Supporting data for “Radiomics, machine learning, deep learning in oncologic imaging: applications in nasopharyngeal carcinoma and esophageal squamous cell carcinoma”.

The dataset is the supporting data for thesis titled "Radiomics, machine learning, deep learning in oncologic imaging: applications in nasopharyngeal carcinoma and esophageal squamous cell carcinoma". esophageal squamous cell carcinoma (ESCC) have for years been the leading causes of death in Hong Kong. In current clinical practice, non-invasive imaging techniques are commonly used for these cancer types. The application of radiomics, machine learning, and deep learning has provided a novel scope for advanced imaging analysis for cancer patients.

The uploaded data records include the code and example data of the patient cohorts in the thesis. This code includes feature extraction from Radiomics and pretrained Deep Convolutional Neural Network model and then further model training and validation by machine learning approaches.