance for the inexperienced radiologists; meanwhile, the experienced radiologists benefit more from AI in reducing interobserver variability.
Collapse
Affiliation(s)
- Chunxiao Li
- Department of Ultrasound, Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital Shanghai, China
| | - Jiajun Li
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Tan
- Department of Mathematics and Computer Science, Centre for Analysis, Scientific Computing, and Applications W-I, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Kun Chen
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Xu
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital Shanghai, China
| |
Collapse
|
19
|
Wang XY, Cui LG, Feng J, Chen W. Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy. Eur J Radiol 2021; 138:109624. [PMID: 33706046 DOI: 10.1016/j.ejrad.2021.109624] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/24/2021] [Accepted: 03/01/2021] [Indexed: 01/30/2023]
Abstract
PURPOSE To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies. METHODS Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards. RESULTS Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P < 0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P < 0.05, compared with conventional US and stratification A). CONCLUSION Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.
Collapse
Affiliation(s)
- Xin-Yi Wang
- Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Breast Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China.
| | - Jie Feng
- Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China; Department of Ultrasound, Haicang Hospital, Xiamen 361000, China
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China
| |
Collapse
|
20
|
Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A. BMC Cancer 2020; 20:959. [PMID: 33008320 PMCID: PMC7532640 DOI: 10.1186/s12885-020-07413-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.
Collapse
|
21
|
Xiao M, Zhao C, Li J, Zhang J, Liu H, Wang M, Ouyang Y, Zhang Y, Jiang Y, Zhu Q. Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients. Front Oncol 2020; 10:1070. [PMID: 32733799 PMCID: PMC7358588 DOI: 10.3389/fonc.2020.01070] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 05/28/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose: The purpose of this study was to compare the diagnostic performance of breast lesions between deep learning-based computer-aided diagnosis (deep learning-based CAD) system and experienced radiologists and to compare the performance between symptomatic and asymptomatic patients. Methods: From January to December 2018, a total of 451 breast lesions in 389 consecutive patients were examined (mean age 46.86 ± 13.03 years, range 19-84 years) by both ultrasound and deep learning-based CAD system, all of which were biopsied, and the pathological results were obtained. The lesions were diagnosed by two experienced radiologists according to the fifth edition Breast Imaging Reporting and Data System (BI-RADS). The final deep learning-based CAD assessments were dichotomized as possibly benign or possibly malignant. The diagnostic performances of the radiologists and deep learning-based CAD were calculated and compared for asymptomatic patients and symptomatic patients. Results: There were 206 asymptomatic screening patients with 235 lesions (mean age 45.06 ± 10.90 years, range 21-73 years) and 183 symptomatic patients with 216 lesions (mean age 50.03 ± 14.97 years, range 19-84 years). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and area under the receiver operating characteristic curve (AUC) of the deep learning-based CAD in asymptomatic patients were 93.8, 83.9, 75.0, 96.3, 87.2, and 0.89%, respectively. In asymptomatic patients, the specificity (83.9 vs. 66.5%, p < 0.001), PPV (75.0 vs. 59.4%, p = 0.013), accuracy (87.2 vs. 76.2%, p = 0.002) and AUC (0.89 to 0.81, p = 0.0013) of CAD were all significantly higher than those of the experienced radiologists. The sensitivity (93.8 vs. 80.0%), specificity (83.9 vs. 61.8%,), accuracy (87.2 vs. 73.6%) and AUC (0.89 vs. 0.71) of CAD were all higher for asymptomatic patients than for symptomatic patients. If the BI-RADS 4a lesions diagnosed by the radiologists in asymptomatic patients were downgraded to BI-RADS 3 according to the CAD, then 54.8% (23/42) of the lesions would avoid biopsy without missing the malignancy. Conclusion: The deep learning-based CAD system had better performance in asymptomatic patients than in symptomatic patients and could be a promising complementary tool to ultrasound for increasing diagnostic specificity and avoiding unnecessary biopsies in asymptomatic screening patients.
