NEWS 2019

Radiomics Analysis for Clinical Decision Support in Nuclear Medicine
Radiomics – the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. A group of researchers including PREDICT ESR Sergey Primakov reviewed the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. They also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.

An article detailing this work was published in September 2019 in Seminars in Nuclear Medicine.

Radiomic features derived from FDG PET are independently associated with LC in patients with NSCLC undergoing SBRT and could therefore be combined in an accurate predictive model
Non‐small cell cancer (NSCLC) is usually associated with a poor prognosis. Over the past two decades, technological developments in target delineation, motion management, conformal treatment planning and daily image guidance have allowed the development of stereotactic body radiation therapy (SBRT). SBRT uses stereotactic targeting to facilitate the accurate delivery of a short course of high‐dose radiation to the target. SBRT has demonstrated high local control (LC) rates (85‐90%), comparable to those obtained with surgery in multiple prospective trials and is now a guideline‐recommended treatment for patients with early stage NSCLC who are medically unfit or unwilling to undergo surgery. Approximately 16% of patients present with early‐stage cT1‐T2 N0 disease at diagnosis.  Amongst these patients, therapeutic results are nonetheless highly variable, and new predictive factors of response to SBRT are needed to better individualize treatment.  Radiomic features are handcrafted metrics used to quantify tumour intensity, shape and heterogeneity, some of which have been shown to reflect intratumoural histopathological properties and to predict patients’ outcome in several pathologies including NSCLC when extracted from FDG PET, CT, or both.  In this retrospective study a group of researchers amongs whom PREDICT ESR Ronrick Da‐ano found that two radiomic features derived from FDG PET were independently associated with LC in patients with NSCLC undergoing SBRT and could be combined in an accurate predictive model. This model could provide local relapse‐related information and could be helpful in clinical decision‐making. 

An article detailing this discovery was published on November 15 in the Journal of Nuclear Medicine.