Computational Radiomics System to Decode the Radiographic Phenotype

Abstract

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.

Conflict of interest statement

Conflicts of interest: None

©2017 American Association for Cancer Research.

Figures

Figure 1

A Overview figure of the process of PyRadiomics. First, medical images are segmented. Second, features are extracted using the PyRadiomics platform, and third, features are analyzed for associations with clinical or biologic factors. B Stability of radiomics features for variation in manual segmentations by expert radiologists. C Heatmap showing expression values of radiomics features (rows) of 429 lesions (columns). Note the four subtypes that could be identified from the expression values and their associations with malignancy. D Area under curve (AUC) showing the performance of the multivariate biomarker to predict malignancy of nodules.