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Introduction: Localised renal masses are an increasing burden on healthcare due to the rising number of cases. However, conventional imaging cannot reliably distinguish between benign and malignant renal masses, and renal mass biopsies are unable to characterise the entirety of the tumour due to sampling error, which may lead to delayed treatment or overtreatment. There is an unmet clinical need to develop novel imaging techniques to characterise renal masses more accurately. Renal tumours demonstrate characteristic metabolic reprogramming, and novel MRI methods have the potential to detect these metabolic perturbations, which may therefore aid accurate characterisation. Here, we present our study protocol for the investigation of the differential biology of benign and malignant renal masses using advanced MRI techniques (IBM-Renal)., Methods and Analysis: IBM-Renal is a multiarm, single-centre, non-randomised, feasibility study with the aim to provide preliminary evidence for the potential role of the novel MRI techniques to phenotype localised renal lesions. 30 patients with localised renal masses will be recruited to three imaging arms, with 10 patients in each: (1) hyperpolarised [1- 13 C]-pyruvate MRI, (2) deuterium metabolic imaging (DMI) and (3) sodium MRI. The diagnosis will be made on samples acquired at biopsy or at surgery. The primary objective is the technical development of the novel MRI techniques, with the ultimate aim to understand whether these can identify differences between benign and malignant tumours, while the secondary objectives aim to assess how complementary the techniques are, and if they provide additional information. The exploratory objective is to link imaging findings with clinical data and molecular analyses for the biological validation of the novel MRI techniques., Ethics and Dissemination: This study was ethically approved (UK REC HRA: 22/EE/0136; current protocol version 2.1 dated 11 August 2022). The plans for dissemination include presentations at conferences, publications in scientific journals, a doctoral thesis and patient and public involvement., Trial Registration Number: NCT06016075., Competing Interests: Competing interests: GDS has received educational grants from Pfizer, AstraZeneca and Intuitive Surgical; consultancy fees from Pfizer, MSD, EUSA Pharma and CMR Surgical; Travel expenses from MSD and Pfizer; Speaker fees from Pfizer; Clinical lead (urology) National Kidney Cancer Audit and Topic Advisor for the NICE kidney cancer guideline. SJW is a founder and director of Pinto Medical Consultancy. FAG has research grants from GlaxoSmithKline and AstraZeneca, research support from GE Healthcare and has consulted for AstraZeneca on behalf of the University of Cambridge. All other authors have no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Kidney cancer is a serious malignant disease, and early diagnosis along with precise segmentation are crucial for effective treatment. However, due to the scarcity of labelled medical image data, the development of the intelligent diagnosis of kidney cancer is restricted. To address this challenge, we propose a novel unsupervised domain adaptation (UDA) framework specifically designed for kidney and tumor CT image segmentation. Our framework consists of a generation phase and an adaptation phase. In the generation phase, we employ a wavelet-based style mining generator to create class-specific source-like images, facilitating domain alignment. In the adaptation phase, we introduce contrastive domain extraction and compact-aware domain consistency modules, enhancing feature-level and output-level adaptability through data augmentation techniques. Experimental results demonstrate that our method performs better in kidney and tumor segmentation tasks, exhibiting higher accuracy and generalization capability than state-of-the-art domain adaptation methods. This indicates that our approach has significant advantages in medical image segmentation for kidneys and tumors., (© 2024. The Author(s).)

Objective: To analyze the characteristics of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in solid renal tumors using qualitative and quantitative methods., Methods: Seventy-five patients who underwent preoperative conventional ultrasound (US), conventional contrast-enhanced ultrasound (C-CEUS), and H-CEUS examination of renal tumors were retrospectively analyzed, with a total of 89 renal masses. The masses were divided into the benign (30 masses) and malignant groups (59 masses) based on the results of enhanced computer tomography and pathology. The location, diameter, shape, border, calcification, and color doppler blood flow imaging (CDFI) of the lesions were observed by US, and the characteristics of the C-CEUS and H-CEUS images were qualitatively and quantitatively analyzed. The χ² test or Fisher's exact probability method was used to compare the US image characteristics between the benign and malignant groups, and the image characteristics of C-CEUS and H-CEUS between the benign and malignant groups. Moreover, the nonparametric Mann-Whitney test was used to compare the differences in C-CEUS and H-CEUS time-intensity curve (TIC) parameters., Results: Significant differences in gender, surgical approach, echogenicity, and CDFI were observed between the malignant and benign groups (p = 0.003, < 0.001, < 0.001, = 0003). Qualitative analysis also revealed significant differences in the mode of wash-out and fill-in direction between C-CEUS and H-CEUS in the malignant group (p = 0.041, 0.002). In addition, the homogeneity of enhancement showed significant differences between the two contrast models in the benign group (p = 0.009). Quantitative analysis indicated that the TIC parameters peak intensity (PI), deceleration time (DT) /2, area under the curve (AUC), and mean transition time (MTT) were significantly lower in the H-CEUS model compared to the C-CEUS model in both the benign and malignant groups. (all p < 0.001). In contrast, ascending slope of rise curve (AS) was significantly higher in the H-CEUS model compared to the C-CEUS model in the malignant group (p = 0.048)., Conclusions: In renal tumors, H-CEUS shows clearer internal enhancement of the mass and the changes in the wash-out period. The quantitative TIC parameters PI, DT/2, AUC, and MTT were lower in H-CEUS compared to C-CEUS. Both the quantitative and qualitative analyses indicated that H-CEUS better displays the characteristics of solid renal masses compared with C-CEUS., (© 2024. The Author(s).)

