Purpose To investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC). Materials and methods This multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34–79) with ccRCC examined with CT in 81 patients, MRI in 19, and both CT and MRI in three; images were downloaded from The Cancer Imaging Archive, an NCI-funded project for genome-mapping and analyses. Imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%–33%, 34%–66% or >66%), growth pattern (endophytic, <50% exophytic, or ≥50% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. The association of imaging features with mutational status (VHL, BAP1, PBRM1, SETD2, KDM5C, and MUC4) was assessed. Results Median tumor size was 49 mm (range 14–162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had ≥50% exophytic component, and 18 (19.8%) had calcification. VHL (n = 52) and PBRM1 (n = 24) were the most common mutations. BAP1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, Pearson’s χ2 test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann–Whitney U test). Conclusions BAP1 mutation was associated with ill-defined tumor margins and presence of calcification; MUC4 mutation was associated with exophytic growth. Given the known prognostic implications of BAP1 and MUC4 mutations, these results support using radiogenomics to aid in prognostication and management.

There is a wide pathological spectrum of kidney sarcomas that show characteristic histology, ontogeny, and clinical-biological behavior. While leiomyosarcomas commonly arise from the capsule, solitary fibrous tumors and clear cell sarcomas typically show renal sinus and medullary epicenter, respectively. Although distribution and imaging findings of some sarcomas may be characteristic, definitive diagnosis warrants histopathological examination following surgery. Renal sarcomas manifest advanced disease at presentation and portend poor prognosis.

Significant advances in imaging technology have changed the management of pancreatic cancer. In computed tomography (CT), this has included development of multidetector row, rapid, thin-section imaging that has also facilitated the advent of advanced reconstructions, which in turn has offered new perspectives from which to evaluate this disease. In magnetic resonance imaging, advances including higher field strengths, thin-section volumetric acquisitions, diffusion weighted imaging, and liver specific contrast agents have also resulted in new tools for diagnosis and staging. Endoscopic ultrasound has resulted in the ability to provide high-resolution imaging rivaling intraoperative ultrasound, along with the ability to biopsy via real time imaging suspected pancreatic lesions. Positron emission tomography with CT, while still evolving in its role, provides whole body staging as well as the unique imaging characteristic of metabolic activity to aid disease management. This article will review these modalities in the diagnosis and staging of pancreatic cancer.

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.

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