Preoperative and Non-Invasive Approach for Radiomic Biomarker-Based Prediction of Malignancy Grades in Patients with Parotid Gland Cancer in Magnetic Resonance Images

We have investigated a non-invasive approach for predicting parotid gland cancer (PGC) malignancy grade based on radiomic biomarkers in preoperative magnetic resonance (pMR) images using six conventional machine learning (cML) and five deep learning (DL) algorithms. 39 patients were divided into 70%...

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Veröffentlicht in:	Medical Imaging and Information Sciences 2020/12/23, Vol.37(4), pp.66-74
Hauptverfasser:	KAMEZAWA, Hidemi, ARIMURA, Hidetaka, YASUMATSU, Ryuji, NINOMIYA, Kenta, HASEAI, Shu
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Biomarkers Cancer Cytology Datasets Deep learning Diagnostic systems Feature extraction Learning algorithms Machine learning Magnetic resonance imaging Malignancy malignancy grade Medical imaging Model accuracy Parotid gland parotid gland cancer Prediction models Predictions Radiomics Resonance Salivary glands Training Transfer learning
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