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Radiogenomics influence on the future of prostate cancer risk stratification.

ABSTRACT: In an era of powerful computing tools, radiogenomics provides a personalized, precise approach to the detection and diagnosis in patients with prostate cancer (PCa). Radiomics data are obtained through artificial intelligence (AI) and neural networks that analyze imaging, usually MRI, to assess statistical, geometrical, and textural features of images to provide quantitative data of shape, heterogeneity, and intensity of tumors. Genomics involves assessing the genomic markers that are present from tumor biopsies. In this article, we separately investigate the current landscape of radiomics and genomics within the realm of PCa and discuss the integration and validity of both into radiogenomics using the data from three papers on the topic. We also conducted a clinical trials search using the NIH's database, where we found two relevant actively recruiting studies. Although there is more research needed to be done on radiogenomics to fully adopt it as a viable diagnosis tool, its potential by providing personalized data regarding each tumor cannot be overlooked as it may be the future of PCa risk-stratification techniques.

SUBMITTER: Banerjee V

PROVIDER: S-EPMC9490455 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature

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Radiogenomics influence on the future of prostate cancer risk stratification.

Banerjee Vinayak V Wang Shu S Drescher Max M Russell Ryan R Siddiqui M Minhaj MM

Therapeutic advances in urology 20220101

In an era of powerful computing tools, radiogenomics provides a personalized, precise approach to the detection and diagnosis in patients with prostate cancer (PCa). Radiomics data are obtained through artificial intelligence (AI) and neural networks that analyze imaging, usually MRI, to assess statistical, geometrical, and textural features of images to provide quantitative data of shape, heterogeneity, and intensity of tumors. Genomics involves assessing the genomic markers that are present fr ...[more]

PMID: 36160762

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Project description:Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated.

Project description:Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

Dataset Information

Radiogenomics influence on the future of prostate cancer risk stratification.

Publications

Radiogenomics influence on the future of prostate cancer risk stratification.

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