Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (25)
- Williams BJ, "Future-proofing pathology : the case for clinical adoption of digital pathology", J Clin Pathol, 70, pp.1010~1018 (2017)
- Wang S, "Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome", Sci Rep, 8, pp.10393~ (2018)
- Litjens G, "Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis", Sci Rep, 6, pp.26286~ (2016)
- Ehteshami Bejnordi B, "Diagnostic assess-ment of deep learning algorithms for detection of lymph node metastases in women with breast cancer", JAMA, 318, pp.2199~2210 (2017)
- Kasper DL, "Harrison’s principles of internal medicine" (2015), McGraw-Hill
- Hayes SC, "Lymphedema after breast cancer : incidence, risk factors, and effect on upper body function", J Clin Oncol, 26, pp.3536~3542 (2008)
- Fleissig A, "Post-operative arm morbidity and quality of life : results of the ALMANAC randomised trial comparing sentinel node biopsy with standard axillary treatment in the management of patients with early breast cancer", Breast Cancer Res Treat, 95, pp.279~293 (2006)
- Lyman GH, "Sentinel lymph node biopsy for patients with early-stage breast cancer : American Society of Clinical Oncology clinical practice guideline update", J Clin Oncol, 32, pp.1365~1383 (2014)
- Manca G, "Sentinel lymph node biopsy in breast cancer : indications, contraindications, and controversies", Clin Nucl Med, 41, pp.126~133 (2016)
- Galimberti V, "Axillary dissection versus no axillary dissection in pati-ents with breast cancer and sentinel-node micrometastases(IBCSG 23-01) : 10-year follow-up of a randomised, controlled phase 3 trial", Lancet Oncol, 19, pp.1385~1393 (2018)
- Giuliano AE, "Effect of axillary dissection vs no axi-llary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis : the ACOSOG Z0011(Alliance)randomized clinical trial", JAMA, 318, pp.918~926 (2017)
- Wang J, "Is surgical axillary staging necessary in women with T1 breast cancer who are treated with breast-conserving therapy?", Cancer Commun(Lond), 39, pp.25~ (2019)
- Donker M, "Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer(EORTC 10981-22023 AMAROS) : a randomised, multicentre, open-label, phase 3 non-inferiority trial", Lancet Oncol, 15, pp.1303~1310 (2014)
- Celebioglu F, "Intraoperative sentinel lymph node examination by frozen section, immunohistochemistry and imprint cytology during breast surgery : a prospective study", Eur J Cancer, 42, pp.617~620 (2006)
- Chen Y, "Frozen-section checklist implementation improves quality and pati-ent safety", Am J Clin Pathol, 151, pp.607~612 (2019)
- Bandi P, "From detection of individual metastases to classification of lymph node status at the patient level : the CAMELYON17 challenge", IEEE Trans Med Imaging, 38, pp.550~560 (2019)
- Honkoop AH, "Effects of chemotherapy on pathologic and biologic characteristics of locally advanced breast cancer", Am J Clin Pathol, 107, pp.211~218 (1997)
- Szegedy C, "Rethinking the inception architecture for computer vision", 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
- Ronneberger O, "U-net: convolutional networks for biomedical image segmentation", International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.234~241 (2015), Springer
- Liaw A, "Classification and regression by random forest", R News, 2, pp.18~22 (2002)
- Youden WJ, "Index for rating diagnostic tests", Cancer, 3, pp.32~35 (1950)
- Laurent-Bellue A, "Four-year experience of digital slide telepathology for intraoperative frozen section consultations in a two-site French academic department of pathology", Am J Clin Pathol, 154, pp.414~423 (2020)
- Menter T, "Intraoperative frozen section consultation by remote whole-slide imaging analysis : validation and comparison to robotic remo-te microscopy", J Clin Pathol, 73, pp.350~352 (2020)
- Akay CL, "Factors impacting the accuracy of intra-ope-rative evaluation of sentinel lymph nodes in breast cancer", Breast J, 24, pp.28~34 (2018)
- Houpu Y, "Use of Memorial Sloan Kettering Cancer Center nomogram to guide intraoperative sentinel lymph node frozen sections in pati-ents with early breast cancer", J Surg Oncol, 120, pp.587~592 (2019)