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Introduction: Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel–Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics. Material and Methods: We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups—'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models. Results: This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90. Conclusion: This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population. [ABSTRACT FROM AUTHOR]

Background: von Hippel–Lindau syndrome (VHL) is an autosomal dominant hereditary syndrome with an increased predisposition of developing numerous cysts and tumors, almost exclusively clear cell renal cell carcinoma (ccRCC). Considering the lifelong surveillance in such patients to monitor the disease, patients with VHL are preferentially imaged using MRI to eliminate radiation exposure. Purpose: Segmentation of kidney and tumor structures on MRI in VHL patients is useful in lesion characterization (e.g., cyst vs. tumor), volumetric lesion analysis, and tumor growth prediction. However, automated tasks such as ccRCC segmentation on MRI is sparsely studied. We develop segmentation methodology for ccRCC on T1 weighted precontrast, corticomedullary, nephrogenic, and excretory contrast phase MRI. Methods: We applied a new neural network approache using a novel differentiable decision forest, called hinge forest (HF), to segment kidney parenchyma, cyst, and ccRCC tumors in 117 images from 115 patients. This data set represented an unprecedented 504 ccRCCs with 1171 cystic lesions obtained at five different MRI scanners. The HF architecture was compared with U‐Net on 10 randomized splits with 75% used for training and 25% used for testing. Both methods were trained with Adam using default parameters (α=0.001,β1=0.9,β2=0.999$\alpha = 0.001,\ \beta _1 = 0.9,\ \beta _2 = 0.999$) over 1000 epochs. We further demonstrated some interpretability of our HF method by exploiting decision tree structure. Results: The HF achieved an average kidney, cyst, and tumor Dice similarity coefficient (DSC) of 0.75 ± 0.03, 0.44 ± 0.05, 0.53 ± 0.04, respectively, while U‐Net achieved an average kidney, cyst, and tumor DSC of 0.78 ± 0.02, 0.41 ± 0.04, 0.46 ± 0.05, respectively. The HF significantly outperformed U‐Net on tumors while U‐Net significantly outperformed HF when segmenting kidney parenchymas (α<0.01$\alpha < 0.01$). Conclusions: For the task of ccRCC segmentation, the HF can offer better segmentation performance compared to the traditional U‐Net architecture. The leaf maps can glean hints about deep learning features that might prove to be useful in other automated tasks such as tumor characterization. [ABSTRACT FROM AUTHOR]

Purpose: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. Methods: We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011–2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). Results: Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562–0.907] and 0.817 [95% CI: 0.663–0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814–0.978]and 0.926 [95% CI: 0.854–0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742–0.927] and 0.886 [95% CI: 0.819–0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. Conclusion: This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is associated with an aggressive form of renal cell carcinoma with high risk of metastasis, even in small primary tumors with unequivocal imaging findings. In this study, we compare the performance of ultra-high b-value diffusion-weighted imaging (DWI) sequence (b = 2000 s/mm2) to standard DWI (b = 800 s/mm2) sequence in identifying malignant lesions in patients with HLRCC. Methods: Twenty-eight patients (n = 18 HLRCC patients with 22 lesions, n = 10 controls) were independently evaluated by three abdominal radiologists with different levels of experience using four combinations of MRI sequences in two separate sessions (session 1: DWI with b-800, session 2: DWI with b-2000). T1 precontrast, T2-weighted (T2WI), and apparent diffusion coefficient (ADC) sequences were similar in both sessions. Each identified lesion was subjectively assessed using a six-point cancer likelihood score based on individual sequences and overall impression. Results: The ability to distinguish benign versus malignant renal lesions improved with the use of b-2000 for more experienced radiologists (Reader 1 AUC: Session 1—0.649 and Session 2—0.938, p = 0.017; Reader 2 AUC: Session 1—0.781 and Session 2—0.921, p = 0.157); whereas no improvement was observed for the less experienced reader (AUC: Session 1—0.541 and Session 2—0.607, p = 0.699). Conclusion: The inclusion of ultra-high b-value DWI sequence improved the ability of classification of renal lesions in patients with HLRCC for experienced radiologists. Consideration should be given toward incorporation of DWI with b-2000 s/mm2 into existing renal MRI protocols. [ABSTRACT FROM AUTHOR]

Purpose: Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors. Materials and Methods: A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT < = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs). Results: The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4–90.6] and mean AUC-ROC of 0.795 [0.608–0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56). Conclusion: Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences. [ABSTRACT FROM AUTHOR]

