Benign Renal Tumors Research Articles

Spectral CT imaging versus conventional CT post-processing technique in differentiating malignant and benign renal tumors.

Quantitative comparison of the diagnostic value of spectral CT imaging and conventional CT post-processing technique in differentiating malignant and benign renal tumors. A total of 209 patients with renal tumors who had undergone CT enhancement were assigned to three groups-clear cell renal cell carcinoma (ccRCC, n = 106), non-ccRCC (n = 60), and benign renal tumor (n = 43) groups. Parametric CT enhancement of each tumor based on spectral CT and conventional CT was performed using in-house software, and the iodine concentration, water content, slope, and density values among the three groups were compared. The receiver operating characteristic (ROC) curve analysis was performed to determine the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity, and accuracy of the above parameters. The iodine concentration, slope, and density values were higher in the ccRCCs group compared to the non-ccRCCs and benign renal tumor groups (p < 0.05). Moreover, the iodine concentration, slope, and density values were higher in benign renal tumors compared to non-ccRCCs (p < 0.05). According to the ROC curve analysis, iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. The pairwise comparisons of the ROC curves and the diagnostic efficacies revealed that ROI-based CT enhancement was worse than the spectral CT imaging analysis in terms of density (p < 0.05). Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. 1. The iodine concentration, slope, and density values were higher for the ccRCCs compared to non-ccRCCs and benign renal tumors.2. Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors.3. Spectral CT imaging analysis performed better than conventional CT in differentiating malignant and benign renal tumors.

Report and follow-up on two new patients with congenital mesoblastic nephroma

BackgroundTumors are rare in neonatal age. Congenital mesoblastic nephroma (CMN) is a usually benign renal tumor observed at birth, or in the first months of life. It may also be identified prenatally and associated with polyhydramnios leading to preterm delivery. Effective treatment is surgical in most cases, consisting in total nephrectomy. In literature, very few studies report on the neonatal management of such a rare disease, and even less are those describing its uncommon complications.Cases presentationWe report on two single-center newborns affected with CMN. The first patient is a preterm female baby, born at 30+ 1 weeks of gestation (WG) due to premature labor, with prenatal (25 WG) identification of an intra-abdominal fetal mass associated with polyhydramnios. Once obtained the clinical stability, weight gain, instrumental (computed tomography, CT, showing a 4.8 × 3.3 cm left renal neoformation) and histological/molecular characterization of the lesion (renal needle biopsy picture of classic CMN with ETV6-NTRK3 translocation), a left nephrectomy was performed at 5 weeks of chronological age. The following clinical course was complicated by intestinal obstruction due to bowel adherences formation, then by an enterocutaneous fistula, requiring multiple surgical approaches including transitory ileo- and colostomy, before the conclusive anastomoses intervention. The second patient is a 17-day-old male term baby, coming to our observation due to postnatal evidence of palpable left abdominal mass (soon defined through CT, showing a 7.5 × 6.5 cm neoformation in the left renal lodge), feeding difficulties and poor weight gain. An intravenous diuretic treatment was needed due to the developed hypertension and hypercalcemia, which regressed after the nephrectomy (histological diagnosis of cellular CMN with ETV6-NTRK3 fusion) performed at day 26. In neither case was chemotherapy added. Both patients have been included in multidisciplinary follow-up, they presently show regular growth and neuromotor development, normal renal function and no local/systemic recurrences or other gastrointestinal/urinary disorders.ConclusionsThe finding of a fetal abdominal mass should prompt suspicion of CMN, especially if it is associated with polyhydramnios; it should also alert obstetricians and neonatologists to the risk of preterm delivery. Although being a usually benign condition, CMN may be associated with neonatal systemic-metabolic or postoperative complications. High-level surgical expertise, careful neonatological intensive care and histopathological/cytogenetic-molecular definition are the cornerstones for the optimal management of patients. This should also include an individualized follow-up, oriented to the early detection of any possible recurrences or associated anomalies and to a better quality of life of children and their families.

Small Renal Masses: Developing a Robust Radiomic Signature.

(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38-87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.

Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI.