Background: With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions., Methods: CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation., Results: Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively., Conclusions: The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making., (© 2024. The Author(s).)

Background: Body composition may be related to survival in patients with clear-cell renal cell carcinoma (ccRCC), but studies have not simultaneously considered adipose and muscle tissue quantity and radiodensity., Methods: We analyzed data from 1,022 patients with ccRCC who underwent nephrectomy between 2000 and 2020 at Memorial Sloan Kettering Cancer Center. Skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue indexes (cm2/m2) and radiodensities [Hounsfield units (HU)] were assessed from noncontrast presurgical CT scans; clinical and demographic characteristics were available from the time of surgery. HRs and confidence intervals were estimated for overall (OS) and disease-free survival (DFS) through March 2023 in multivariable models that simultaneously accounted for all body composition measures., Results: The median age of the patients was 58 years, 69% were male, and 90% were White. There were 169 OS events over 8,392 person-years and 253 DFS events over 7,753 person-years of follow-up. In adjusted analyses, poor OS was associated with lower skeletal muscle radiodensity [-10 HU, HR (95% confidence interval), 1.37 (1.05-1.77)] and greater visceral adipose tissue radiodensity [+10 HU, 1.66 (1.06-2.59)], with similar findings for DFS. Poor survival was also associated with greater visceral adipose tissue index [+40 cm2/m2, OS: 1.32 (0.97, 1.79); DFS: 1.33 (1.04, 1.71)]. Associations with skeletal muscle radiodensity were limited to patients with stage 1/2 disease., Conclusions: Radiodensities of skeletal muscle and visceral adipose tissues may be novel presurgical prognostic factors for patients with ccRCC., Impact: The findings underscore the importance of evaluating the full range of body composition features simultaneously in multivariable models., (©2024 American Association for Cancer Research.)

