Rabindra Gautam

Renal tumor quantification and classification in contrast-enhanced abdominal CT

Kidney cancer occurs in both a hereditary (inherited) and sporadic (non-inherited) form. It is estimated that almost a quarter of a million people in the USA are living with kidney cancer and their number increases with 51,000 diagnosed with the disease every year. In clinical practice, the response to treatment is monitored by manual measurements of tumor size, which are 2D, do not reflect the 3D geometry and enhancement of tumors, and show high intra- and inter-operator variability. We propose a computer-assisted radiology tool to assess renal tumors in contrast-enhanced CT for the management of tumor diagnoses and responses to new treatments. The algorithm employs anisotropic diffusion (for smoothing), a combination of fast-marching and geodesic level-sets (for segmentation), and a novel statistical refinement step to adapt to the shape of the lesions. It also quantifies the 3D size, volume and enhancement of the lesion and allows serial management over time. Tumors are robustly segmented and the comparison between manual and semi-automated quantifications shows disparity within the limits of inter-observer variability. The analysis of lesion enhancement for tumor classification shows great separation between cysts, von Hippel-Lindau syndrome lesions and hereditary papillary renal carcinomas (HPRC) with p-values inferior to 0.004. The results on temporal evaluation of tumors from serial scans illustrate the potential of the method to become an important tool for disease monitoring, drug trials and noninvasive clinical surveillance.

Automated noninvasive classification of renal cancer on multiphase CT

PURPOSE To explore the added value of the shape of renal lesions for classifying renal neoplasms. To investigate the potential of computer-aided analysis of contrast-enhanced computed-tomography (CT) to quantify and classify renal lesions. METHODS A computer-aided clinical tool based on adaptive level sets was employed to analyze 125 renal lesions from contrast-enhanced abdominal CT studies of 43 patients. There were 47 cysts and 78 neoplasms: 22 Von Hippel-Lindau (VHL), 16 Birt-Hogg-Dube (BHD), 19 hereditary papillary renal carcinomas (HPRC), and 21 hereditary leiomyomatosis and renal cell cancers (HLRCC). The technique quantified the three-dimensional size and enhancement of lesions. Intrapatient and interphase registration facilitated the study of lesion serial enhancement. The histograms of curvature-related features were used to classify the lesion types. The areas under the curve (AUC) were calculated for receiver operating characteristic curves. RESULTS Tumors were robustly segmented with 0.80 overlap (0.98 correlation) between manual and semi-automated quantifications. The method further identified morphological discrepancies between the types of lesions. The classification based on lesion appearance, enhancement and morphology between cysts and cancers showed AUC = 0.98; for BHD + VHL (solid cancers) vs. HPRC + HLRCC AUC = 0.99; for VHL vs. BHD AUC = 0.82; and for HPRC vs. HLRCC AUC = 0.84. All semi-automated classifications were statistically significant (p < 0.05) and superior to the analyses based solely on serial enhancement. CONCLUSIONS The computer-aided clinical tool allowed the accurate quantification of cystic, solid, and mixed renal tumors. Cancer types were classified into four categories using their shape and enhancement. Comprehensive imaging biomarkers of renal neoplasms on abdominal CT may facilitate their noninvasive classification, guide clinical management, and monitor responses to drugs or interventions.

Computer-aided renal cancer quantification and classification from contrast-enhanced CT via histograms of curvature-related features

In clinical practice, renal cancer diagnosis is performed by manual quantifications of tumor size and enhancement, which are time consuming and show high variability. We propose a computer-assisted clinical tool to assess and classify renal tumors in contrast-enhanced CT for the management and classification of kidney tumors. The quantification of lesions used level-sets and a statistical refinement step to adapt to the shape of the lesions. Intra-patient and inter-phase registration facilitated the study of lesion enhancement. From the segmented lesions, the histograms of curvature-related features were used to classify the lesion types via random sampling. The clinical tool allows the accurate quantification and classification of cysts and cancer from clinical data. Cancer types are further classified into four categories. Computer-assisted image analysis shows great potential for tumor diagnosis and monitoring.

The ABCs of BHD: An In-Depth Review of Birt-Hogg-Dubé Syndrome

OBJECTIVE Birt-Hogg-Dubé (BHD) syndrome is an autosomal dominant inherited syndrome involving multiple organs. In young patients, renal neoplasms that are multiple, bilateral, or both, such as oncocytomas, chromophobe renal cell carcinoma (RCC), hybrid chromophobe RCC-oncocytomas, clear cell RCC, and papillary RCC, can suggest BHD syndrome. Extrarenal findings, including dermal lesions, pulmonary cysts, and spontaneous pneumothoraces, also aid in diagnosis. CONCLUSION Radiologists may be one of the first medical specialists to suggest the diagnosis of BHD syndrome. Knowledge of pathogenesis and management, including the importance of the types of renal neoplasms in a given patient, is needed to properly recognize this rare condition.

