The usage of medical imaging is swiftly evolving from being primarily a diagnostic tool to playing a central role in the context of personalized medicine with the advent of radiomics in cancer patients [1]. Radiomics refers to the qualitative and quantitative extraction of data from clinical images and the methodology used to convert imaging features in a way that supports decision-making [2]. It has mainly been used in the field of oncology for outcome prediction, risk assessment, treatment response, tumor staging, and assessment of cancer genetics, also known as tumor phenotyping [3, 4].

Radiomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology, and this relationship can be revealed via quantitative image analysis that offers information on the disease microenvironment and the disease state...

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The systematic review followed the methods described in a published protocol in the PROSPERO register (ID 310058). All original research with the words ‘radiomics’ and ‘genomics’ in English and performed in humans up to 31^st of January 2022 were identified on Medline and Embase.

Studies were included if: (1) they were radiomics studies with complex feature extraction performed in any imaging technique including MRI, CT, US, PET-CT but not limited to; (2) relevant clinical outcome was assessed including survival, treatment response, prognosis, or risk-stratification; (3) biological validation of the clinical outcomes and/or radiomic phenotype with genetic data; and (4) the study was performed in urogenital cancer patients (Figure 2). 

Studies meeting the inclusion criteria subsequently underwent...

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A total of 10 studies were identified and eight met the inclusion criteria and were included in the systematic review (Figure 3). All of them were published in the last four years. As part of the quality analysis of the studies, we identified limitations and biases (Figure 4).

Clear cell renal cell carcinoma (ccRCC) was the only represented type of renal cancer and was the focus point of six of the included studies. They were all based on radiomic features extracted from CT images and all of them made use of The Cancer Genome Atlas and/or The Cancer Image Archive for partial or total data collection. The ccRCC studies generally presented large study populations (214–520, mean 373) and low to moderate RQS (6–15, mean 12). Five out of six studies had external validation. The most frequent limitation of the ccRCC studies was verification bias (4 of 6...

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We present an emerging association between radiological features and genomic / molecular expression in urogenital cancers that can be used to explain underlying disease mechanisms and enhance prognostic assessment and treatment response. While genomics and radiomics have been studied individually, the integration of genomic and radiomic data into multi-omics-based machine learning models could provide new scope for precision oncology, which would aid a more comprehensive understanding and management of urogenital cancers. In clear cell renal cell carcinoma, radiomic features show a promising ability to predict genetic biomarkers and immunotherapeutic response. 

REFERENCES

[1] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762....

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