Background: With limitations of conventional imaging and biopsy, accurate, non-invasive techniques to detect clear-cell renal cell carcinoma in patients with renal masses remain an unmet need. 89 Zr-labelled monoclonal antibody ([ 89 Zr]Zr-girentuximab) has high affinity for carbonic anhydrase 9, a tumour antigen highly expressed in clear-cell renal cell carcinoma. We aimed to evaluate [ 89 Zr]Zr-girentuximab PET-CT imaging for detection and characterisation of clear-cell renal cell carcinoma., Methods: ZIRCON was a prospective, open-label, multicentre, phase 3 trial conducted at 36 research hospitals and practices across nine countries (the USA, Australia, Canada, the UK, Türkiye, Belgium, the Netherlands, Spain, and France). Patients aged 18 years or older with an indeterminate renal mass 7 cm or smaller (cT1) suspicious for clear-cell renal cell carcinoma and scheduled for nephrectomy received a single dose of [ 89 Zr]Zr-girentuximab (37 MBq ±10%; 10 mg girentuximab) intravenously followed by abdominal PET-CT imaging 5 days (±2 days) later. Surgery was performed no later than 90 days after administration of [ 89 Zr]Zr-girentuximab. Blinded central review, conducted by three independent readers, determined the histology from surgical samples. The coprimary endpoints, determined for each individual reader, were the sensitivity and specificity of [ 89 Zr]Zr-girentuximab PET-CT imaging to detect clear-cell renal cell carcinoma, with histopathological confirmation as standard of truth. Analyses were on the full analysis set of patients, defined as patients who had evaluable PET-CT imaging and a confirmed histopathological diagnosis. The trial is registered with ClinicalTrials.gov, NCT03849118, and EUDRA Clinical Trials Register, 2018-002773-21, and is closed to enrolment., Findings: Between Aug 14, 2019, and July 8, 2022, 371 patients were screened for eligibility, 332 of whom were enrolled. 300 patients received [ 89 Zr]Zr-girentuximab (214 [71%] male and 86 [29%] female). 284 (95%) evaluable patients were included in the primary analysis. The mean sensitivity was 85·5% (95% CI 81·5-89·6) and mean specificity was 87·0% (81·0-93·1). No safety signals were observed. Most adverse events were not or were unlikely to be related to [ 89 Zr]Zr-girentuximab, with most (193 [74%] of 261 events) occurring during or after surgery. The most common grade 3 or worse adverse events were post-procedural haemorrhage (in six [2%] of 261 patients), urinary retention (three [1%]), and hypertension (three [1%]). In 25 (8%) of 300 patients, 52 serious adverse events were reported, of which 51 (98%) occurred after surgery. There were no treatment-related deaths., Interpretation: Our results suggest that [ 89 Zr]Zr-girentuximab PET-CT has a favourable safety profile and is a highly accurate, non-invasive imaging modality for the detection and characterisation of clear-cell renal cell carcinoma, which has the potential to be practice changing., Funding: Telix Pharmaceuticals., Competing Interests: Declaration of interests BS has served as a consultant for Telix Pharmaceuticals, Veracyte, Merck, and Johnson & Johnson; has served as a speaker for Merck; and received travel support from Histosonics. AJP has received institutional funding and medical writing support from Telix Pharmaceuticals for this trial. J-CB has served as a consultant or advisor, and received honoraria and research funding from Bristol Myers Squibb, Conmed, Ipsen Pharmaceuticals, Merck Sharp and Dohme, Pfizer, and Intuitive Surgical. MAM has received institutional funding from Telix Pharmaceuticals; served as a board member for Oranomed, RayzeBio, Fusion, Advanced Molecular Imaging and Therapy, American College of Nuclear Medicine, and Society for Nuclear Medicine and Molecular Imaging; and holds stock or stock options from Advanced Molecular Imaging and Therapy, Gentem Health, and SoftThread. VM has received honoraria from Ethicon and Exelixis. AMS has received institutional research funding from Telix Pharmaceuticals, National Health and Medical Research Council, National Imaging Facility, National Breast Cancer Foundation, Medical Research Future Fund, Australian Cancer Research Foundation, and Victorian Cancer Agency; holds intellectual property licensing with Humanigen, AbbVie, and Life Science Pharmaceuticals; served as an advisory board member and consultant for ImmunOs and Imagion Bio; served as a board member, committee member, or chair for Fusion, Telix Pharmaceuticals, Australian and New Zealand Society of Nuclear Medicine, Australian and New Zealand Urogenital and Prostate Cancer trials group, Cooperative Trials Group for Neuro-Oncology, National Imaging Facility Molecular Imaging Theme, Australian Academy of Health and Medical Sciences, Melbourne Academic Centre for Health Molecular Imaging Theme, World Federation of Nuclear Medicine and Biology, Victorian Comprehensive Cancer Centre, Australian Nuclear Science and Technology Organisation External Advisory Board, and International Centers for Precision Oncology Foundation. CvP has served as consultant or advisor for Astellas and Merck; received honoraria from Astellas; and received travel funding from Ipsen. CBa has served as a data safety monitoring board or advisory board member for Telix Pharmaceuticals. BO has received research funding from Telix Pharmaceuticals. TA has received honoraria and research funding from Telix Pharmaceuticals. RM has received research funding from Telix Pharmaceuticals. DMS has received institutional research funding from Telix Pharmaceuticals; served as a consultant for Global Medical Solutions Taiwan, Progenics Pharmaceuticals, Heidelberg University, and DuChemBio; received payments or honoraria from the School of Breast Oncology, PrecisCa, and US Department of Justice; and served as an associate chair of Society for Nuclear Medicine and Molecular Imaging Scientific Program Committee. STL has received research funding from Telix Pharmaceuticals. NP-T has received honoraria from Actinium Pharmaceuticals; served as a consultant and advisor for Illumina, Imaginab, Actinium, and Progenics/Lantheus; a speaker for Actinium Pharmaceuticals and Telix Pharmaceuticals; and received institutional research funding from Actinium Pharmaceuticals, Imaginab, Regeneron Pharmaceuticals, Bristol Myers Squibb, Janssen, Clarity, Bayer Health, Telix Pharmaceuticals, and Ymabs. ACF is a board member of Molecular Decisions; a consultant or advisor for Verily; has received research funding from Earli, Filtricine, MagARRAY, and Calithera; and holds founders stock at Molecular Decisions, ACF's spouse is an employee or owner of Silverado Hospice and Antelope Valley Hospice. PA is an employee of Premier Research, and a consultant for Premier Research. KS is an employee of ABX-CRO advanced pharmaceutical services Forschungsgesellschaft. LT is an employee of Telix Pharmaceuticals and holds stock from Novartis. MW is an employee and holds patents and stock from Telix Pharmaceuticals. CBe is an employee of Telix Pharmaceuticals. CRWH was an employee of Telix Pharmaceuticals at the time of the trial. PM has received research funding from Telix Pharmaceuticals., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Abstract: Targeted immunotherapy became the most advanced approach for cancer treatment. Programmed death-1 (PD-1) expressed on activated T cells can reverse immune suppression and cause T-cell activation. Nivolumab, a PD-1 immune checkpoint inhibitor antibody that is a fully human immunoglobulin G4, blocks PD-1 and promotes antitumor immunity. Cabozantinib (tyrosine kinase inhibitor) inhibits the tyrosine kinase activity of vascular endothelial growth factor receptors 1, 2, and 3. As a result of enhancing immune response in normal tissues, immune-related adverse events can occur. Thyroid dysfunction is a common form of immune-related adverse event and seen on 18 F-FDG PET/CT scans post therapy., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Multilocular cystic nephroma (MLCN) is an unusual, benign slow-growing renal cystic neoplasm which mimics other cystic renal lesions and has such clinical, radiological, and morphological features that causes diagnostic dilemma. MLCN lies in the spectrum of mixed epithelial and stromal tumor (MEST) family of kidney. According to World Health Organization (WHO 2016 classification), MEST encompasses spectrum of tumors ranging from predominantly cystic tumors, adult cystic nephroma (ACN) to tumors that are variably solid (MEST), thus creating diagnostic dilemma. Moreover, it has several benign and malignant differentials due to its several overlapping histomorphological features which when not cautiously dealt with may result in misdiagnosing it as malignant lesion. We hereby present a case of a woman in late twenties who presented with left flank swelling and pain since 6 months which was misdiagnosed as renal cell carcinoma on radiology which turned out to be ACN on histology and further verified on immunohistochemistry., (© 2024. The Author(s), under exclusive licence to Japanese Society of Nephrology.)

