Clinical Application of Radiomics in Oncology: Where Do We Stand?

Over the past decade, the medical imaging literature has been revolutionized by radiomics, which allows to extract huge amounts of quantitative data from magnetic resonance images and other imaging modalities for a wide variety of diagnostic and prognostic purposes, particularly in oncology. Radiomics can capture imaging features of tumors that go beyond the conventional visual analysis and well correlate with histologic types, genetic mutations, response to treatment, and patient survival.1 Despite promising results achieved by several research studies in recent years, the translation of radiomics into clinical practice has been rather slow. For example, only a small proportion of FDA-approved medical devices based on artificial intelligence involve radiomics.2 As for clinical trials, a search on clinicaltrail.gov with the keywords "oncology" and "radiomics" yields 238 results, of which 40 completed, but only one reported the results of the study.3 Besides the inherent slowness of technological translation that is characteristic of any scientific discipline, several specific reasons for the scarcity of clinical applications of radiomic research could be identified. These include poor standardization of imaging protocols and radiomic analysis, a lack of proper external validation of trained machine-learning models, and a missing focus on the interpretability and biological meaning of identified radiomics features.4 From a technical perspective, radiomics encompasses multiple steps, including imaging acquisition and preprocessing, segmentation, feature extraction and selection, modeling, and evaluation. This complexity has consequences on the generalizability of individual studies, because of the different approaches that could be followed at each step, resulting in heterogeneous and sometime conflicting results.5, 6 Collaborative efforts are underway to address the challenges and the sources of bias associated with radiomics and have been translated into updated reference standards, reporting guidelines and quality assessment tools.6-8 As radiomics is a rapidly developing field, it is natural that such guidelines promoted by scientific societies and international collaborations remain current and aligned with advancements in radiomics technology. Validation of radiomic models on an external dataset is a milestone for testing their clinical utility. The availability of more high-quality datasets from institutions for use as test sets for radiomic models facilitates greater confidence and validation of radiomic findings.9 Inspired by findability, accessibility, interoperability, and reusability principles and Open Science initiatives, most recent radiomic guidelines encourage research teams to share the codes and data produced during their studies, with the aim of improving the transparency and reproducibility of the results. The integration of radiomics into the existing infrastructure of diagnosis and reporting is another essential step for maximizing its utility and impact in real-world medical settings. It is imperative to ensure accessibility to end-users, particularly radiologists and oncologists. This requires the development of specialized analysis software incorporated into clinical workflows that can interact with reporting systems.10 Imaging features capture macroscopic characteristics of the tumor, but the link with histological, genetic or molecular features is not always assured. This can hamper the interpretability of radiomic models. Clinical translation of radiomic studies could succeed by bridging the gap between such "radiomic signature" and the underlying pathology. To achieve this, establishing a tissue-based pathological validation of a radiomic model may lead to a deeper understanding of the relationship between medical imaging data and pathological features of the tumor.4 Radiomics provides radio-phenotypic characteristics which are useful for individual patient management. However, in order to become part of personalized medicine like other omics have done (e.g. genomics), prospective multicenter radiomic studies that incorporate the radiomic signatures into clinical trials as primary or secondary endpoints should be conducted, following established processes of standardization and validation.2, 4-6 To assess the clinical utility of a radiomic model, its performance in the intended clinical setting should be measured and the risk-benefit balance for the patient should be evaluated, especially because it may involve changes in clinical decisions. For a radiomic model to be clinically useful, it must lead to either superior clinical outcomes compared with standard options, noninferior outcomes with reduced risks, or avoidance of unnecessary or ineffective treatments. That said, clinical utility of a radiomic model is absent if it only identifies statistically different patient groups without impacting recommended clinical management.2 Another crucial aspect for advancing the implementation of radiomics in clinical practice is the education of medical students and radiologists in informatics, coupled with collaboration with computer scientists. Universities and research institutes play a pivotal role in driving this change by offering interdisciplinary programs and fostering collaborative research initiatives. Incorporating informatics education into medical and radiology curricula equips future healthcare professionals with the necessary skills to effectively navigate and analyze radiomic data. While radiomics holds immense promise for revolutionizing personalized patient care, its effective integration into medical imaging and clinical practice necessitates concerted efforts to address technical, standardization, reproducibility, and interpretability challenges. By fostering collaboration, transparency, and validation, radiomics can pave the way for more precise diagnosis, treatment, and management of diseases, ultimately improving patient outcomes, and advancing healthcare delivery.