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KMID : 0939920230550020513
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대한암학회지
2023 Volume.55 No. 2 p.513 ~ p.522
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Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer
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Kim Young-Gon
Song In-Hye
Cho Seung-Yeon
Kim Sung-Chul
Kim Mi-Lim
Ahn Soo-Min
Lee Hyun-Na
Yang Dong-Hyun
Kim Nam-Kug
Kim Sung-Wan
Kim Tae-Woo
Kim Dae-Young
Choi Jong-Hyeon
Lee Ki-Sun
Ma Min-Uk
Jo Min-Ki
Park So-Yeon
Gong Gyung-Yub
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Abstract
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Purpose Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin?stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients.
Materials and Methods A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study.
Results The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis.
Conclusion In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.
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KEYWORD
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Breast neoplasms, Deep learning, Frozen sections, Neoplasm metastasis, Sentinel lymph node, Metastasis, Classification
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