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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.
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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.
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Eremici I, Borlea A, Dumitru C, Stoian D. Factors Associated with False Positive Breast Cancer Results in the Real-Time Sonoelastography Evaluation of Solid Breast Lesions. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1023. [PMID: 39064452 PMCID: PMC11279031 DOI: 10.3390/medicina60071023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/07/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024]
Abstract
Background and Objectives: Breast cancer is one of the most widespread cancers among the female population around the world and is curable if diagnosed in an early stage. Consequently, breast cancer screening imaging techniques have greatly evolved and adjusted over the last decades. Alongside mammography, sonoelastography became an important tool for breast cancer detection. However, sonoelastography still has its limitations, namely, there is still a high occurrence of false positive results in the BIRADS 4 category. The aim of our study is to identify potential false positive predictors and to ascertain the factors influencing the quality of strain ultrasound elastography for the evaluation of suspicious solid breast lesions categorized as BIRADS 4B, 4C, and 5. Materials and Methods: We conducted a retrospective study in a single private medical center in Timisoara between January 2017 and January 2022 analyzing 1625 solid breast lesions by the sonoelastography strain using a standardized BIRADS-US lexicon. Results: Our study showed that most sonoelastography factors linked to incorrect and overdiagnosis were due to a nodule dimension (OR = 1.02 per unit increase), posterior acoustic shadowing (OR = 12.26), reactive adenopathy (OR = 6.35), and an increased TES score (TES3 OR = 6.60; TES4 OR = 23.02; TES5 OR = 108.24). Regarding patient characteristics, age (OR = 1.09 per unit increase), BMI, (OR = 1.09 per unit increase), and breastfeeding history (OR = 3.00) were observed to increase the likelihood of false positive results. On the other hand, the nodules less likely to be part of the false positive group exhibited the following characteristics: a regular shape (OR = 0.27), homogenous consistency (OR = 0.42), and avascularity (OR = 0.22). Conclusions: Older age, high BMI, patients with a breastfeeding history, and those who exhibit the following specific nodule characteristics were most often linked to false positive results: large tumors with posterior acoustic shadowing and high elasticity scores, accompanied by reactive adenopathy. On the other hand, homogenous, avascular nodules with regular shapes were less likely to be misdiagnosed.
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Affiliation(s)
- Ivana Eremici
- PhD School, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Andreea Borlea
- Department of Internal Medicine II, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Catalin Dumitru
- Obstetrics and Gynecology Department, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Dana Stoian
- Department of Internal Medicine II, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
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Guldogan N, Taskin F, Icten GE, Yilmaz E, Turk EB, Erdemli S, Parlakkilic UT, Turkoglu O, Aribal E. Artificial Intelligence in BI-RADS Categorization of Breast Lesions on Ultrasound: Can We Omit Excessive Follow-ups and Biopsies? Acad Radiol 2024; 31:2194-2202. [PMID: 38087719 DOI: 10.1016/j.acra.2023.11.031] [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: 09/18/2023] [Revised: 11/18/2023] [Accepted: 11/20/2023] [Indexed: 07/01/2024]
Abstract
RATIONALE AND OBJECTIVES Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evaluate the performance of an AI system for the BI-RADS category assessment in breast masses detected on breast ultrasound. MATERIALS AND METHODS: A total of 715 masses detected in 530 patients were analyzed. Three breast imaging centers of the same institution and nine breast radiologists participated in this study. Ultrasound was performed by one radiologist who obtained two orthogonal views of each detected lesion. These images were retrospectively reviewed by a second radiologist blinded to the patient's clinical data. A commercial AI system evaluated images. The level of agreement between the AI system and the two radiologists and their diagnostic performance were calculated according to dichotomic BI-RADS category assessment. RESULTS This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups. CONCLUSION AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices.