A metabolic shift from altered mitochondrial DNA: Deficiency in the metabolic enzyme fumarate hydratase distinguishes an aggressive and lethal form of kidney cancer called hereditary leiomyomatosis and renal cell carcinoma (HLRCC). Crooks et al. investigated the molecular basis for why HLRCC tumors rapidly grow and metastasize. Deficiency in fumarate hydratase led to the accumulation of the metabolite fumarate, resulting in the modification and inactivation of factors involved in mitochondrial DNA replication and proofreading. Subsequently, mitochondrial DNA mutations increased, leading to loss of mitochondria and a metabolic shift to aerobic glycolysis. Thus, lack of a crucial metabolic enzyme leads to mitochondrial dysfunction and metabolic rewiring that promote tumor progression and metastasis. Understanding the mechanisms of the Warburg shift to aerobic glycolysis is critical to defining the metabolic basis of cancer. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is an aggressive cancer characterized by biallelic inactivation of the gene encoding the Krebs cycle enzyme fumarate hydratase, an early shift to aerobic glycolysis, and rapid metastasis. We observed impairment of the mitochondrial respiratory chain in tumors from patients with HLRCC. Biochemical and transcriptomic analyses revealed that respiratory chain dysfunction in the tumors was due to loss of expression of mitochondrial DNA (mtDNA)–encoded subunits of respiratory chain complexes, caused by a marked decrease in mtDNA content and increased mtDNA mutations. We demonstrated that accumulation of fumarate in HLRCC tumors inactivated the core factors responsible for replication and proofreading of mtDNA, leading to loss of respiratory chain components, thereby promoting the shift to aerobic glycolysis and disease progression in this prototypic model of glucose-dependent human cancer. [ABSTRACT FROM AUTHOR]

Purpose: This study seeks to evaluate the use of quantitative texture parameters extracted from multiphasic contrast-enhanced magnetic resonance (MR) imaging in differentiating between benign and malignant masses (oncocytoma vs. clear cell and papillary RCC) and between common subtypes of renal cell carcinoma (clear cell vs. papillary RCC) in small renal masses (< 4 cm).Method: One-hundred and forty-two renal lesions (90 clear cell and 22 papillary RCCs; 30 oncocytomas) were identified in a cohort of 41 patients (18 men, 23 women: mean age, 52.8 ± 14.4 years) who underwent preoperative multiphasic contrast-enhanced MR with four phases (unenhanced, arterial, venous, and delayed) between 2015 and 2016. In this study, texture features were extracted from entire cross-sectional tumoral region in three consecutive slices containing the largest cross-sectional area from each of the four phases. The change in imaging feature between precontrast imaging and each postcontrast phase was calculated. Data dimension reduction and feature selection were performed by conducting (1) pairwise Wilcoxon rank test followed by modified false discovery rate adjustment, and (2) Lasso regression. Multivariate modeling incorporating the selected features was performed using random forest classification method.Results: Histogram imaging features were informative variables in differentiating between benign and malignant masses, while textures imaging features were of added value in differentiating between subtypes of RCCs. Papillary RCCs were distinguished from clear cell RCCs (sensitivity 65.5%, specificity 88%, and accuracy 77.9%), oncocytomas from clear cell RCCs (sensitivity 67.3%, specificity 88.9%, and accuracy 79.3%), and oncocytomas from papillary and clear cell RCCs (sensitivity 64.7%, specificity 85.9%, and accuracy 77.9%).Conclusions: A combination of histogram and texture imaging features on multiphasic MR can help differentiate histologic cell types in common small renal masses (< 4 cm). [ABSTRACT FROM AUTHOR]

To characterize the clinical manifestations and genetic basis of a familial cancer syndrome in patients with lipomas and Birt-Hogg-Dubé-like clinical manifestations including fibrofolliculomas and trichodiscomas and kidney cancer. Genomic analysis of blood and renal tumor DNA was performed. Inheritance pattern, phenotypic manifestations, and clinical and surgical management were documented. Cutaneous, subcutaneous, and renal tumor pathologic features were characterized. Affected individuals were found to be at risk for a highly penetrant and lethal form of bilateral, multifocal papillary renal cell carcinoma. Whole genome sequencing identified a germline pathogenic variant in PRDM10 (c.2029 T>C, p.Cys677Arg), which cosegregated with disease. PRDM10 loss of heterozygosity was identified in kidney tumors. PRDM10 was predicted to abrogate expression of FLCN , a transcriptional target of PRDM10, which was confirmed by tumor expression of GPNMB, a TFE3/TFEB target and downstream biomarker of FLCN loss. In addition, a sporadic papillary RCC from the TCGA cohort was identified with a somatic PRDM10 mutation. We identified a germline PRDM10 pathogenic variant in association with a highly penetrant, aggressive form of familial papillary RCC, lipomas, and fibrofolliculomas/trichodiscomas. PRDM10 loss of heterozygosity and elevated GPNMB expression in renal tumors indicate that PRDM10 alteration leads to reduced FLCN expression, driving TFE3-induced tumor formation. These findings suggest that individuals with Birt-Hogg-Dubé-like manifestations and subcutaneous lipomas, but without a germline pathogenic FLCN variant, should be screened for germline PRDM10 variants. Importantly, kidney tumors identified in patients with a pathogenic PRDM10 variant should be managed with surgical resection instead of active surveillance. [ABSTRACT FROM AUTHOR]

Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma. From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575). After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619–0.926] and 0.808 [0.537–0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498–0.960] and 0.92 [0.825–0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically. According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted. • CT radiomics has a high degree of accuracy in distinguishing RCCs from renal oncocytomas (ROs), including chRCCs from ROs • Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous • For this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols are warranted. [ABSTRACT FROM AUTHOR]

To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma. In this retrospective study, 74 patients (49 male, mean age 59.3) with 243 renal masses (203 ccRCC and 40 oncocytoma) that had undergone MR imaging 6 months prior to pathologic confirmation of the lesions were included. Segmentation using seed placement and bounding box selection was used to extract the lesion patches from T2-WI, and T1-WI pre-contrast, post-contrast arterial and venous phases. Then, a deep convolutional neural network (AlexNet) was fine-tuned to distinguish the ccRCC from oncocytoma. Five-fold cross validation was used to evaluate the AI algorithm performance. A subset of 80 lesions (40 ccRCC, 40 oncocytoma) were randomly selected to be classified by two radiologists and their performance was compared to the AI algorithm. Intra-class correlation coefficient was calculated using the Shrout-Fleiss method. Overall accuracy of the AI system was 91% for differentiation of ccRCC from oncocytoma with an area under the curve of 0.9. For the observer study on 80 randomly selected lesions, there was moderate agreement between the two radiologists and AI algorithm. In the comparison sub-dataset, classification accuracies were 81%, 78%, and 70% for AI, radiologist 1, and radiologist 2, respectively. The developed AI system in this study showed high diagnostic performance in differentiation of ccRCC versus oncocytoma on multi-phasic MRIs. • A robust radiologic method to accurately differentiate ccRCC and oncocytoma is not available. • The AI system in the present study showed high diagnostic performance in differentiation of ccRCC and oncocytoma on MRI. • There was moderate agreement between the two radiologists and AI algorithm for the subset observer study. [ABSTRACT FROM AUTHOR]

To retrospectively investigate the radiological presentations of HLRCC-associated renal tumors to facilitate accurate lesion characterization and compare these presentations with simple cysts and characteristics of other subtypes of renal cell carcinoma (RCC) as reported in the literature. The MRI and CT imaging characteristics of 39 pathologically confirmed lesions from 30 patients (20 male, 10 female) with HLRCC syndrome were evaluated by two radiologists. Patients had an average age at diagnosis of 43.8 ± 13.1 years. Lesion characteristics including laterality, homogeneity, diameter (cm), nodularity, septations, T1 and T2 signal intensity, enhancement, and restricted diffusion were recorded. Imaging characteristics of the lesions were further compared to characteristics of benign simple cysts surgically removed at the same time point. The examined lesions had a mean diameter of 5.06 ± 3.80 cm, an average growth rate of 2.91 × 10−3 cm/day and an estimated annual growth rate of 1.06 cm/year. 50% of lesions demonstrated nodularity, 65% were mostly T2-hyperintense, 83% demonstrated restricted diffusion in solid portions of the lesions, and 65% had well-defined margins. 76% of patients demonstrated extra-renal manifestations, 53% lymphadenopathy, and 43% distant metastasis. Our analysis confirmed that while HLRCC-associated renal lesions demonstrate diversity in imaging presentations, the majority are unilateral and solitary, T2-hyperintense, heterogeneous with well-defined margins, and frequently demonstrate restricted diffusion and nodularity. • HLRCC-associated renal lesions demonstrated an average annual growth rate of 1.06 cm/year. • Majority of lesions had well-defined margins (65%) and were T2-hyperintense (65%). • Lesions demonstrated high rates of metastasis and lymph node involvement. [ABSTRACT FROM AUTHOR]

Medical imaging of the 3 most common genitourinary (GU) cancers-prostate adenocarcinoma, renal cell carcinoma, and urothelial carcinoma of the bladder-has evolved significantly during the last decades. The most commonly used imaging modalities for the diagnosis, staging, and follow-up of GU cancers are computed tomography, magnetic resonance imaging (MRI), and positron emission tomography (PET). Multiplanar multidetector computed tomography and multiparametric MRI with diffusion-weighted imaging are the main imaging modalities for renal cell carcinoma and urothelial carcinoma, and although multiparametric MRI is rapidly becoming the main imaging tool in the evaluation of prostate adenocarcinoma, biopsy is still required for diagnosis. Functional and molecular imaging using 18-fluorodeoxyglucose-PET and sodium fluoride-PET are essential for the diagnosis, and especially follow-up, of metastatic GU tumors. This review provides an overview of the latest advances in the imaging of these 3 major GU cancers. [ABSTRACT FROM AUTHOR]