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Neoplastic and Non-neoplastic Acute Intracerebral Hemorrhage in CT Brain Scans: Machine Learning-Based Prediction Using Radiomic Image Features by Nawabi, Jawed; Kniep, Helge; Kabiri, Reza; Broocks, Gabriel; Faizy, Tobias D.; Thaler, Christian; Schön, Gerhard; Fiehler, Jens; Hanning, Uta is a Medicine article available to read on EtoBox.

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**Background:** Early differentiation of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) can be difficult in initial radiological evaluation, especially for extensive ICHs. The aim of this study was to evaluate the potential of a machine learning-based prediction of etiology for acute ICHs based on quantitative radiomic image features extracted from initial non-contrast-enhanced computed tomography (NECT) brain scans. **Methods:** The analysis included NECT brain scans from 77 patients with acute ICH (__n__ = 50 non-neoplastic, __n__ = 27 neoplastic). Radiomic features including shape, histogram, and texture markers were extracted from non-, wavelet-, and log-sigma-filtered images using regions of interest of ICH and perihematomal edema (PHE). Six thousand and ninety quantitative predictors were evaluated utilizing random forest algorithms with five-fold model-external cross-validation. Model stability was assessed through comparative analysis of 10 randomly drawn cross-validation sets. Classifier performance was compared with predictions of two radiologists employing the Matthews correlation coefficient (MCC). **Results:** The receiver operating characteristic (ROC) a

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Author

Nawabi, Jawed; Kniep, Helge; Kabiri, Reza; Broocks, Gabriel; Faizy, Tobias D.; Thaler, Christian; Schön, Gerhard; Fiehler, Jens; Hanning, Uta

Publisher

Frontiers; Frontiers Media SA; Frontiers Media S.A.; Lausanne: Frontiers Media S.A., 2010- (ISSN 1664-2295)

Published

2020

Language

EN

Field

Medicine (Health Sciences)