Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations between CT Imaging Features and Mutations

Abstract

Purpose To investigate associations between computed tomography (CT) features of clear-cell renal cell carcinoma (ccRCC) and mutations in VHL, PBRM1, SETD2, KDM5C or BAP1 genes. Materials and Methods The institutional review board approved this retrospective, hypotheses-generating study of 233 patients with ccRCC and waived the informed consent requirement. The study was HIPAA compliant. Three radiologists independently reviewed pre-treatment CT images of all ccRCC without knowledge of their genomic profile. One radiologist measured largest diameter and enhancement parameters of each ccRCC. Associations between CT features and mutations in VHL, PBRM1, SETD2, KDM5C and BAP1 genes were tested using Fisher’s exact tests. Associations between mutations and size/enhancement were assessed using independent t-tests. Interreader agreements were calculated using Fleiss’ Kappa. Results Mutation frequencies among ccRCC were: VHL, 53.2% (124/233); PBRM1, 28.8% (67/233); SETD2, 7.3% (17/233); KDM5C, 6.9% (16/233); BAP1, 6% (14/233). Mutations of VHL were significantly associated with well-defined tumor margins (p=0.013), nodular tumor enhancement (p=0.021) and gross appearance of intratumoral vascularity (p=0.018). Mutations of KDM5C and BAP1 were significantly associated with evidence of renal vein invasion (p=0.022 and 0.046, respectively). The genotype of solid ccRCC differed significantly from the one of multicystic ccRCC. While mutations of SETD2, KDM5C and BAP1 were absent in multicystic ccRCC, mutations of VHL (p=0.016) and PBRM1 (p=0.017) were significantly more common among solid ccRCC. Interreader agreements for CT feature assessments ranged from substantial to excellent (κ=0.791–0.912). Conclusion This preliminary Radiogenomics analysis of ccRCC revealed associations between CT features and underlying mutations which warrant further investigation and validation.

Imaging genomics in cancer research: limitations and promises

“…Five studies used data from TCGA/ TCIA; 45,46,48,49,57 one study used multi-institutional data; 50 and one study combined both institutional data and data from TCGA/TCIA. 44 The number of patients ranged from 25 to 539 with a median of 75. Only two studies had a validation data set.…”

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

“…1,[8][9][10] In this study, we explore the relationship between integrative radiogenomic features and several important clinical variables including pathology stage, lymph node metastasis, and molecular receptor stats. Innovatively, we assess whether combining genomic and radiomic features can improve the prediction of clinical outcomes.…”

Nichtinvasive Phänotypisierung von Nierentumoren – aktueller Stand

“…Zusammenhänge zwischen CT-Features wie Nekrosen oder Verkalkungen mit genetischen Faktoren konnten nachgewiesen werden, bedürfen allerdings noch weiterer Erforschung [19]. Ein besonders ausgeprägtes arterielles Enhancement kann das klarzellige RCC von anderen Subtypen unterscheiden [6,46], wobei höhergradige Tumoren in der kortikomedullären Phase ein geringeres Enhancement zeigen, als niedriggradigere [45].…”