These features are biomarkers with prognostic and predictive value, which provide a characterization of tumor phenotypes in a non-invasive manner.
The clinical application of radiomics is hampered by challenges such as lack of image acquisition and analysis standardization. Textural features extracted from computed tomography CTmagnetic resonance imaging MRI and positron emission tomography-computed tomography PET-CT images of patients diagnosed with head and neck cancers can be used in the pre-therapeutic evaluation of the response to multimodal chemo-radiotherapy.
For patients with positive HPV-oropharyngeal cancers, the correlation of the radiomic textural features from the tumor with p16 values from the pathological sample can identify tumor specific signatures in CT imaging, an entity with favorable prognosis and a better response to chemo-radiotherapy.
Pretreatment contrast CT-scans were extracted and radiomics analysis of gross tumor volume were performed using MaZda package apart from MaZda software containing B11 program for texture analysis and visualization. Data set was randomly divided into a training dataset and a test dataset and machine learning algorithms were applied to identify a textural radiomic signature.
Radiomic texture analysis and machine learning algorithms demonstrate a predictive potential related to the capability of stratification for subclasses of platinum-chemotherapy resistance and radioresistant head and neck cancers requiring an intensification of multimodal treatment.
Keywords: radiomics, head and neck oncology, machine learning, texture, features.