Collapse
Affiliation(s)
- Mengsu Xiao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Chenyang Zhao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Jianchu Li
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Jing Zhang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - He Liu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Ming Wang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Yunshu Ouyang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Yixiu Zhang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Yuxin Jiang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Qingli Zhu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| |
Collapse
|
22
|
S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors. J Ultrasound 2020; 24:143-150. [PMID: 32447631 DOI: 10.1007/s40477-020-00476-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 05/06/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND To assess inter-reader agreement for US BI-RADS descriptors using S-Detect: a computer-guided decision-making software assisting in US morphologic analysis. METHODS 73 solid focal breast lesions (FBLs) (mean size: 15.9 mm) in 73 consecutive women (mean age: 51 years) detected at US were randomly and independently assessed according to the BI-RADS US lexicon, without and with S-Detect, by five independent reviewers. US-guided core-biopsy and 24-month follow-up were considered as standard of reference. Kappa statistics were calculated to assess inter-operator agreement, between the baseline and after S-Detect evaluation. Agreement was graded as poor (≤ 0.20), moderate (0.21-0.40), fair (0.41-0.60), good (0.61-0.80), or very good (0.81-1.00). RESULTS 33/73 (45.2%) FBLs were malignant and 40/73 (54.8%) FBLs were benign. A statistically significant improvement of inter-reader agreement from fair to good with the use of S-Detect was observed for shape (from 0.421 to 0.612) and orientation (from 0.417 to 0.7) (p < 0.0001) and from moderate to fair for margin (from 0.204 to 0.482) and posterior features (from 0.286 to 0.522) (p < 0.0001). At baseline analysis isoechoic (0.0485) and heterogeneous (0.1978) echo pattern, microlobulated (0.1161) angular (0.1204) and spiculated (0.1692) margins and combined pattern (0.1549) for posterior features showed the worst agreement rate (poor). After S-Detect evaluation, all variables but isoechoic pattern showed an agreement class upgrade with a statistically significant improvement of inter-reader agreement (p < 0.0001). CONCLUSIONS S-Detect significantly improved inter-reader agreement in the assessment of FBLs according to the BI-RADS US lexicon but evaluation of margin and echo pattern needs to be further improved, particularly isoechoic pattern.
Collapse
|
23
|
Yongping L, Juan Z, Zhou P, Yongfeng Z, Liu W, Shi Y. Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study. JMIR Med Inform 2020; 8:e18251. [PMID: 32369039 PMCID: PMC7238092 DOI: 10.2196/18251] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/16/2020] [Accepted: 04/10/2020] [Indexed: 12/13/2022] Open
Abstract
Background Computer-aided diagnosis (CAD) is a tool that can help radiologists diagnose breast lesions by ultrasonography. Previous studies have demonstrated that CAD can help reduce the incidence of missed diagnoses by radiologists. However, the optimal method to apply CAD to breast lesions using diagnostic planes has not been assessed. Objective The aim of this study was to compare the performance of radiologists with different levels of experience when using CAD with the quadri-planes method to detect breast tumors. Methods From November 2018 to October 2019, we enrolled patients in the study who had a breast mass as their most prominent symptom. We assigned 2 ultrasound radiologists (with 1 and 5 years of experience, respectively) to read breast ultrasonography images without CAD and then to perform a second reading while applying CAD with the quadri-planes method. We then compared the diagnostic performance of the readers for the 2 readings (without and with CAD). The McNemar test for paired data was used for statistical analysis. Results A total of 331 patients were included in this study (mean age 43.88 years, range 17-70, SD 12.10), including 512 lesions (mean diameter 1.85 centimeters, SD 1.19; range 0.26-9.5); 200/512 (39.1%) were malignant, and 312/512 (60.9%) were benign. For CAD, the area under the receiver operating characteristic curve (AUC) improved significantly from 0.76 (95% CI 0.71-0.79) with the cross-planes method to 0.84 (95% CI 0.80-0.88; P<.001) with the quadri-planes method. For the novice reader, the AUC significantly improved from 0.73 (95% CI 0.69-0.78) for the without-CAD mode to 0.83 (95% CI 0.80-0.87; P<.001) for the combined-CAD mode with the quadri-planes method. For the experienced reader, the AUC improved from 0.85 (95% CI 