Vascular Endothelial Growth Factor Receptor–Targeted Therapy in Succinate Dehydrogenase C Kidney Cancer

Introduction Our understanding of the genetic basis of kidney cancer continues to advance with the identification of new familial kidney cancer syndromes and the advent of large-scale genomic sequencing. Since the identification of the first kidney cancer gene (VHL) two decades ago, nearly a dozen new genes associated with the development of renal cell carcinoma (RCC) have been identified. Many of these genes are associated with metabolic pathways involved in the regulation of energy, oxygen, nutrient, and iron sensing. An improved understanding of the genetic basis of kidney cancer should lead to the integration of molecular tumor signatures in the classification and treatment of RCC. Much of the current understanding of the genetic basis of kidney cancer comes from the study of familial forms of this disease. One group of hereditary syndromes, long referred to as hereditary paraganglioma syndromes (PGL 1-5), are characterized by pheochromocytomas and head and neck paragangliomas. Identification of the gene responsible for PGL was confounded by the fact that linkage analyses revealed several candidate regions that mapped to different chromosomes. In 2000, the gene for PGL1 was determined to be SDHD, a member of the multi-subunit Krebs cycle enzyme, succinate dehydrogenase (SDH). PGL syndromes have since been linked to mutations in other SDH subunits including SDHA, SDHB, SDHC, and SHDAF2. Kidney cancer was first recognized as a manifestation of the SDHB syndrome when a renal tumor from a patient with a known SHDB germline mutation was found to have loss of heterozygosity (LOH) of the wild-type SDHB gene allele. Ricketts et al later demonstrated that SDHB alterations accounted for approximately 5% of patients with familial RCC not associated with a recognized syndrome. Mutations in SDHC and SDHD have recently also been linked to RCC. We recently reported that SDH inactivation is associated with early-onset, clinically aggressive kidney cancer similar to that associated with inactivation of fumarate hydratase, another mitochondrial Krebs cycle enzyme. In addition, Malinoc et al described a patient with a germline SDHC mutation presenting with clear-cell and papillary RCC; both tumors demonstrated LOH at SDHC and at 3p near the VHL locus. SDH-associated kidney cancer that is localized to kidney is surgically managed. On the basis of the observation that cancers associated with Krebs cycle alterations have a propensity to metastasize early, active surveillance of SDH-deficient kidney cancer is not recommended. We recommend that even small solid renal masses be removed with a wide surgical margin. There are no known effective systemic agents for treatment of metastatic SDH-RCC. To our knowledge, this is the first report describing systemic therapy in a patient with advanced SDH-associated RCC.

Renal tumor quantification and classification in triple-phase contrast-enhanced abdominal CT

It is estimated that a quarter of a million people in the USA are living with kidney cancer. In clinical practice, the response to treatment is monitored by manual measurements of tumor size, which are time consuming and show high intra- and inter-operator variability. We propose a computer-assisted radiology tool to assess renal tumors in contrast-enhanced CT for the management of tumor diagnoses and treatments. The algorithm employs anisotropic diffusion, a combination of fast-marching and geodesic level-sets, and a novel statistical refinement step to adapt to the shape of the lesions. It also quantifies the 3D size, volume and enhancement of the lesion and allows serial management of tumors. The comparison between manual and semi-automated quantifications shows disparity within the limits of inter-observer variability. The automated tumor classification shows great separation between cysts, von Hippel-Lindau syndrome (VHL) lesions and hereditary papillary renal carcinomas (HPRC) (p ≪ 0.004).

Imaging findings of hereditary renal tumors, a review of what the radiologist should know

It is estimated that up to 8% of currently diagnosed renal cancers are part of a hereditary syndrome. The radiologist may be the first person to associate a renal tumor presenting during an imaging study to other manifestations of a hereditary syndrome. This diagnosis can have broad implications for the patient but also for other family members. This update reviews the current known associations and emerging mutations of hereditary renal cancers from a radiologists perspective. Renal manifestations, as well as associated radiological findings and pitfalls are discussed. Additionally, screening and surveillance recommendations are also discussed to aid radiologists in the decision-making process for patient management.

PD59-03 GROWTH KINETICS IN VON HIPPEL-LINDAU-ASSOCIATED RENAL TUMORS: DEFINING THE INFLUENCE OF GERMLINE MUTATION TYPE

free survival. Progressionwasstrictly definedasgrowth rate>0.5 cm/year, greatest tumor diameter >4.0 cm, metastatic disease, or elective crossover. Outcomes were evaluated using Kaplan-Meier survival analysis and comparisons were performed using the log-rank test. RESULTS: Of the 615 enrolled patients, 298 (48.5%) chose primary intervention and 317 (51.5%) chose active surveillance. From the active surveillance cohort, 45 (14.2%) patients underwent delayed intervention.Median follow-up time for theentire registrywas2.9years,with203 (33.0%) patients followed for 5 years or more. At baseline, patients who chose active surveillance were older (P < 0.001) and had higher comorbidity status (P< 0.001) than thosewho chose primary intervention. There was no difference in cancer-specific survival at 7 years between primary interventionandactive surveillance (99.0%vs100%, respectively,P1⁄4 0.3) [Figure 1A]. However, overall survival was higher in patients with primary intervention when compared to active surveillance at 5 years (93.0% vs 80.2%, respectively) and 7 years (91.7% vs 65.9%, respectively, P 1⁄4 0.002) [Figure 1B]. The 5-year and 7-year progression-free survival rate in the active surveillance cohort was 83.9% and 71.4%, respectively. CONCLUSIONS: In the intermediate-term, active surveillance appears to be as safe as and not inferior to primary intervention for carefully selected patients with small renal masses. As the registry matures, further studies will elucidate the effectiveness of active surveillance in the long-term.