Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features

Abstract

Abstract. This paper presents an imaging-genomic pipeline to derive three-dimensional intra-tumor heterogeneity features from contrast-enhanced CT images and correlates them with gene mutation status. The pipeline has been demonstrated using CT scans of patients with clear cell renal cell carcinoma (ccRCC) from The Cancer Genome Atlas. About 15% of ccRCC cases reported have BRCA1-associated protein 1 (BAP1) gene alterations that are associated with high tumor grade and poor prognosis. We hypothesized that BAP1 mutation status can be detected using computationally derived image features. The molecular data pertaining to gene mutation status were obtained from the cBioPortal. Correlation of the image features with gene mutation status was assessed using the Mann-Whitney-Wilcoxon rank-sum test. We also used the random forests classifier in the Waikato Environment for Knowledge Analysis software to assess the predictive ability of the computationally derived image features to discriminate cases with BAP1 mutations for ccRCC. Receiver operating characteristics were obtained using a leave-one-out-cross-validation procedure. Our results show that our model can predict BAP1 mutation status with a high degree of sensitivity and specificity. This framework demonstrates a methodology for noninvasive disease biomarker detection from contrast-enhanced CT images.

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

“…Similarly, Kocak et al aimed to predict the presence of gene PBRM1 mutations by creating an ANN algorithm and ML-based TA from CMP images; they found that the former outperformed the latter, upholding 95% accuracy and 0.987 AUC for PBRM1 mutation status. This tumor suppressor gene's mutation was associated with advanced-stage and higher grade ccRCC, and it was also suggested to influence response rates to immune checkpoint inhibitors [53,54].…”

Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings

“…Regarding the previous works on imaging research of BAP1 mutation based on TCGA and TCIA data, Ghost et al found out that the prediction model based on nephrographic phase images performed the best with an area under curve (AUC) of 0.71 (34). However, they failed to make corresponding adjustments when the number of BAP1 mutations is too few.…”

A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas

“…The GLCMs also facilitate further statistical modeling and analysis in diagnostic oncology, which is otherwise hindered due to irregular shape and sizes of the tumor in the original image space. Current modeling approaches using GLCM focus on deriving textural features, which are summary statistics constructed using the GLCM entries ( Hitam et al, 2003 , Lee et al, 2015 , Ghosh et al, 2015 ). Many studies have also reported high correlation between the derived textural features which leads to over-fitting ( Albregtsen et al, 2008 , Zhang et al, 2008 , Kassner and Thornhill, 2010 ).…”