Abstract
Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.
Highlights
Radiomics is generally associated with artificial intelligence (AI)- and machine learning (ML)-specific medical literature and embraces studies involving all medical imaging modalities
Imaging biomarkers (IB), those potentially outsourced by RAD, may represent spatio-temporal heterogeneity in cancer [22,23,24,25,26]
Time, (b) cost-effective collection of evidence, (c) assessment of spatial heterogeneity at a larger extent compared to biopsies, (d) significance of disease hallmarks, (e) identification of drug targets and biomarker roadmaps, (f) merge between qualitative and quantitative features obtained from different modalities, and (g) facilitated clinical decision making between alternative treatments on the basis of Je. fPfeircsi.eMnetdm. 2o02n0i,t1o0r,i1n5g of disease progression
Summary
Radiomics is generally associated with artificial intelligence (AI)- and machine learning (ML)-specific medical literature and embraces studies involving all medical imaging modalities. CAD-MIM may first converge to a RAD feature space by providing evidence for clinical use through ad hoc image acquisition protocols and display methods, among others It might combine the quantitative measurements of the features into diagnostic scores in an attempt to extract disease-associated biomarkers. RAD requires deep assimilation by the actively involved research communities but appears inherently designed to add value in terms of precision This in turn translates into the need of establishing more objective data interpretation standards and benchmarks. Med. 2020, 10, 15 evaluations (size-based imaging endpoints) with very limited correlation with physiological outcome or early information about response to specific targeted therapies or pursuit of exploratory endpoints toward the design of the so-called integral cancer markers Such limitations are in part induced by infrastructural changes involving imaging technologies or combination of imaging modalities, and involve gaps in data collection strategies, digitalization, and other factors
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