Radiomics: from qualitative to quantitative imaging

Abstract

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

“…Next, CT data from pre-treatment and 2 weeks post-treatment PDX mouse tumour scans from DR_MOMP predicted combination-only responder (CRC0076) and FOLFOX alone responder (CRC0344) PDX models underwent additional radiomic analysis. Radiomics is a multistep process in which radiographic features based on shape, pixel intensities, and texture are extracted both from clinical and pre-clinical radiological images [ 35 , 36 , 37 ]. Radiomic analysis of CT data was undertaken due to the clinical relevance of CT in the CRC patient setting.…”

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

“…Indeed, a fast-growing literature shows the great promise of radiomics signatures (radiomics features and models) as a "virtual biopsy" to assist in cancer diagnosis and prognosis, treatment plan, patient stratification, and assessment of tumor response to therapy. The current status of CT-based radiomics in lung cancer has been well summarized in a recent collection of review articles [e.g., (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)].…”

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

“…Quantitative imaging analysis using radiomics is an approach to extract imaging features by high-throughput data mining on textures, shapes and intensities [18]. Radiomics has shown prognostic and predictive potential in multiple solid tumors [19,20] including GBM [21]. Furthermore, radiomics features have the potential to analyze the entire tumor and to identify intratumor molecular heterogeneity and underlying biological processes [22,23].…”