Objectives: To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization., Methods: 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM., Results: Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM., Conclusion: Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM., Clinical Relevance: Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Fumarate hydratase-deficient renal cell cancer (FHRCC) is a rare and aggressive form of renal cell carcinoma. The diagnostic value of 68 Ga-FAPI PET/CT for FHRCC remains unexplored. Therefore, we compared the potential value of 68 Ga-FAPI-04 and 18 F-FDG PET/CT in FHRCC., Patients and Methods: Patients with FHRCC underwent 68 Ga-FAPI-04 and 18 F-FDG PET/CT from May 2022 to December 2023. The SUV max and tumor-to-liver ratio (TLR) of both tracers were compared using the Wilcoxon signed rank test., Results: Eleven patients with 83 lesions were enrolled. The rate of 18 F-FDG PET/CT in detecting lesions was higher than that of 68 Ga-FAPI-04 PET/CT: primary tumors: 75.0% (6/8) versus 50.0% (4/8); lymph nodes: 94.9% (37/39) versus 89.7% (35/39); and bone lesions: 100.0% (21/21) versus 90.5% (19/21). The median SUV max of primary and metastatic lesions on 18 F-FDG PET/CT was comparable to 68 Ga-FAPI-04 PET/CT in semiquantitative analysis (primary lesions: 13.86 vs 16.35, P = 1.000; lymph nodes: 10.04 vs 9.33, P = 0.517; bone lesions: 13.49 vs 9.84, P = 0.107; visceral lesions: 8.54 vs 4.20, P = 0.056). However, the median TLRs of primary and metastatic lesions on 68 Ga-FAPI-04 PET/CT were higher than that of 18 F-FDG PET/CT (primary lesions: 30.44 vs 5.41, P = 0.010; lymph nodes: 17.71 vs 3.95, P = 0.000; bone lesions: 15.94 vs 5.21, P = 0.000; visceral lesions: 9.26 vs 3.44, P = 0.003)., Conclusions: 18 F-FDG PET/CT detected more primary and metastatic FHRCC lesions than 68 Ga-FAPI-04 PET/CT. However, the higher TLR in FHRCC on 68 Ga-FAPI-04 PET/CT may indicate therapeutic potential in targeting fibroblast activation protein in FHRCC., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Rationale and Objectives: This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs)., Material and Methods: This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model., Results: There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001)., Conclusion: The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Indeterminate renal masses are increasingly incidentally found on cross-sectional imaging. 99m Tc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) scans can be used to identify oncocytomas and oncocytic renal neoplasms, including a subset of chromophobe renal cell carcinomas (chRCCs), which are viewed as false-positive., Procedure: Patients imaged with renal sestamibi scans between 2014 and 2023 were reviewed. Those patients with solitary tumors that were originally classified as chRCC were included in the analysis. Imaging with SPECT/CT from the liver dome down had been carried out 75 min after the administration of 925 MBq of 99m Tc-sestamibi. All available H&E and immunostained slides were re-reviewed and classified according to WHO 2022 criteria. Confirmatory immunohistochemical stains were performed in tumors considered morphologically suspicious for non-chRCC entities., Result: A total of 18 patients with solitary tumors were included in the final analysis. 13/18 (72.2%) tumors in this cohort remained classified as chRCC, with 4/18 (22.2%) being eosinophilic-variant chRCC. The reclassified tumors (5/18 [27.8%]) included 2/18 (11.1%) low-grade oncocytic tumor (LOT), 1/18 (5.5%) eosinophilic vacuolated tumor (EVT), and 2/18 (11.1%) unclassified low-grade oncocytic neoplasms. As such, only 2/9 (22.2%) qualitatively "hot" tumors were chRCC other than eosinophilic-variant and only 1/9 (11.1%) "cold" tumors was a histology other than chRCC., Conclusion: Based on current histopathologic classification methods, it is likely that the "false-positive" rate of uptake on renal sestamibi scans with chRCC has been over-stated. Further study is warranted to better refine the optimal utility of renal sestamibi scans for non-invasive risk stratification of indeterminate renal masses., (© 2024. The Author(s), under exclusive licence to World Molecular Imaging Society.)

