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Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer
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 1...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582378/ https://www.ncbi.nlm.nih.gov/pubmed/37860526 http://dx.doi.org/10.1016/j.heliyon.2023.e20712 |
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author | Song, Pengjie Zhang, Li Bai, Longmei Wang, Qing Wang, Yanlei |
author_facet | Song, Pengjie Zhang, Li Bai, Longmei Wang, Qing Wang, Yanlei |
author_sort | Song, Pengjie |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10582378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-105823782023-10-19 Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer Song, Pengjie Zhang, Li Bai, Longmei Wang, Qing Wang, Yanlei Heliyon Research Article 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. Elsevier 2023-10-06 /pmc/articles/PMC10582378/ /pubmed/37860526 http://dx.doi.org/10.1016/j.heliyon.2023.e20712 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Song, Pengjie Zhang, Li Bai, Longmei Wang, Qing Wang, Yanlei Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
title | Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
title_full | Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
title_fullStr | Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
title_full_unstemmed | Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
title_short | Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
title_sort | diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582378/ https://www.ncbi.nlm.nih.gov/pubmed/37860526 http://dx.doi.org/10.1016/j.heliyon.2023.e20712 |
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