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Affiliation(s)
- Nilgun Guldogan
- Breast Clinic, Acibadem Altunizade Hospital, 34662, Istanbul, Turkey (N.G., E.Y., E.B.T., E.A.).
| | - Fusun Taskin
- Department of Radiology, Acibadem M.A.A. University School of Medicine, Atakent University Hospital, 34755, Istanbul, Turkey (F.T., S.E.); Acibadem M.A.A. University Senology Research Institute, 34457, Sarıyer, Istanbul, Turkey (F.T., G.E.I., U.T.P.)
| | - Gul Esen Icten
- Acibadem M.A.A. University Senology Research Institute, 34457, Sarıyer, Istanbul, Turkey (F.T., G.E.I., U.T.P.); Department of Radiology, Acibadem M.A.A. University School of Medicine, Acıbadem Maslak Hospital, Büyükdere St. 40, 34457, Maslak, Istanbul, Turkey (G.E.I.)
| | - Ebru Yilmaz
- Breast Clinic, Acibadem Altunizade Hospital, 34662, Istanbul, Turkey (N.G., E.Y., E.B.T., E.A.)
| | - Ebru Banu Turk
- Breast Clinic, Acibadem Altunizade Hospital, 34662, Istanbul, Turkey (N.G., E.Y., E.B.T., E.A.)
| | - Servet Erdemli
- Department of Radiology, Acibadem M.A.A. University School of Medicine, Atakent University Hospital, 34755, Istanbul, Turkey (F.T., S.E.)
| | - Ulku Tuba Parlakkilic
- Acibadem M.A.A. University Senology Research Institute, 34457, Sarıyer, Istanbul, Turkey (F.T., G.E.I., U.T.P.)
| | - Ozlem Turkoglu
- Department of Radiology, Taksim Training and Research Hospital, Istanbul, Turkey (O.T.)
| | - Erkin Aribal
- Breast Clinic, Acibadem Altunizade Hospital, 34662, Istanbul, Turkey (N.G., E.Y., E.B.T., E.A.); Department of Radiology, Acibadem M.A.A. University School of Medicine, Istanbul, Turkey (E.A.)
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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.
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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
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Berg WA, López Aldrete AL, Jairaj A, Ledesma Parea JC, García CY, McClennan RC, Cen SY, Larsen LH, de Lara MTS, Love S. Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting. Radiology 2023; 307:e223351. [PMID: 37129492 PMCID: PMC10323289 DOI: 10.1148/radiol.223351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/20/2023] [Accepted: 03/15/2023] [Indexed: 05/03/2023]
Abstract
Background Most low- and middle-income countries lack access to organized breast cancer screening, and women with lumps may wait months for diagnostic assessment. Purpose To demonstrate that artificial intelligence (AI) software applied to breast US images obtained with low-cost portable equipment and by minimally trained observers could accurately classify palpable breast masses for triage in a low-resource setting. Materials and Methods This prospective multicenter study evaluated participants with at least one palpable mass who were enrolled in a hospital in Jalisco, Mexico, from December 2017 through May 2021. Orthogonal US images were obtained first with portable US with and without calipers of any findings at the site of lump and adjacent tissue. Then women were imaged with standard-of-care (SOC) US with Breast Imaging Reporting and Data System assessments by a radiologist. After exclusions, 758 masses in 300 women were analyzable by AI, with outputs of benign, probably benign, suspicious, and malignant. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. Results The mean patient age ± SD was 50.0 years ± 12.5 (range, 18-92 years) and mean largest lesion diameter was 13 mm ± 8 (range, 2-54 mm). Of 758 masses, 360 (47.5%) were palpable and 56 (7.4%) malignant, including six ductal carcinoma in situ. AI correctly identified 47 or 48 of 49 women (96%-98%) with cancer with either portable US or SOC US images, with AUCs of 0.91 and 0.95, respectively. One circumscribed invasive ductal carcinoma was classified as probably benign with SOC US, ipsilateral to a spiculated invasive ductal carcinoma. Of 251 women with benign masses, 168 (67%) imaged with SOC US were classified as benign or probably benign by AI, as were 96 of 251 masses (38%, P < .001) with portable US. AI performance with images obtained by a radiologist was significantly better than with images obtained by a minimally trained observer. Conclusion AI applied to portable US images of breast masses can accurately identify malignancies. Moderate specificity, which could triage 38%-67% of women with benign masses without tertiary referral, should further improve with AI and observer training with portable US. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Slanetz in this issue.