Purpose: This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC)., Methods: A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score., Results: In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5 year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5 year DFS compared to UISS and SSIGN scores., Conclusions: The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT)., Material and Methods: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center., Results: The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify., Clinical Relevance Statement: Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort., (© 2024. The Author(s).)

Purpose: Dialysis patients are at an increased risk of developing renal cell carcinoma (RCC); however, differentiating between RCC and benign cysts can sometimes be difficult using modalities, such as computed tomography (CT) and ultrasonography. 18 F-Fluorodeoxyglucose positron emission tomography (FDG-PET)/CT efficiently detects malignant tumors; however, physiological accumulation of FDG in the kidney limits its efficacy in detecting renal tumors. However, in patients with severely impaired renal function, the renal accumulation of FDG is decreased, possibly improving the detection of renal malignancies in this patient population. This study evaluated the usefulness of FDG-PET/CT as a screening tool for detecting RCC in patients with end-stage renal disease., Materials and Methods: This prospective study recruited 150 participants from 2012 to 2016 who were on dialysis or underwent renal transplantation and were on dialysis until transplantation. FDG-PET/CT was performed to screen for RCC. Three radiologists independently evaluated the images. No protocol was defined for the additional management of positive examinations, leaving decisions to the discretion of each participant. Negative examinations were observed until the end of 2019., Results: In total, 150 participants (mean age, 58 ± 13 years; 105 men) underwent FDG-PET/CT. Twenty patients (13.4%) were diagnosed as positive. Fifteen patients underwent additional examinations and/or procedures, and RCC was found in seven patients. Of the four patients who underwent surgical resection, the pathological results were clear cell RCC in one, papillary RCC in one, and acquired cystic disease-associated RCC in two. Two participants were diagnosed with RCC on bone biopsy, and one was diagnosed on dynamic CT but opted for observation. The sensitivity, specificity, and negative predictive value were 100%, 93.9%, and 100%, respectively., Conclusion: FDG-PET/CT was useful for detecting RCC in patients with end-stage renal disease. Our findings show the potential use of FDG-PET/CT as a screening tool for RCC in this patient population., (© 2024. The Author(s).)

Purpose: Metabolite-specific balanced SSFP (MS-bSSFP) sequences are increasingly used in hyperpolarized [1- 13 C]Pyruvate (HP 13 C) MRI studies as they improve SNR by refocusing the magnetization each TR. Currently, pharmacokinetic models used to fit conversion rate constants, k PL and k PB , and rate constant maps do not account for differences in the signal evolution of MS-bSSFP acquisitions., Methods: In this work, a flexible MS-bSSFP model was built that can be used to fit conversion rate constants for these experiments. The model was validated in vivo using paired animal (healthy rat kidneys n = 8, transgenic adenocarcinoma of the mouse prostate n = 3) and human renal cell carcinoma (n = 3) datasets. Gradient echo (GRE) acquisitions were used with a previous GRE model to compare to the results of the proposed GRE-bSSFP model., Results: Within simulations, the proposed GRE-bSSFP model fits the simulated data well, whereas a GRE model shows bias because of model mismatch. For the in vivo datasets, the estimated conversion rate constants using the proposed GRE-bSSFP model are consistent with a previous GRE model. Jointly fitting the lactate T 2 with k PL resulted in less precise k PL estimates., Conclusion: The proposed GRE-bSSFP model provides a method to estimate conversion rate constants, k PL and k PB , for MS-bSSFP HP 13 C experiments. This model may also be modified and used for other applications, for example, estimating rate constants with other hyperpolarized reagents or multi-echo bSSFP., (© 2024 International Society for Magnetic Resonance in Medicine.)

