Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01. Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

A Prospective Study of the Diagnostic Performance of Photon-Counting CT Compared With MRI in the Characterization of Renal Masses.

OBJECTIVES: The aim of this study was to assess the interreader reliability and per-RCC sensitivity of high-resolution photon-counting computed tomography (PCCT) in the detection and characterization of renal masses in comparison to MRI. MATERIALS AND METHODS: This prospective study included 24 adult patients (mean age, 52 ± 14 years; 14 females) who underwent PCCT (using an investigational whole-body CT scanner) and abdominal MRI within a 3-month time interval and underwent surgical resection (partial or radical nephrectomy) with histopathology (n = 70 lesions). Of the 24 patients, 17 had a germline mutation and the remainder were sporadic cases. Two radiologists (R1 and R2) assessed the PCCT and corresponding MRI studies with a 3-week washout period between reviews. Readers recorded the number of lesions in each patient and graded each targeted lesion's characteristic features, dimensions, and location. Data were analyzed using a 2-sample t test, Fisher exact test, and weighted kappa. RESULTS: In patients with von Hippel-Lindau mutation, R1 identified a similar number of lesions suspicious for neoplasm on both modalities (51 vs 50, P = 0.94), whereas R2 identified more suspicious lesions on PCCT scans as compared with MRI studies (80 vs 56, P = 0.12). R1 and R2 characterized more lesions as predominantly solid in MRIs (R1: 58/70 in MRI vs 52/70 in PCCT, P < 0.001; R2: 60/70 in MRI vs 55/70 in PCCT, P < 0.001). R1 and R2 performed similarly in detecting neoplastic lesions on PCCT and MRI studies (R1: 94% vs 90%, P = 0.5; R2: 73% vs 79%, P = 0.13). CONCLUSIONS: The interreader reliability and per-RCC sensitivity of PCCT scans acquired on an investigational whole-body PCCT were comparable to MRI scans in detecting and characterizing renal masses. CLINICAL RELEVANCE STATEMENT: PCCT scans have comparable performance to MRI studies while allowing for improved characterization of the internal composition of lesions due to material decomposition analysis. Future generations of this imaging modality may reveal additional advantages of PCCT over MRI.

Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

BACKGROUND: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. RESULTS: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. DATA CONCLUSION: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses.

Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. Objective: We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design setting and participants: We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysis: Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitations: Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2-5) tumors and five (IQR: 0-13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58-89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR. Conclusions: A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summary: Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.

PRDM10 RCC: A Birt-Hogg-Dubé-like Syndrome Associated With Lipoma and Highly Penetrant, Aggressive Renal Tumors Morphologically Resembling Type 2 Papillary Renal Cell Carcinoma.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Deep learning-based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI.

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 , ß 1 = 0.9 , ß 2 = 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.

Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.

We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.

Differential VHL Mutation Patterns in Bilateral Clear Cell RCC Distinguishes Between Independent Primary Tumors and Contralateral Metastatic Disease.

OBJECTIVE: To evaluate whether bilateral, multifocal clear cell renal cell carcinoma (ccRCC) patients can be differentiated by VHL mutation analysis into cases that represent either multiple independently arising primary tumors, or a single primary tumor which has spread ipsilaterally as well as to the contralateral kidney. The nature of kidney cancer multifocality outside of known hereditary syndromes is as yet poorly understood. MATERIALS AND METHODS: DNA from multiple tumors per patient were evaluated for somatic VHL gene mutation and hypermethylation. A subset of tumors with shared VHL mutations were analyzed with targeted, next-generation sequencing assays. RESULTS: This cohort contained 5 patients with multiple tumors that demonstrated a shared somatic VHL mutation consistent with metastatic spread including to the contralateral kidney. In several cases this was substantiated by additional shared somatic mutations in ccRCC-associated genes. In contrast, the remaining 14 patients with multiple tumors demonstrated unique, unshared VHL alterations in every analyzed tumor, consistent with independently arising kidney tumors. None of these latter patients showed any evidence of local spread or distant metastasis. CONCLUSION: The spectrum of VHL alterations within evaluated bilateral, multifocal ccRCC tumors from a single patient can distinguish between multiple independent tumor growth and metastasis. This can be performed using currently available clinical genetic tests and will improve the accuracy of patient diagnosis and prognosis, as well as informing appropriate management.

A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI.

PURPOSE: To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma. METHODS: 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. RESULTS: 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. CONCLUSION: The developed AI system in this study showed high diagnostic performance in differentiation of ccRCC versus oncocytoma on multi-phasic MRIs.

Update on Hereditary Renal Cancer and Imaging Implications.