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Affiliation(s)
- Wendie A. Berg
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Ana-Lilia López Aldrete
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Ajit Jairaj
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Juan Carlos Ledesma Parea
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Claudia Yolanda García
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - R. Chad McClennan
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Steven Yong Cen
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Linda H. Larsen
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - M. Teresa Soler de Lara
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
| | - Susan Love
- From the Department of Radiology, University of Pittsburgh School of
Medicine, Magee-Womens Hospital, 300 Halket St, Pittsburgh, PA 15213 (W.A.B.);
Departments of Gynecology (A.L.L.A., C.Y.G.) and Radiology (J.C.L.P.), Hospital
Valentín Gómez Farias, Zapopan, Mexico; Koios Medical, New York,
NY (A.J., R.C.M.); Department of Radiology, Keck School of Medicine of USC, Los
Angeles, Calif (S.Y.C., L.H.L.); and Dr Susan Love Research Foundation, West
Hollywood, Calif (M.T.S.d.L., S.L.)
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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.
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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.
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Zhou L, Zheng LL, Zhang CJ, Wei HF, Xu LL, Zhang MR, Li Q, He GF, Ghamor-Amegavi EP, Li SY. Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules. Front Endocrinol (Lausanne) 2023; 14:1098031. [PMID: 36761203 PMCID: PMC9902707 DOI: 10.3389/fendo.2023.1098031] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/11/2023] [Indexed: 01/26/2023] Open
Abstract
PURPOSE The aim of this study was to investigate the value of S-Detect for predicting the malignant risk of cytologically indeterminate thyroid nodules (CITNs). METHODS The preoperative prediction of 159 CITNs (Bethesda III, IV and V) were performed using S-Detect, Thyroid Imaging Reporting and Data System of American College of Radiology (ACR TI-RADS) and Chinese TI-RADS (C-TIRADS). First, Linear-by-Linear Association test and chi-square test were used to analyze the malignant risk of CITNs. McNemar's test and receiver operating characteristic curve were used to compare the diagnostic efficacy of S-Detect and the two TI-RADS classifications for CITNs. In addition, the McNemar's test was used to compare the diagnostic accuracy of the above three methods for different pathological types of nodules. RESULTS The maximum diameter of the benign nodules was significantly larger than that of malignant nodules [0.88(0.57-1.42) vs 0.57(0.46-0.81), P=0.002]. The risk of malignant CITNs in Bethesda system and the two TI-RADS classifications increased with grade (all P for trend<0.001). In all the enrolled CITNs, the diagnostic results of S-Detect were significantly different from those of ACR TI-RADS and C-TIRADS, respectively (P=0.021 and P=0.007). The sensitivity and accuracy of S-Detect [95.9%(90.1%-98.5%) and 88.1%(81.7%-92.5%)] were higher than those of ACR TI-RADS [87.6%(80.1%-92.7%) and 81.8%(74.7%-87.3%)] (P=0.006 and P=0.021) and C-TIRADS [84.3%(76.3%-90.0%) and 78.6%(71.3%-84.5%)] (P=0.001 and P=0.001). Moreover, the negative predictive value and the area under curve value of S-Detect [82.8% (63.5%-93.5%) and 0.795%(0.724%-0.855%)] was higher than that of C-TIRADS [54.8%(38.8%-69.8%) and 0.724%(0.648%-0.792%] (P=0.024 and P=0.035). However, the specificity and positive predictive value of S-Detect were similar to those of ACR TI-RADS (P=1.000 and P=0.154) and C-TIRADS (P=1.000 and P=0.072). There was no significant difference in all the evaluated indicators between ACR TI-RADS and C-TIRADS (all P>0.05). The diagnostic accuracy of S-Detect (97.4%) for papillary thyroid carcinoma (PTC) was higher than that of ACR TI-RADS (90.4%) and C-TIRADS (87.8%) (P=0.021 and P=0.003). CONCLUSION The diagnostic performance of S-Detect in differentiating CITNs was similar to ACR TI-RADS and superior to C-TIRADS, especially for PTC.