0.81-0.88) to 0.87 (95% CI 0.84-0.91; P=.15). The kappa indicating consistency between the experienced reader and the novice reader for the combined-CAD mode was 0.63. For the novice reader, the sensitivity significantly improved from 60.0% for the without-CAD mode to 79.0% for the combined-CAD mode (P=.004). The specificity, negative predictive value, positive predictive value, and accuracy improved from 84.9% to 87.8% (P=.53), 76.8% to 86.7% (P=.07), 71.9% to 80.6% (P=.13), and 75.2% to 84.4% (P=.12), respectively. For the experienced reader, the sensitivity improved significantly from 76.0% for the without-CAD mode to 87.0% for the combined-CAD mode (P=.045). The NPV and accuracy moderately improved from 85.8% and 86.3% to 91.0% (P=.27) and 87.0% (P=.84), respectively. The specificity and positive predictive value decreased from 87.4% to 81.3% (P=.25) and from 87.2% to 93.0% (P=.16), respectively. Conclusions S-Detect is a feasible diagnostic tool that can improve the sensitivity, accuracy, and AUC of the quadri-planes method for both novice and experienced readers while also improving the specificity for the novice reader. It demonstrates important application value in the clinical diagnosis of breast cancer. Trial Registration ChiCTR.org.cn 1800019649; http://www.chictr.org.cn/showproj.aspx?proj=33094
Collapse
Affiliation(s)
- Liang Yongping
- The Xiangya Medical School, Central South University, Changsha, Hunan, China
| | - Zhang Juan
- The Xiangya Medical School, Central South University, Changsha, Hunan, China
| | - Ping Zhou
- The Xiangya Medical School, Central South University, Changsha, Hunan, China
| | - Zhao Yongfeng
- The Xiangya Medical School, Central South University, Changsha, Hunan, China
| | - Wengang Liu
- The Xiangya Medical School, Central South University, Changsha, Hunan, China
| | - Yifan Shi
- The Xiangya Medical School, Central South University, Changsha, Hunan, China
| |
Collapse
|
24
|
Xiao M, Zhao C, Zhu Q, Zhang J, Liu H, Li J, Jiang Y. An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions. J Thorac Dis 2019; 11:5023-5031. [PMID: 32030218 DOI: 10.21037/jtd.2019.12.10] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Deep learning-based computer-aided diagnosis (CAD) is an important method in aiding diagnosis for radiologists. We investigated the accuracy of a deep learning-based CAD in classifying breast lesions with different histological types. Methods A total of 448 breast lesions were detected on ultrasound (US) and classified by an experienced radiologist, a resident and deep learning-based CAD respectively. The pathological results of the lesions were chosen as the golden standard. The diagnostic performances of the three raters in different pathological types were analyzed. Results For the overall diagnostic performance, deep learning-based CAD presented a significantly higher specificity (76.96%) compared with the two radiologists. The area under ROC of CAD was almost equal with the experienced radiologist (0.81 vs. 0.81), while significantly higher than the resident (0.81 vs. 0.70, P<0.0001). In the benign lesions, deep learning-based CAD had a higher accuracy than both the two radiologists, which correctly classified as benign lesions in 119/135 of fibroadenomas (88.1%), 25/35 of adenosis (71.4%), 14/27 of intraductal papillary tumors (51.9%), 5/10 of inflammation (50%), and 4/8 of sclerosing adenosis (50%). But only the differences between CAD and the two radiologists in fibroadenomas had statistical significance (P=0.0011 and P=0.0313), and the differences between CAD and the resident in adenosis had statistical significance (P=0.012). In the malignant lesions, 151/168 of invasive ductal carcinomas (89.9%), 21/29 of ductal carcinoma in situ (DCIS) (72.4%) and 6/7 of invasive lobular carcinomas (85.7%) were diagnosed as malignancies by deep learning-based CAD, with no significant differences between CAD and the two radiologists. Conclusions In the diagnosis of these common types of breast lesions, deep learning-based CAD had a satisfying performance. Deep learning-based CAD had a better performance in the breast benign lesions, especially in fibroadenomas and adenosis. Therefore, deep learning-based CAD is a promising supplemental tool to US to increase the specificity and avoid unnecessary benign biopsies.
Collapse
Affiliation(s)
- Mengsu Xiao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| | - Chenyang Zhao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| | - Qingli Zhu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| | - Jing Zhang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| | - He Liu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| | - Jianchu Li
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| | - Yuxin Jiang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China
| |
Collapse
|