Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images

• URL: http://arxiv.org/abs/2012.04878v1
• Date: Wed, 9 Dec 2020 05:56:41 GMT
• Title: Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images
• Authors: Esteban Fern\'andez Morales and Cong Zhang and Guanghua Xiao and Chul Moon and Qiwei Li
• Abstract summary: Digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. Recent developments in deep-learning methods have enabled us to automatically detect and characterize the tumor regions in pathology images at large scale.
• Score: 2.864559331994679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to automatically detect and characterize the tumor regions in pathology images at large scale. From each identified tumor region, we extracted 30 well-defined descriptors that quantify its shape, geometry, and topology. We demonstrated how those descriptor features were associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial (n=143). Besides, a descriptor-based prognostic model was developed and validated in an independent patient cohort from The Cancer Genome Atlas Program program (n=318). This study proposes new insights into the relationship between tumor shape, geometrical, and topological features and patient prognosis. We provide software in the form of R code on GitHub: https://github.com/estfernandez/Slide_Image_Segmentation_and_Extraction.

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