Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers

Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers

Abstract To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massively parallel sequencing of primary ECs bet...

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Bibliographic Details
Main Authors: Harini Veeraraghavan, Claire F. Friedman, Deborah F. DeLair, Josip Ninčević, Yuki Himoto, Silvio G. Bruni, Giovanni Cappello, Iva Petkovska, Stephanie Nougaret, Ines Nikolovski, Ahmet Zehir, Nadeem R. Abu-Rustum, Carol Aghajanian, Dmitriy Zamarin, Karen A. Cadoo, Luis A. Diaz, Mario M. Leitao, Vicky Makker, Robert A. Soslow, Jennifer J. Mueller, Britta Weigelt, Yulia Lakhman
Format: Article
Language:English
Published: Nature Portfolio 2020-10-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-020-72475-9

Internet

https://doi.org/10.1038/s41598-020-72475-9

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