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Affiliation(s)
- Ling Zhou
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Lin-Lin Zheng
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chuan-Ju Zhang
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hong-Fen Wei
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Li-Long Xu
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Mu-Rui Zhang
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qiang Li
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Gao-Fei He
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | | | - Shi-Yan Li
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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8
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Xing B, Chen X, Wang Y, Li S, Liang YK, Wang D. Evaluating breast ultrasound S-detect image analysis for small focal breast lesions. Front Oncol 2022; 12:1030624. [PMID: 36582786 PMCID: PMC9792476 DOI: 10.3389/fonc.2022.1030624] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022] Open
Abstract
Background S-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists' assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation. Methods The US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two. Results There were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group (Z = 3.882, p = 0.0001) and the S-Detect group (Z = 3.861, p = 0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions when comparing conventional US and S-Detect techniques. Conclusions The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.
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Affiliation(s)
- Boyuan Xing
- Department of Ultrasound Imaging, The People’s Hospital of China Three Gorges University/the First People’s Hospital of Yichang, Yichang, Hubei, China
| | - Xiangyi Chen
- Department of Nuclear Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yalin Wang
- Department of Medical Engineering, Medical Supplies Center of PLA General Hospital, Beijing, China
| | - Shuang Li
- Department of Pathology, The People’s Hospital of China Three Gorges University/the First People’s Hospital of Yichang, Yichang, Hubei, China
| | - Ying-Kui Liang
- Department of Nuclear Medicine, The Sixth Medical Center of People's Liberation Army General Hospital, Beijing, China,*Correspondence: Dawei Wang, ; Ying-Kui Liang,
| | - Dawei Wang
- Department of Medical Engineering, Medical Supplies Center of PLA General Hospital, Beijing, China,Department of Nuclear Medicine, The Sixth Medical Center of People's Liberation Army General Hospital, Beijing, China,*Correspondence: Dawei Wang, ; Ying-Kui Liang,
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9
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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
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10
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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]
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O'Connell AM, Bartolotta TV, Orlando A, Jung SH, Baek J, Parker KJ. Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:97-105. [PMID: 33665833 DOI: 10.1002/jum.15684] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 02/17/2021] [Accepted: 02/18/2021] [Indexed: 05/26/2023]
Abstract
OBJECTIVES We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists. METHODS A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists. RESULTS The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI-RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies. CONCLUSION The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.
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Affiliation(s)
- Avice M O'Connell
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Tommaso V Bartolotta
- Department of Radiology, University Hospital, Palermo, Italy
- Fondazione Istituto G. Giglio Hospital, Cefalù, Italy
| | - Alessia Orlando
- Department of Radiology, University Hospital, Palermo, Italy
| | - Sin-Ho Jung
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jihye Baek
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - Kevin J Parker
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
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Ilesanmi AE, Chaumrattanakul U, Makhanov SS. Methods for the segmentation and classification of breast ultrasound images: a review. J Ultrasound 2021; 24:367-382. [PMID: 33428123 PMCID: PMC8572242 DOI: 10.1007/s40477-020-00557-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Breast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images. METHODS In this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed. RESULTS Commonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated. CONCLUSIONS We presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.
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Affiliation(s)
- Ademola E. Ilesanmi
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12000 Thailand
| | | | - Stanislav S. Makhanov
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12000 Thailand
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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.
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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
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Cheng C, Hu C, Zhou S, Zhao H, Yu M. Qualitative Diagnosis of Solid Breast Mass by Blood Flow in Solid Breast Mass. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Breast cancer is one of the most common malignant tumors that seriously endangers women’s physical and mental health and even life-threatening. With the increasing incidence of breast malignant tumors year by year, people are increasingly concerned about the health of female breasts.
