Leveraging Radiomics for Computational Inference Advances in Oncology

Dear Colleagues,

A wide spectrum of Artificial Intelligence and Machine Learning solutions is currently available to provide access to image-driven data critical to fields such as oncology. These developments have fueled the radiomics field, providing new complementary information for cancer diagnosis therapy, prognosis. The use of predictive learning models has greatly facilitated the combination of radiomic detections with other informative markers to the benefit of clinical decision making. 

Despite the promising results emerging from radiomics, some limitations remain to be addressed in order to optimize the inference processes in oncology. The selected features are required to satisfy criteria such as i) repeatability with regard to acquisitions and used parameters, within the same imaging modality, ii) reproducibility of results across changing parameters, depending on the measurement system of choice, iii) transferability of evidence over patient groups, and iv) generalizability of findings across cancer types. 

Advancing in all such directions is crucial for allowing better standardization in radiomics studies. The Image Biomarker Standardization Initiative (IBSI) provides guidelines centered on data and aimed at establishing benchmarks for the processing of image features. Feature-based approaches require that model generation ultimately prove its reliability in clinical practice, although the computational workflow may be complex and hardly interpretable in the outcomes. Stepping from black-box approaches to graph-based ones or other types of representation frameworks needs to be further developed. This Special Issue invites proposals addressing new solutions in view of their potential integrability within inference approaches supporting medical decisions.

Dr. Enrico Capobianco
Dr. Marco Diego Dominietto
Guest Editors

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• oncology
• radiomics
• inference
• computational approaches
• clinical decision processes