A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.

Supervised machine learning methods are usually used to build a custom model for disease diagnosis and auxiliary prognosis in radiomics studies. A classical machine learning pipeline involves a series of steps and multiple algorithms, which leads to a great challenge to find an appropriate combination of algorithms and an optimal hyper-parameter set for radiomics model building. We developed a freely available software package for radiomics model building. It can be used to lesion labeling, feature extraction, feature selection, classifier training and statistic result visualization. This software provides a user-friendly graphic interface and flexible IOs for radiologists and researchers to automatically develop radiomics models. Moreover, this software can extract features from corresponding lesion regions in multi-modality images, which is labeled by semi-automatic or full-automatic segmentation algorithms. It is designed in a loosely coupled architecture, programmed with Qt, VTK, and Python. In order to evaluate the availability and effectiveness of the software, we utilized it to build a CT-based radiomics model containing peritumoral features for malignancy grading of cell renal cell carcinoma. The final model got a good performance of grading study with AUC=0.848 on independent validation dataset.Clinical Relevance-the developed provides convenient and powerful toolboxes to build radiomics models for radiologists and researchers on clinical studies.