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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 1,103 - 1,111 (9page)
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Purpose Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin?stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods A total of 297 digital slides were obtained from frozen SLN sections, which include post?neoadjuvant cases (n=144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). Results The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.
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참고문헌 (25)
참고문헌 신청
Williams BJ / 2017 / Future-proofing pathology : the case for clinical adoption of digital pathology / J Clin Pathol 70:1010~1018
Wang S / 2018 / Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome / Sci Rep 8:10393
Litjens G / 2016 / Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis / Sci Rep 6:26286
Ehteshami Bejnordi B / 2017 / Diagnostic assess-ment of deep learning algorithms for detection of lymph node metastases in women with breast cancer / JAMA 318:2199~2210
Kasper DL / 2015 / Harrison’s principles of internal medicine / McGraw-Hill
Wang S / 2018 / Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome / Sci Rep 8:10393
Litjens G / 2016 / Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis / Sci Rep 6:26286
Ehteshami Bejnordi B / 2017 / Diagnostic assess-ment of deep learning algorithms for detection of lymph node metastases in women with breast cancer / JAMA 318:2199~2210
Kasper DL / 2015 / Harrison’s principles of internal medicine / McGraw-Hill
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