However, due to the lack of primary prevention methods for breast cancer, the key to improving the cure rate of breast cancer and reducing mortality is early detection, early diagnosis, and early treatment. Hemodynamics embodies the characteristics and laws of the movement of blood and its
components in the body. Clinically, monitoring of hemodynamic indicators is usually used to reveal the physiological or pathological changes of the body and understand the development process of the disease. The blood flow in the tumor tissue is high-speed and high-impedance. CDFI can show
the distribution of blood flow in the mass. The highest peak velocity (PSV), resistance index (RI) and pulsatility index (PI) of systolic phase can be measured by sampling. This study use contrast-enhanced ultrasound (CEUS) combined with color energy Doppler flow imaging (CDFI) to explore
the hemodynamic information of the main blood supply arteries and masses of the breast, analyze the abnormal hemodynamic information of tumors and surrounding tissues, and explore the blood in the breast, the clinical value of hydrodynamics in the qualitative diagnosis of breast masses.
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Affiliation(s)
- Chen Cheng
- First Affiliated Hospital of Soochow University, Suzhou Jiangsu, 215006, China
| | - Chunhong Hu
- First Affiliated Hospital of Soochow University, Suzhou Jiangsu, 215006, China
| | - Shengli Zhou
- The First People's Hospital of Lianyungang, Lianyungang Jiangsu, 222002, China
| | - Hongyan Zhao
- Lianyungang Traditional Chinese Medicine Hospital, Lianyungang Jiangsu, 222004, China
| | - Ming Yu
- The First People's Hospital of Lianyungang, Lianyungang Jiangsu, 222002, China
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15
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Berg WA, Gur D, Bandos AI, Nair B, Gizienski TA, Tyma CS, Abrams G, Davis KM, Mehta AS, Rathfon G, Waheed UX, Hakim CM. Impact of Original and Artificially Improved Artificial Intelligence-based Computer-aided Diagnosis on Breast US Interpretation. JOURNAL OF BREAST IMAGING 2021; 3:301-311. [PMID: 38424776 DOI: 10.1093/jbi/wbab013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Indexed: 03/02/2024]
Abstract
OBJECTIVE For breast US interpretation, to assess impact of computer-aided diagnosis (CADx) in original mode or with improved sensitivity or specificity. METHODS In this IRB approved protocol, orthogonal-paired US images of 319 lesions identified on screening, including 88 (27.6%) cancers (median 7 mm, range 1-34 mm), were reviewed by 9 breast imaging radiologists. Each observer provided BI-RADS assessments (2, 3, 4A, 4B, 4C, 5) before and after CADx in a mode-balanced design: mode 1, original CADx (outputs benign, probably benign, suspicious, or malignant); mode 2, artificially-high-sensitivity CADx (benign or malignant); and mode 3, artificially-high-specificity CADx (benign or malignant). Area under the receiver operating characteristic curve (AUC) was estimated under each modality and for standalone CADx outputs. Multi-reader analysis accounted for inter-reader variability and correlation between same-lesion assessments. RESULTS AUC of standalone CADx was 0.77 (95% CI: 0.72-0.83). For mode 1, average reader AUC was 0.82 (range 0.76-0.84) without CADx and not significantly changed with CADx. In high-sensitivity mode, all observers' AUCs increased: average AUC 0.83 (range 0.78-0.86) before CADx increased to 0.88 (range 0.84-0.90), P < 0.001. In high-specificity mode, all observers' AUCs increased: average AUC 0.82 (range 0.76-0.84) before CADx increased to 0.89 (range 0.87-0.92), P < 0.0001. Radiologists responded more frequently to malignant CADx cues in high-specificity mode (42.7% vs 23.2% mode 1, and 27.0% mode 2, P = 0.008). CONCLUSION Original CADx did not substantially impact radiologists' interpretations. Radiologists showed improved performance and were more responsive when CADx produced fewer false-positive malignant cues.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
| | - David Gur
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
| | - Andriy I Bandos
- University of Pittsburgh Graduate School of Public Health, Department of Biostatistics, Pittsburgh, PA, USA
| | - Bronwyn Nair
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
| | - Terri-Ann Gizienski
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
| | - Cathy S Tyma
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
- New York University Langone Medical Center, Department of Radiology, New York, NY,USA
| | - Gordon Abrams