Laminin, fibronectin, and collagen IV are pivotal extracellular matrix (ECM) components. The ECM environment governs the fundamental properties of tumors, including proliferation, vascularization, and invasion. Given the critical role of cell-matrix adhesion in malignant tumor progression, we hypothesize that the concentrations of these proteins may be altered in the plasma of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate the serum, urine, and tissue levels of laminin-5, collagen IV, and fibronectin among a control group and ccRCC patients, with the latter divided into stages T1-T2 and T3-T4 according to the TNM classification. We included 60 patients with histopathologically confirmed ccRCC and 26 patients diagnosed with chronic cystitis or benign prostatic hyperplasia (BPH). Collagen IV, laminin-5, and fibronectin were detected using Surface Plasmon Resonance Imaging biosensors. Significant differences were observed between the control group and ccRCC patients, as well as between the T1-T2 and T3-T4 subgroups. Levels were generally higher in plasma and tissue for fibronectin and collagen IV in ccRCC patients and lower for laminin. The ROC (Receiver operating characteristic) analysis yielded satisfactory results for differentiating between ccRCC patients and controls (AUC 0.84-0.93), with statistical significance for both fibronectin and laminin in plasma and urine. Analysis between the T1-T2 and T3-T4 groups revealed interesting findings for all examined substances in plasma (AUC 0.8-0.95). The results suggest a positive correlation between fibronectin and collagen levels and ccRCC staging, while laminin shows a negative correlation, implying a potential protective role. The relationship between plasma and urine concentrations of these biomarkers may be instrumental for tumor detection and staging, thereby streamlining therapeutic decision-making.

Retroperitoneal neuroendocrine tumours are exceptionally rare. The excision of tumours located in the renal hilum near the renal vessels can be challenging. We report a case of a paraganglioma located at the renal hilum which was excised successfully in a child who presented with abdominal pain, breathlessness, left varicocele and hypertension., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC)., Methods: This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant., Results: Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04-0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849-0.856) across different folds., Conclusion: The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC., (© 2024. The Author(s).)

Purpose: The management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC., Methods: A systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools., Results: The systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55-0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from - 5 to 20, with a mean RQS total of 9.15 ± 7.95., Conclusion: Radiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment., Methods: The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results., Results: At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05)., Conclusion: The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment., (© 2024. The Author(s).)

Rationale: The different variants of renal angiomyolipoma pose diagnostic and therapeutic challenges in clinical practice. We report a rare case of renal solid-cystic angiomyolipoma, with the aim of offering new insights into the preoperative imaging assessment of renal masses., Patient Concerns: A 72-year-old female was admitted to our hospital because of a solid-cystic mass discovered in her right kidney during an abdominal computed tomography scan at another hospital. Her medical history includes a 5-year history of hypertension treated with medication, as well as hepatic cysts and bilateral renal cysts., Diagnoses: The postoperative pathological diagnosis is renal solid-cystic angiomyolipoma., Interventions: The solid-cystic mass in the right kidney was surgically removed via laparoscopic partial nephrectomy under general anesthesia., Outcomes: The patient had an uneventful recovery and was discharged on the second postoperative day without complications., Lessons: Renal angiomyolipoma is usually easily distinguishable on imaging, but this case aims to alert clinicians to differentiate the rare variants of renal angiomyolipoma from other renal tumors. In the future, more cases are needed to summarize the characteristics of different variants of renal angiomyolipoma., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

This study aims to evaluate the clinical diagnostic value of contrast-enhanced ultrasound combined with microflow imaging (CEUS-MFI) in the differential diagnosis of benign and malignant renal tumors. All patients underwent CEUS, MFI, color doppler flow imaging (CDFI), and CEUS-MFI. The efficacies of these different diagnostic modalities in diagnosing benign and malignant renal tumors were evaluated by Kappa consistency test and the receiver operating characteristic (ROC) curve, with pathological findings serving as the gold standard. CDFI, MFI and CEUS-MFI all demonstrated higher blood flow in malignant tumors compared with benign tumors. Compared with benign tumors, CDFI detected a higher rate of punctate and linear Adler grade 2 and 3 blood flows in malignant tumors, as well as peripheral semicircular or annular blood flow. MFI identified a high rate of peripheral circumferential blood flow and irregular vascular morphology in malignant tumors, with most exhibiting Adler grade 3 blood flow. In addition, CEUS-MFI showed more dendritic or irregular Adler grade 2 or 3 blood flows in malignant renal tumors than MFI alone. Further analysis showed that CEUS-MFI had the highest consistency with pathological diagnosis (Kappa = 0.808). The ROC curve showed that the area under the curve (AUC) for CEUS-MFI in differentiating between benign and malignant lesions was 0.898, significantly outperforming other single diagnostic methods. With its capability to display microvascular information and assess overall pathological characteristics, MFI can accurately predict the nature of renal tumors and assist in surgical planning.

