Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system

Presence of Intratumoral Calcifications and Vasculature Is Associated With Poor Overall Survival in Clear Cell Renal Cell Carcinoma

PURPOSE

The objective of this study was to explore the prognostic significance of the preoperative computed tomography (CT) features in clear cell renal cell carcinoma.

PATIENTS AND METHODS

The clinical data and CT data from 210 patients (1 grade 1, 84 grade 2, 92 grade 3, and 32 grade 4) generated with The Cancer Imaging Archive were reviewed. Overall survival was assessed using Kaplan-Meyer analysis. The relationship between CT features and survivals were evaluated using univariate and multivariable Cox regression analysis.

RESULTS

The follow-up occurred between 13 and 3989 days (median, 1405 days; mean, 1434 days).On univariate Cox regressions, 4 preoperative CT features (intratumoral calcifications: yes vs no hazard ratio [HR], 2.054; 95% confidence interval [CI], 1.231-3.428; renal vein invasion: yes vs no HR, 2.013; 95% CI, 1.218-3.328; collecting system invasion: yes vs no HR, 2.139; 95% CI, 1.286-3.558; gross appearance of intratumoral vasculature: yes vs no HR, 2.385; 95% CI, 1.454-3.915) were significantly associated with overall survival (all P < 0.05). On multivariable Cox regression analysis, predictors of mortality in clear cell renal cell carcinoma were the presence of intratumoral calcifications (HR, 1.718; 95% CI, 1.014-2.911; P = 0.044) and gross appearance of intratumoral vasculature (HR, 2.137; 95% CI, 1.284-3.557; P = 0.003).

CONCLUSIONS

Presence of intratumoral calcifications and vasculature can be potential prognostic features to screen patients for unfavorable prognosis.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.