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
| | - Katie M Davis
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
- Vanderbilt University Medical Center, Department of Radiology, Nashville, TN,USA
| | - Amar S Mehta
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
- DuPage Medical Group, Department of Radiology, Downers Grove, IL,USA
| | - Grace Rathfon
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
- Steuben Radiology Associates, Steubenville, OH,USA
| | - Uzma X Waheed
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
| | - Christiane M Hakim
- University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA
- Magee-Womens Hospital of UPMC, Pittsburgh, PA,USA
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Xia Q, Cheng Y, Hu J, Huang J, Yu Y, Xie H, Wang J. Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3680-3689. [PMID: 34198406 DOI: 10.3934/mbe.2021184] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Objective Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses. Methods The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency. Result Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly. Conclusion The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.
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Affiliation(s)
- Qun Xia
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
| | - Yangmei Cheng
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
| | - Jinhua Hu
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
| | - Juxia Huang
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
| | - Yi Yu
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
| | - Hongjuan Xie
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
| | - Jun Wang
- Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anhui 246004, China
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Zhang N, Li XT, Ma L, Fan ZQ, Sun YS. Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging. Clin Imaging 2021; 79:56-63. [PMID: 33887507 DOI: 10.1016/j.clinimag.2021.03.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging and to compare its performance with that of radiologists. METHODS The ultrasound images of 1311 lesions were evaluated by radiologists according to the BI-RADS categories, using pathology results as reference. Two classification standards (standards 1 and 2) for benign and malignant lesions were defined and used to calculate the diagnostic performance of radiologists, altogether and individually. The breast lesion images were also used to develop an AI diagnostic model. RESULTS The diagnostic performance of AI and that of the radiologists were compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). All parameters of diagnostic performance, except for sensitivity and NPV, improved with standard 2. For the 202 lesions in the test set, the diagnostic performance of the AI model had 77.0% accuracy, 82.0% sensitivity, 71.7% specificity, 79.3% PPV, 75.1% NPV, and an AUC of 0.846. When the AI model was used to analyze category 4A lesions, the PPV was 9.3%, which was better than that of the radiologists, although not significantly. CONCLUSIONS Deep learning technology shows a good performance in classifying benign and malignant breast lesions. It may be potentially used in practice to improve diagnostic accuracy and reduce unnecessary biopsies of breast lesions.
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Affiliation(s)
- Nan Zhang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fucheng Road, Haidian District, Beijing, China; Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Breast Center, Peking University Cancer Hospital & Institute, No. 52 Fucheng Road, Haidian District, Beijing, China
| | - Xiao-Ting Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fucheng Road, Haidian District, Beijing, China
| | - Lu Ma
- Beijing Yizhun Medical AI Co., Ltd., No.1106, 11th Floor, Weishi Building, No. 39 Xueyuan Road, Haidian District, Beijing, China
| | - Zhao-Qing Fan
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Breast Center, Peking University Cancer Hospital & Institute, No. 52 Fucheng Road, Haidian District, Beijing, China
| | - Ying-Shi Sun
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fucheng Road, Haidian District, Beijing, China.
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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.
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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
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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.