Objective: Contrast-enhanced computed tomography (CECT) improves lesion contrast with surrounding tissues through the injection of contrast agents. This enhancement allows for more precise lesion characterization, aiding in the early diagnosis of clear cell renal cell carcinoma (ccRCC). This meta-analysis aims to assess the diagnostic efficacy of CECT in ccRCC and to provide an ideal imaging examination method for the preoperative diagnosis of ccRCC., Methods: We conducted a comprehensive search across six major online databases: PubMed, Web of Science, Cochrane Library, WANFANG DATA, China National Knowledge Infrastructure, and Chinese BioMedical Literature Database (CBM). The objective was to collate and analyze studies that evaluate the diagnostic utility of CECT in the identification of ccRCC. Meta-disc 1.4 and Stata 16.0 were used to conduct a meta-analysis and evaluate the diagnostic accuracy of CECT for ccRCC., Results: The meta-analysis included 17 relevant studies investigating the diagnostic value of CECT for ccRCC. The combined sensitivity and specificity of CECT were 0.88 (95% confidence interval: 0.83-0.91) and 0.82 (95%CI: 0.75-0.87), respectively. Positive diagnostic likelihood ratio = 4.87 (95%CI: 3.47-6.84), negative diagnostic likelihood ratio = 0.15 (95%CI: 0.11-0.21), and diagnostic odds ratio = 32.67 (95%CI: 18.21-58.61). In addition, the area under the ROC curve was 0.92 (95%CI: 0.89-0.94), indicating that CECT has a decent discriminative ability in diagnosing ccRCC., Conclusions: CECT is recognized as a highly effective imaging tool for diagnosing ccRCC. It provides valuable guidance in the preoperative assessment and planning of surgical strategies for patients with ccRCC., (© 2024. The Author(s).)

Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99m Tc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed., Competing Interests: Declaration of competing interest SPR is a consultant for, has equity in, and has received research funding from Precision Molecular, Inc. SPR and MAG are consultants for Lantheus. SPR and MAG are consultants for Telix. JDO has been a consultant for Telix., (Copyright © 2024 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Rationale and Objectives: Radiogenomics of clear cell renal cell carcinoma (ccRCC) has been developed thanks to the availability of genomic data, both gene expressions and gene mutations, obtained through the sequencing of ccRCC genome. These data are collected in the Cancer Genome Atlas (TCGA) Research Network-work. Disintegrin and metalloproteinase domain-containing protein 12 (ADAM12) gene belongs to the family of genes coding for multidomain and multifunctional type I transmembrane proteins ADAMs. These proteins are fundamental for regulating cell adhesion and mediating proteolysis of a series of cell surface receptors and signal molecules extracellular domains. Recently, a correlation was detected between ADAM12 expression in ccRCC and tumor aggressiveness in terms of cell proliferation, migration, invasion, tumor progression, metastases, and poor prognosis, suggesting ADAM12 as a prognostic marker and therapeutic target in ccRCC. The computed tomography (CT) imaging phenotype of ADAM12 expression in ccRCC has never been studied. The aim of this study is to investigate the CT imaging phenotype of ADAM12 expression in ccRCC patients., Materials and Methods: In this retrospective study, we enrolled 202 ccRCC patients divided into two groups: ccRCC patients with ADAM12 expression (n = 35) and ccRCC patients without ADAM12 expression (n = 167). Different imaging features were evaluated on CT scan at first diagnosis. The statistical significance threshold was set at p < 0.05., Results: A statistically significant correlation was found with larger primary tumor size (p = 0.020), ill-defined tumor margins (p = 0.044), tumor necrosis (p = 0.011), and collecting system invasion (p = 0.014)., Conclusion: This study demonstrates CT imaging features associated to ADAM12 expression in ccRCC. These results could help delve into ADAM12 gene status through CT approach and to further investigate towards the development of targeted therapies in ccRCC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: To evaluate the technical success and outcomes of renal biopsies performed under magnetic resonance imaging (MRI) using a closed-bore, 1.5-Tesla MRI unit., Materials and Methods: We retrospectively reviewed our institutional biopsy database and included 150 consecutive MRI-guided biopsies for renal masses between November 2007 and March 2020. We recorded age, sex, BMI, tumor characteristics, RENAL nephrometry score, MRI scan sequence, biopsy technique, complications, diagnostic yield, pathologic outcome, and follow-up imaging. Univariate logistic regression was used to assess the association between different parameters and the development of complications. McNemar's test was used to assess the association between paired diagnostic yield measurements for fine-needle aspiration and core samples., Results: A total of 150 biopsies for 150 lesions were performed in 150 patients. The median tumor size was 2.7 cm. The median BMI was 28.3. The lesions were solid, partially necrotic/cystic, and predominantly cystic in 137, eight, and five patients, respectively. Image guidance using fat saturation steady-state free precession sequence was recorded in 95% of the biopsy procedures. Samples were obtained using both fine-needle aspiration (FNA) and cores in 99 patients (66%), cores only in 40 (26%), and FNA only in three (2%). Tissue sampling was diagnostic in 144 (96%) lesions. No major complication developed following any of the biopsy procedures. The median follow-up imaging duration was 8 years and none of the patients developed biopsy-related long-term complication or tumor seeding., Conclusions: MRI-guided renal biopsy is safe and effective, with high diagnostic yield and no major complications., Clinical Relevance Statement: Image-guided renal biopsy is safe and effective, and should be included in the management algorithm of patients with renal masses. Core biopsy is recommended., Key Points: • MRI-guided biopsy is a safe and effective technique for sampling of renal lesions. • MRI-guided biopsy has high diagnostic yield with no major complications. • Percutaneous image-guided biopsy plays a key role in the management of patients with renal masses., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Purpose: To evaluate retrospectively the influence of percutaneous cryoablation for small renal tumors on total and affected kidney function and risk factors associated with worsening function of the affected kidney., Materials and Methods: Between April 2016 and March 2022, 27 patients who underwent cryoablation for small renal tumors at our institution participated in this study, which investigated time-dependent changes in postoperative renal function. We evaluated estimated glomerular filtration rates (eGFRs) and split renal function revealed by scintigraphy using 99 m technetium-mercaptoacetyltriglycine (99mTc-MAG3) before cryoablation and at 1 week, 1 month, and 6 months after cryoablation. Numerous variables were analyzed to assess risk factors for worsening renal function., Results: Baseline eGFR (mean ± standard deviation) was 56.5 ± 23.7 mL/min/1.73 m 2 (mean ± SD; range, 20.5-112.5). Mean eGFRs at 1 week, 1 month, and 6 months after cryoablation were 57.4 ± 24.5 (19.1-114.9), 57.1 ± 25.1 (21.5-114.9), and 53.8 ± 23.9 mL/min/1.73 m 2 (20.0-107.5), respectively. Changes were statistically insignificant (p = 1.0000, = 0.6749, and = 0.0761, respectively). Regarding split renal function, mean baseline contribution of the affected kidney determined by 99mTc-MAG3 was 49.7% ± 6.0% (38.8-63.3%); these rates at 1 week, 1 month, and 6 months after cryoablation were 43.7% ± 8.8 (29.1-70.6%), 46.2% ± 7.7% (32.6-70.3%), and 46.0% ± 8.5% (32.5-67.6%), respectively. Differences from baseline were significant for all periods (p < 0001, < 0001, = 0.0001, respectively). Serum C reactive protein and lactate dehydrogenase at 1 day following cryoablation, tumor's nearness to the collecting system or sinus, and volume of ablated normal renal parenchyma were significantly correlated with decreased contributions of the affected kidney by > 10% after cryoablation., Conclusion: Unlike total renal function, affected kidney function could worsen after cryoablation., (© 2024. The Author(s).)

