@inproceedings{9986820603c543c7b653477d87bf1958,
title = "Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: A radio-pathomic study",
abstract = "Shaped-based descriptors from Computed Tomography (CT) scans and whole slide digital pathology images were used to differentiate the two major histopathological subtypes of non-small-cell lung cancer (NSCLC). Our two hypotheses are 1) Encoding information on local heterogeneity will augment the model's classification capabilities 2) Shape-based biomarkers from radiology and pathology can complement each other. Shape features were extracted from the tumor map from pathology and radiology images. In pathology, tumor-microenvironment features were encoded by clustering the tumor map into phenotype maps. These features performed better than the features from whole tumor map. Integration of radio-pathomics performed best, achieving 0.802 AUC.",
keywords = "Cluster phenotype map, Radio-Pathomic Study, Radiomic features, Shape features",
author = "Saarthak Kapse and Rajarsi Gupta and Prateek Prasanna",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE.; Medical Imaging 2022: Digital and Computational Pathology ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2613167",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, \{John E.\} and Ward, \{Aaron D.\} and Levenson, \{Richard M.\}",
booktitle = "Medical Imaging 2022",
}