BACKGROUND Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study aimed to examine how visceral fat area (VFA) and body mass index (BMI) correlate with intraoperative complexities, thereby guiding the selection of surgical techniques for RAPN. MATERIAL AND METHODS The study analyzed the medical records of 213 Chinese patients diagnosed with a range of benign and malignant renal neoplasms and treated with RAPN in 2020. Visceral fat area was quantified using computed tomography (CT) scans taken at the umbilical level. Various perioperative indicators, such as demographic details, clinicopathological parameters, operation time, estimated blood loss (EBL), warm ischemic time (WIT), and intraoperative complications, were assessed. RESULTS For the retroperitoneal approach, patients with either visceral obesity or general obesity had longer operation times (P<0.001 and P=0.004) and had a tendency for higher EBL (P=0.003 and P=0.001) compared to non-obese patients. In the transperitoneal approach, those with visceral obesity had significantly longer operation times (P=0.008) than their non-viscerally obese counterparts; however, general obesity showed no impact on operation time (P=0.251). Estimated blood loss was higher for patients with visceral obesity (P=0.004), but no significant difference was noted among those with general obesity (P=0.980). CONCLUSIONS VFA appears to offer predictive advantages over BMI in assessing intraoperative complexities for transperitoneal RAPN. When used in conjunction with BMI, it could serve as a valuable tool in selecting the most appropriate surgical approach for RAPN.

Homolateral and synchronic clear cell and papillary type of renal cell carcinoma Diagnostic hints and review of literature

Commonest solid mass of kidney is renal cell carcinoma, constituting 2-3% of all adult cancers. Synchronous malignant and benign renal tumours bilaterally have been described in a few studies. However, homolateral and synchronic appearance of malignant renal masses is extremely rare and there have been only a few case reports that had different subtypes of malignant renal neoplasms of the same kidney on histopathology. In this report, we present a 66-year-old male patient who had two different histological variants, ie, papillary RCC and clear cell RCC in his right kidney, and underwent radical nephrectomy. Our aim is to provide radiological diagnostic hints and review the literature relevant to this rare case, as the awareness of its occurrence can help in early diagnosis and deciding an appropriate management course.

Metanephric adenoma diagnosed on biopsy in an infant: a case report

BackgroundMetanephric adenoma is a rare benign renal tumor of the kidney, uncommonly observed in children. It is often misdiagnosed preoperatively as a malignant neoplasm, leading to an unnecessary nephrectomy. The challenge is to make the right diagnosis preoperatively and therefore manage it with conservative surgery. We report a case of a child with metanephric adenoma who underwent nephron-sparing surgery.Case presentationA renal tumor was discovered fortuitously in an 18-month-old Caucasian girl with several congenital malformations. Investigations showed a 28 × 27 × 27 mm left renal mass centrally located, well defined, nonvascularized, with no calcifications and which compressed the adjacent renal tissue. Furthermore, there were no signs of metastasis. The decision of a multidisciplinary meeting was to perform a computed tomography (CT)-scan-guided biopsy. Histologic examination concluded it was a metanephric adenoma. We performed a left open partial nephrectomy via a flank retroperitoneal incision. The final histopathological examination confirmed the diagnosis. The postoperative course was uneventful.ConclusionPreoperative diagnosis of metanephric adenoma is challenging. Because of the high probability of unnecessary radical nephrectomy, preoperative biopsy can be safe and determining to guide a more conservative approach so nephron-sparing surgery can be performed.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75).

Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.

Metanephric stromal tumor in an adult with PDGFRA mutation: a case report

BackgroundMetanephric stromal tumors (MST) are rare benign renal tumors that mainly occur in infants and children. Approximately 72% of MST in children have the B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation. To date, only five cases of adult MSTs have been reported and no clear genetic alterations have been found.Case presentationWe report a case of MST in a 45-year-old woman who complained of left lower back pain for a week, accompanied by hypertension (150/79 mmHg). Magnetic resonance imaging (MRI) showed an abnormally enhanced nodule (1.1 cm in the middle of the left kidney), which was histopathologically consistent with an MST. The BRAF V600E mutation was not detected in tumor cells using PCR and next-generation sequencing (NGS). However, a platelet-derived growth factor receptor alpha (PDGFRA) mutation was detected in this case using NGS. The patient showed no recurrence or metastasis nine months after partial nephrectomy, and her blood pressure was consistently normal.ConclusionThis is the first report of alterations in PDGFRA in MSTs. This result advances our knowledge of genetic variations in adult MSTs, which may have different gene alterations from MSTs in children.

Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors.

