Background/Aim: To investigate whether a radiomic machine learning (ML) approach
employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able …

Introduction In this study, we investigate the role of radiomics for prediction of overall survival
(OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC …

Objectives: To evaluate the performance of radiomics features extracted from planning target
volume (PTV) and gross tumor volume (GTV) in the prediction of the death prognosis and …

Objectives To develop and compare several machine learning models to predict occult
cervical lymph node (LN) metastasis in early-stage oral tongue squamous cell cancer …

Background. Oropharyngeal squamous cell carcinoma (OPSCC) is one of the fastest
growing disease sites of head and neck cancers. A recently described radiomic signature …

Purpose To determine the performance of CT-based radiomic features for noninvasive
prediction of histopathologic features of tumor grade, extracapsular spread, perineural …

Radiomics is one such “big data” approach that applies advanced image refining/data
characterization algorithms to generate imaging features that can quantitatively classify …

Background: Radiomics has been widely used to non-invasively mine quantitative
information from medical images and could potentially predict tumor phenotypes. Pathologic …

Background and purpose To determine the neck management of tongue cancer, this study
attempted to construct an artificial neural network (ANN)-assisted model based on computed …

Introduction “Radiomics” extracts and mines a large number of medical imaging features in a
non-invasive and cost-effective way. The underlying assumption of radiomics is that these …