Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers Journal Article
| Authors: | Veeraraghavan, H.; Friedman, C. F.; DeLair, D. F.; Ninčević, J.; Himoto, Y.; Bruni, S. G.; Cappello, G.; Petkovska, I.; Nougaret, S.; Nikolovski, I.; Zehir, A.; Abu-Rustum, N. R.; Aghajanian, C.; Zamarin, D.; Cadoo, K. A.; Diaz, L. A. Jr; Leitao, M. M. Jr; Makker, V.; Soslow, R. A.; Mueller, J. J.; Weigelt, B.; Lakhman, Y. |
| Article Title: | 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 between 2014 and 2018 and preoperative CE-CT were included (n = 150). Molecular subtypes of EC were assigned using DNA polymerase epsilon (POLE) hotspot mutations and immunohistochemistry-based p53 and MMR protein expression. TMB was derived from sequencing, with > 15.5 mutations-per-megabase as a cut-point to define TMB-H tumors. After radiomic feature extraction and selection, radiomic features and clinical variables were processed with the recursive feature elimination random forest classifier. Classification models constructed using the training dataset (n = 105) were then validated on the holdout test dataset (n = 45). Integrated radiomic-clinical classification distinguished MMR-D from copy number (CN)-low-like and CN-high-like ECs with an area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI 0.58–0.91). The model further differentiated TMB-H from TMB-low (TMB-L) tumors with an AUROC of 0.87 (95% CI 0.73–0.95). Peritumoral-rim radiomic features were most relevant to both classifications (p ≤ 0.044). Radiomic analysis achieved moderate accuracy in identifying MMR-D and TMB-H ECs directly from CE-CT. Radiomics may provide an adjunct tool to molecular profiling, especially given its potential advantage in the setting of intratumor heterogeneity. © 2020, The Author(s). |
| Journal Title: | Scientific Reports |
| Volume: | 10 |
| ISSN: | 2045-2322 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2020-10-20 |
| Start Page: | 17769 |
| Language: | English |
| DOI: | 10.1038/s41598-020-72475-9 |
| PUBMED: | 33082371 |
| PROVIDER: | scopus |
| PMCID: | PMC7575573 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 November 2020 -- Source: Scopus |
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