C. van Dooijeweert, R. N. Flach, N. D. ter Hoeve, C. P. H. Vreuls, R. Goldschmeding, J. E. Freund, P. Pham, T. Q. Nguyen, E. van der Wall, G. W. J. Frederix, N. Stathonikos, P. J. van Diest
{"title":"人工智能辅助检测前哨淋巴结乳腺癌转移的临床实施:CONFIDENT-B 单中心非随机临床试验。","authors":"C. van Dooijeweert, R. N. Flach, N. D. ter Hoeve, C. P. H. Vreuls, R. Goldschmeding, J. E. Freund, P. Pham, T. Q. Nguyen, E. van der Wall, G. W. J. Frederix, N. Stathonikos, P. J. van Diest","doi":"10.1038/s43018-024-00788-z","DOIUrl":null,"url":null,"abstract":"Pathologists’ assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and allocated biweekly to the intervention arm (n = 100) or control arm (n = 90). In both arms, digital whole-slide images of hematoxylin–eosin sections of SN specimens were assessed by an expert pathologist, who was assisted by the ‘Metastasis Detection’ app (Visiopharm) in the intervention arm. Our primary endpoint showed a significantly reduced adjusted relative risk of IHC use (0.680, 95% confidence interval: 0.347–0.878) for AI-assisted pathologists, with subsequent cost savings of ~3,000 €. Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates the safety and potential for cost and time savings of AI assistance. Van Dooijeweert et al. conducted a prospective study on the clinical implementation of artificial-intelligence-assisted detection of sentinel lymph node metastasis in persons with breast cancer and report on its effects, including on time and cost.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00788-z.pdf","citationCount":"0","resultStr":"{\"title\":\"Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial\",\"authors\":\"C. van Dooijeweert, R. N. Flach, N. D. ter Hoeve, C. P. H. Vreuls, R. Goldschmeding, J. E. Freund, P. Pham, T. Q. Nguyen, E. van der Wall, G. W. J. Frederix, N. Stathonikos, P. 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Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial
Pathologists’ assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and allocated biweekly to the intervention arm (n = 100) or control arm (n = 90). In both arms, digital whole-slide images of hematoxylin–eosin sections of SN specimens were assessed by an expert pathologist, who was assisted by the ‘Metastasis Detection’ app (Visiopharm) in the intervention arm. Our primary endpoint showed a significantly reduced adjusted relative risk of IHC use (0.680, 95% confidence interval: 0.347–0.878) for AI-assisted pathologists, with subsequent cost savings of ~3,000 €. Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates the safety and potential for cost and time savings of AI assistance. Van Dooijeweert et al. conducted a prospective study on the clinical implementation of artificial-intelligence-assisted detection of sentinel lymph node metastasis in persons with breast cancer and report on its effects, including on time and cost.
期刊介绍:
Cancer is a devastating disease responsible for millions of deaths worldwide. However, many of these deaths could be prevented with improved prevention and treatment strategies. To achieve this, it is crucial to focus on accurate diagnosis, effective treatment methods, and understanding the socioeconomic factors that influence cancer rates.
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