The increasing evidence of oncocytic renal tumors positive in 99mTc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of 99mTc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of 99mTc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of 99mTc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and 99mTc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that 99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with 99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of 99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of 99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.

Classification of Benign and Malignant Renal Tumors Based on CT Scans and Clinical Data Using Machine Learning Methods

Up to 20% of renal masses ≤4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective of this study is to propose a machine learning (ML) framework for pre-operative renal tumor classification using readily available clinical and CT imaging data. We tested both traditional ML methods (i.e., XGBoost, random forest (RF)) and deep learning (DL) methods (i.e., multilayer perceptron (MLP), 3D convolutional neural network (3DCNN)) to build the classification model. We discovered that the combination of clinical and radiomics features produced the best results (i.e., AUC [95% CI] of 0.719 [0.712–0.726], a precision [95% CI] of 0.976 [0.975–0.978], a recall [95% CI] of 0.683 [0.675–0.691], and a specificity [95% CI] of 0.827 [0.817–0.837]). Our analysis revealed that employing ML models with CT scans and clinical data holds promise for classifying the risk of renal malignancy. Future work should focus on externally validating the proposed model and features to better support clinical decision-making in renal cancer diagnosis.

99mTc-MIBI SPECT/CT Evaluation of a Renal Collision Tumor.

Preoperative differentiation of oncocytomas from renal cell carcinoma (RCC) is often challenging. 99mTc-MIBI imaging could play a potential role in differentiating oncocytoma from RCC, which in turn could guide surgical decision-making. We present the use of 99mTc-MIBI SPECT/CT to characterize a renal mass in a 66-year-old man with a complex medical history, including history of bilateral oncocytomas. 99mTc-MIBI SPECT/CT showed features suspicious of a malignant tumor, which was confirmed postnephrectomy as a chromophobe and papillary RCC collision tumor. This case supports 99mTc-MIBI imaging for preoperative differentiation of benign versus malignant renal tumors.

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach.

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

PROSPECTIVE OBSERVATIONAL STUDY COMPARING OUTCOMES AND COMPLICATIONS OF OPEN VS LAPAROSCOPIC NEPHRECTOMY

Introduction: Nephrectomy refers to surgical removal of the kidney and is further divided into simple, radical, partial and donor nephrectomy. Simple nephrectomy is conducted in irreversible kidney damage; radical nephrectomy in malignant or benign renal tumours while partial nephrectomy involves removal of part of kidney. Nephrectomy may be performed via open or laparoscopic approaches; although there is evidence favouring laparoscopy, supplementary evidence is necessary to establish it.2 This study aims to compare outcomes, complications and other parameters for laparoscopic and open surgical nephrectomy at a tertiary care centre in western India. To compare outcomes and Aim: complications for laparoscopic and open nephrectomy. Objectives: To compare: 1. Immediate and late complications. 2. A] Requirement of analgesia. B] Return of bowel functions. 3. Mean length of hospitalization and return to regular normal activity. Materials And Methods: A prospective observational single centre study was conducted at a tertiary care centre in western India. Total sample size was 49 of which 25 patients underwent laparoscopic nephrectomy while 24 patients underwent open nephrectomy. Intraoperative anaesthesia, trocar access, duration of surgery, blood loss, repeat haemoglobin and haematocrit value at post operative day 1, requirement of blood transfusion, duration of analgesics requirement, post-operative stay; histopathology report and time to resume normal activity at immediate follow up, follow up at 3 months and further follow up after surgery were noted down. Current study found statist Results: ically signicant relationship of higher intra-operative blood transfusion requirement and higher post-op requirement of Analgesia in case of open surgery. Moreover, among laparoscopy group there was signicantly lower blood loss during surgery, early time to oral diet, low pain VAS score, lower doses of Tramadol and early return to normal activity was found as compared to Open surgery group (p&lt;0.05). Laparoscopic nephrectom Conclusion: y results in less blood loss, less requirement of post operative analgesia and more rapid post operative recovery in comparison to open surgery

Coexistence of multiple clear cell papillary renal cell carcinoma with renal oncocytoma: a case report.