Up to 8% of renal cancers are thought to have a hereditary component. Several hereditary renal cancer syndromes have been identified over the last few decades. It is important for the radiologist to be aware of findings associated with hereditary renal cancer syndromes to detect tumors early, enroll patients in appropriate surveillance programs, and improve outcomes for the patient and affected family members. This review discusses from a radiologist's perspective well-known hereditary renal cancer syndromes and emerging genetic mutations associated with renal cancer that are less well characterized, focusing on imaging features and known associations.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome: Spectrum of imaging findings.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Growth Rates of Genetically Defined Renal Tumors: Implications for Active Surveillance and Intervention.

PURPOSE: Published series of growth rates of renal tumors on active surveillance largely consist of tumors without pathologic or genetic data. Growth kinetics of genetically defined renal tumors are not well known. Here, we evaluate the growth of genetically defined renal tumors and their association with patient clinical and genetic characteristics. PATIENTS AND METHODS: We evaluated patients with an inherited kidney cancer susceptibility syndrome as a result of a pathologic germline alteration of VHL, MET, FLCN, or BAP1 with at least 1 solid renal mass managed with active surveillance at our institution. Tumor growth rates (GR) were calculated and patients were stratified by genetic alteration and other clinical and genetic factors to analyze differences in growth rates using linear regression and comparative statistics. RESULTS: A total of 292 patients with 435 genetically defined tumors were identified, including 286 VHL-deficient, 91 FLCN-deficient, 52 MET-activated, and 6 BAP1-deficient tumors. There were significant differences in GRs when stratified by genetic alteration. BAP1-deficient tumors had the fastest median GR (0.6 cm/y; interquartile range [IQR], 0.57-0.68 cm/y), followed by VHL-deficient tumors (GR, 0.37 cm/y; IQR, 0.25-0.57 cm/y), FLCN-deficient tumors (GR, 0.10 cm/y; IQR, 0.04-0.24 cm/y), and tumors with MET activation (GR, 0.15 cm/y; IQR, 0.053-0.32 cm/y; P < .001). Tumors from the same patient had similar GRs. Younger age was independently associated with higher GR (P = .005). CONCLUSION: In a cohort of genetically defined tumors, tumor growth rates varied in a clinically and statistically different manner according to genetic subtype. Rapid growth of BAP1-deficient tumors indicates that these patients should be managed with caution. The faster growth of tumors in younger patients may support more frequent imaging, whereas the slower growth of other tumors may support extended surveillance beyond annual imaging in some instances.

Differentiating papillary type I RCC from clear cell RCC and oncocytoma: application of whole-lesion volumetric ADC measurement.

PURPOSE: To determine whether objective volumetric whole-lesion apparent diffusion coefficient (ADC) distribution analysis improves upon the capabilities of conventional subjective small region-of-interest (ROI) ADC measurements for prediction of renal cell carcinoma (RCC) subtype. METHODS: This IRB-approved study retrospectively enrolled 55 patients (152 tumors). Diffusion-weighted imaging DWI was acquired at b values of 0, 250, and 800 s/mm2 on a 1.5T system (Aera, Siemens Healthcare). Whole-lesion measurements were performed by a research fellow and reviewed by a fellowship-trained radiologist. Mean, median, skewness, kurtosis, and every 5th percentile ADCs were determined from the whole-lesion histogram. Linear mixed models that accounted for within-subject correlation of lesions were used to compare ADCs among RCC subtypes. Receiver-operating characteristic (ROC) curve analysis with optimal cutoff points from the Youden index was used to test the ability of ADCs to differentiate clear cell RCC (ccRCC), papillary RCC (pRCC), and oncocytoma subtypes. RESULTS: Whole-lesion ADC values were significantly different between pRCC and ccRCC, and between pRCC and oncocytoma, demonstrating strong ability to differentiate subtypes across the quantiles (both P < 0.001). Best percentile ROC analysis demonstrated AUC values of 95.2 for ccRCC vs. pRCC; 67.6 for oncocytoma vs. ccRCC; and 95.8 for oncocytoma vs. pRCC. Best percentile ROC analysis further indicated model sensitivities/specificities of 84.5%/93.1% for ccRCC vs. pRCC; 100.0%/10.3% for oncocytoma vs. ccRCC; and 88.5%/93.1% for oncocytoma vs. pRCC. CONCLUSION: The objective methodology of whole-lesion volumetric ADC measurements maintains the sensitivity/specificity of conventional expert-based ROI analysis, provides information on lesion heterogeneity, and reduces observer bias.

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 radiologist's 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.

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.

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.

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.

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).