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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
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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
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Barczyński M, Stopa-Barczyńska M, Wojtczak B, Czarniecka A, Konturek A. Clinical validation of S-Detect TM mode in semi-automated ultrasound classification of thyroid lesions in surgical office. Gland Surg 2020; 9:S77-S85. [PMID: 32175248 DOI: 10.21037/gs.2019.12.23] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background In recent years well-recognized scientific societies introduced guidelines for ultrasound (US) malignancy risk stratification of thyroid nodules. These guidelines categorize the risk of malignancy in relation to a combination of several US features. Based on these US image lexicons an US-based computer-aided diagnosis (CAD) systems were developed. Nevertheless, their clinical utility has not been evaluated in any study of surgeon-performed office US of the thyroid. Hence, the aim of this pilot study was to validate s-DetectTM mode in semi-automated US classification of thyroid lesions during surgeon-performed office US. Methods This is a prospective study of 50 patients who underwent surgeon-performed thyroid US (basic US skills without CAD vs. with CAD vs. expert US skills without CAD) in the out-patient office as part of the preoperative workup. The real-time CAD system software using artificial intelligence (S-DetectTM for Thyroid; Samsung Medison Co.) was integrated into the RS85 US system. Primary outcome was CAD system added-value to the surgeon-performed office US evaluation. Secondary outcomes were: diagnostic accuracy of CAD system, intra and interobserver variability in the US assessment of thyroid nodules. Surgical pathology report was used to validate the pre-surgical diagnosis. Results CAD system added-value to thyroid assessment by a surgeon with basic US skills was equal to 6% (overall accuracy of 82% for evaluation with CAD vs. 76% for evaluation without CAD system; P<0.001), and final diagnosis was different than predicted by US assessment in 3 patients (1 more true-positive and 2 more true-negative results). However, CAD system was inferior to thyroid assessment by a surgeon with expert US skills in 6 patients who had false-positive results (P<0.001). Conclusions The sensitivity and negative predictive value of CAD system for US classification of thyroid lesions were similar as surgeon with expert US skills whereas specificity and positive predictive value were significantly inferior but markedly better than judgement of a surgeon with basic US skills alone.
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Affiliation(s)
- Marcin Barczyński
- Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland
| | - Małgorzata Stopa-Barczyńska
- Clinical Ward of General Surgery and Oncology, Gabriel Narutowicz Memorial Municipal Hospital, Kraków, Poland
| | - Beata Wojtczak
- Department of General, Minimally Invasive and Endocrine Surgery, Wroclaw Medical University, Wroclaw, Poland
| | - Agnieszka Czarniecka
- Department of Oncological and Reconstructive Surgery, M. Sklodowska-Curie Institute - Oncology Centre, Gliwice, Poland
| | - Aleksander Konturek
- Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland
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Tanaka H, Chiu SW, Watanabe T, Kaoku S, Yamaguchi T. Computer-aided diagnosis system for breast ultrasound images using deep learning. Phys Med Biol 2019; 64:235013. [PMID: 31645021 DOI: 10.1088/1361-6560/ab5093] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The purpose of this study was to develop a computer-aided diagnosis (CAD) system for the classification of malignant and benign masses in the breast using ultrasonography based on a convolutional neural network (CNN), a state-of-the-art deep learning technique. We explored the regions for the correct classification by generating a heat map that presented the important regions used by the CNN for human malignancy/benign classification. Clinical data was obtained from a large-scale clinical trial previously conducted by the Japan Association of Breast and Thyroid Sonology. Images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations were collected, with each breast mass captured from various angles using an ultrasound (US) imaging probe. We constructed an ensemble network by combining two CNN models (VGG19 and ResNet152) fine-tuned on balanced training data with augmentation and used the mass-level classification method to enable the CNN to classify a given mass using all views. For an independent test set consisting of 154 masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9% (95% confidence interval 84.5-97.3), a specificity of 87.0% (79.5-94.5), and area under the curve (AUC) of 0.951 (0.916-0.987) compared to that of the two CNN models. In addition, our study indicated that the breast masses themselves were not detected by the CNN as important regions for correct mass classification. Collectively, this CNN-based CAD system is expected to assist doctors by improving the diagnosis of breast cancer in clinical practice.
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Affiliation(s)
- Hiroki Tanaka
- Division of Biostatistics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan. Author to whom any correspondence should be addressed
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