The authors report a case of a female 65-year-old suffering from end-stage kidney failure for 10 years, who presented with a double left renal tumor, composed by an oncocytoma associated to multiple CCPRCC, a very rare entity. A radical left nephrectomy was realized by lumbotomy, with an uneventful postoperative course. Histological examination was challenging. Immunohistological examination showed diffuse positivity of cytokertain 7. No local recurrence nor metastatic progression were found during the 12 months of follow-up. CCPRCC, is a new entity, previously known as the unclassified rena cell carcinoma, is a malignant renal tumor, initially reported in patients at end-stage kidney failure. Oncocytoma is a well-known rare benign renal tumor. The association of both is rare, and should be kept in mind, especially when scanoguided diagnosis biopsy is realized. Histopathological confirmation may be challenging, given the recent identification of CCPRCC. The nuclei disposal toward the luminal surface is a characteristic pathological landmark of CCPRCC. Immunohistopathological examination is of great help, showing a distinctive profile: diffuse staining for cytokertain 7 and carbonic anhydrase IX. CCPRCC is a new malignant pathological entity in renal tumors. It can be associated with other benign renal lesions. This should be taken into consideration while histopathological examination, mainly of scanoguided biopsy cores.

Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis.

BACKGROUND Renal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of malignant and benign renal tumors and to aid active surveillance. MATERIAL AND METHODS A retrospective study based on CT images was conducted. Axial CT images of 357 renal tumor cases were collected. There were 265 (74.2%) cases histologically proven to be malignant, while 34 (9.5%) cases were benign. Radiologists diagnosed 58 (16.3%) cases as angiomyolipoma (AML), based on characteristic appearance, not confirmed histopathologically. For ANN training, the arterial CT phase images were used. A total of 7207 arterial-phase images were collected, then cropped and added to the database with the associated diagnosis. For the test dataset (ANN validation), 38 cases (10 benign, 28 malignant) were chosen by subgroup randomization to correspond to statistical tumor type distribution. The VGG-16 ANN architecture was used in this study. RESULTS Trained ANN correctly classified 23 out of 28 malignant tumors and 8 out of 10 benign tumors. Accuracy was 81.6% (95% confidence interval, 65.7-92.3%), sensitivity was 82.1% (63.1-93.9%), specificity was 80.0% (44.4-97.5%), and F1 score was 86.8% (74.7-94.5%). CONCLUSIONS The created ANN achieved promising accuracy in differentiating benign vs malignant renal tumors.

Abstract 1518: Benign oncocytic tumors in the setting of HLRCC syndrome

Abstract Background: Renal cell carcinomas developing in patients with HLRCC syndrome are known to be aggressive single tumors of variable sizes, with a well-established morphology. Other morphological tumor types occurring in association with classic HLRCC tumors as independent neoplasias in this syndrome, have not been clearly recognized and described. We describe four patients with known HLRCC syndrome with confirmed germ line mutation who developed benign oncocytic renal tumors. Since HLRCC can also be oncocytic in many instances, it is important to be aware of the possible benign oncocytic tumors in this patient group. Design: Four patients with known positive germ line FH mutation and known HLRCC syndrome developed associated or new oncocytic tumors. All tumors were morphologically evaluated, IHC staining was done for FH, 2SC, CKIT, CK7, PAX8 and molecular studies were performed with TSO-500 for the evaluation of other genes. Clinicopathological correlation was done in all cases. Results: The tumors had been identified radiologically and were suspicious for malignancy. Age range was 37-73 years, and all the patients were female. Two patients had coexistent HLRCC cancers while two only had the oncocytomas. Localization was left kidney for all the masses, which were brown in color and well circumscribed and varied in size from 0.7 to 4 cm. In one patient, four separate masses were identified with different locations and sizes, but with the same morphology. This specific patient presented with two additional masses in the opposite kidney later. Tumors were composed of large oncocytic cells with prominent granular eosinophilic cytoplasm and dark pyknotic nuclei. No evidence of atypia, mitosis or necrosis was found. FH was positive, 2SC and CK7 were negative on IHC stains, whereas CKIT showed diffuse membranous positivity. Tumors were diagnosed as oncocytomas. All patients were positive for FH germ line mutation and the mutation was also identified in the tissues. Other molecular studies on the patient with multiple tumors showed additional pathogenic ERCC2 inactivating mutation, suggesting germline, which is rarely encountered in kidney tumors. All the patients are alive and free of disease up to 10 years after diagnosis. Conclusion: HLRCC cancers can have a variety of morphologic patterns and frequently have oncocytic features. Clinical information, genetic testing and special techniques are helpful in the diagnosis of these high-grade tumors. Recognizing that other benign indolent tumors with oncocytic features can occur in this syndrome is important to avoid errors in diagnosis and treatment. Citation Format: Dilara Akbulut, Mark Ball, Mark Raffeld, Marston Linehan, Maria J. Merino. Benign oncocytic tumors in the setting of HLRCC syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1518.

