A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer

Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage (pN-stage) is commonly performed by pathologists detecting metastasi...

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Bibliographic Details
Published inarXiv.org
Main Authors Lee, Byungjae, Paeng, Kyunghyun
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.05.2018
Subjects
Artificial neural networks
Breast cancer
Cancer
Diagnostic systems
Image classification
Image detection
Lymph
Lymphatic system
Medical imaging
Medical prognosis
Metastasis
Predictions
Workload
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Summary:Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage (pN-stage) is commonly performed by pathologists detecting metastasis in histological slides. However, this diagnostic procedure is prone to misinterpretation and would normally require extensive time by pathologists because of the sheer volume of data that needs a thorough review. Automated detection of lymph node metastasis and pN-stage prediction has a great potential to reduce their workload and help the pathologist. Recent advances in convolutional neural networks (CNN) have shown significant improvements in histological slide analysis, but accuracy is not optimized because of the difficulty in the handling of gigapixel images. In this paper, we propose a robust method for metastasis detection and pN-stage classification in breast cancer from multiple gigapixel pathology images in an effective way. pN-stage is predicted by combining patch-level CNN based metastasis detector and slide-level lymph node classifier. The proposed framework achieves a state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17 dataset, outperforming the previous winning method of the Camelyon17 challenge.
ISSN:2331-8422