Diagnostic value of pseudocapsule ring hyperenhancement in contrast-enhanced ultrasound in renal focal lesions.

To investigate the diagnostic value of pseudocapsule ring hyperenhancement in contrast-enhanced ultrasound (CEUS) of focal renal lesions. Ninety eligible patients admitted were selected as the study subjects. The incidence rate of annular hyperenhancement in different benign and malignant focal lesions during angiography and the shape of annular enhancement were counted. The correlation between the occurrence of benign and malignant renal tumors and annular hyperenhancement and unenhanced areas was also analyzed. Capsule enhancement was observed in 56 cases, including 50 cases of malignant tumors (89.3%, 50/56) and 6 cases of benign tumors (10.7%, 6/56). Pearson correlation analysis showed that renal malignancy was positively associated with the occurrence of pseudocapsule ring hyperenhancement (γ = 0.489, P < 0.001). Benign and malignant renal tumors were positively correlated with the occurrence of non-enhancing areas (γ = 0.215, P = 0.042). The thickness and peak intensity of pseudocapsule ring enhancement in renal malignant tumors were significantly higher than those in benign tumors (all P < 0.001), while the time to peak was significantly lower than that in benign tumors (P < 0.001). The results of receiver operating characteristic (ROC) curve showed that the ring enhancement thickness, time to peak, and area under ROC curve of peak intensity of tumor reached more than 0.9. In CEUS, the pseudocapsule of focal renal lesions is a characteristic feature of renal malignant tumors, which can be used as a differential basis for benign and malignant tumors to improve the accuracy of benign and malignant renal tumors.

Differential Expression and Clinical Relevance of C-X-C Motif Chemokine Receptor 4 (CXCR4) in Renal Cell Carcinomas, Benign Renal Tumors, and Metastases

C-X-C Motif Chemokine Receptor 4 (CXCR4) is part of the human chemokine system and involved in progression and metastasis in renal cell carcinoma (RCC). However, the role of CXCR4 protein expression in RCC remains controversial. In particular, data regarding the subcellular distribution of CXCR4 in RCC and RCC metastasis as well as CXCR4 expression in renal tumors of variant histology are limited. The aim of the present study was the evaluation of the differential CXCR4 expression in RCC primary tumor and metastatic tissue as well as in variant renal histologies. In addition, the prognostic capacity of CXCR4 expression in organ-confined clear cell RCC (ccRCC) was evaluated. Three independent renal tumor cohorts (primary ccRCC cohort n1 = 64; cohort of various histological entities n2 = 146; metastatic RCC tissue cohort n3 = 92) were evaluated using tissue microarrays (TMA). After immunohistochemical staining for CXCR4, nuclear and cytoplasmic expression patterns were evaluated. CXCR4 expression was correlated with validated pathologic prognosticators, clinical data, and overall and cancer-specific survival. Positive cytoplasmic staining was observed in 98% of the benign and 38.9% of the malignant samples. Nuclear staining was positive for 94.1% of the benign samples and 83% of the malignant samples. The median cytoplasmic expression score was found to be higher in benign tissue than in ccRCC (130.00 vs. 0.00); median nuclear expression score analysis indicated the opposite (56.0 vs. 71.0). Within malignant subtypes, the highest expression score was seen in papillary renal cell carcinomas (cytoplasmic: 117.50, nuclear: 41.50). Within benign renal tumors, high cytoplasmic and nuclear CXCR4 expression scores were seen for oncocytomas (cytoplasmic: 100.00, nuclear: 31.00). Expression scores in RCC metastasis ranked between benign renal tissue and ccRCC in cytoplasmic and nuclear expression. Cytoplasmic CXCR4 expression was identified as a prognostic factor for OS and CSS (p = 0.042; p = 0.019). Multivariate analysis including clinicopathological parameters did not reveal an independent prognostic character of CXCR4 expression. CXCR4 expression differs significantly within benign lesions and renal neoplasms. Cytoplasmic and nuclear expression of CXCR4 could be detected across all RCC subtypes. The prognostic value of CXCR4 in ccRCC was confirmed in univariate analysis.

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer

Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor's zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.