Objective: Previous research on diagnostic assessment by superb microvascular imaging (SMI) were based on qualitative or semi-quantitative assessments of vascularity, which may be subjective and unrepeatable by different sonographers. This study aimed to evaluate diagnostic performance of SMI Image-pro Plus (IPP) based vascular index (VI) for malignant renal masses., Method: We retrospectively reviewed 222 masses in 214 patients who underwent SMI between August 2019 and August 2022 in our study. We evaluated the diagnostic performance of blood flow via Alder grade, VI based on both IPP and SMI., Results: The kappa consistency of the Adler grade and VI for renal masses was classified among different observers were 0.765 and 0.824. The intra-observers correlation ecoefficiency (ICC) were 0.727 and 0.874. Benign renal masses were mainly Adler grade 0, grade I, and grade II, VI was 4.30 ± 4.27 (Range 0.98-16.42); while malignant masses were mainly Adler grade III, VI was 14.95 ± 10.94 (Range 0.79-56.89). VI was higher in malignant than benign masses (t = 15.638, P < 0.01). Among the malignant masses, the mean VI in clear cell renal cell carcinoma was higher than that in papillary renal cell carcinoma and chromophobe renal cell carcinoma (F = 30.659, P < 0.01). The sensitivity, specificity and accuracy of SMI were 80.00%, 71.15%, and 78.64%, respectively. The sensitivity, specificity, and accuracy were 60.59%, 88.46%, and 80.18% by using a VI of 7.95 as the cutoff value to identify malignant lesions from benign masses yielded. VI had better diagnostic efficiency than ultrasonic characteristics and Adler grade in benign and malignant differential diagnosis (Z = 4.851, P < 0.01; Z = 2.732, P < 0.01)., Conclusion: VI was higher in malignant than benign in renal masses. In malignant masses, VI in CCRCC was higher than that in papillary renal cell carcinoma and ChRCC. As a noninvasive examination, it had important clinical significance in the differential diagnosis of renal masses. VI from IPP may assist sonographer in distinguish renal malignances as a quantitative tool for vascularity., Competing Interests: Declaration of competing interest The authors have declared that no competing interests exist., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Abstract: A 47-year-old man with a history of metastatic non-clear cell left renal cell carcinoma, unclassified subtype, status post left radical nephrectomy 4 years prior, and treated with immunotherapy for approximately 2½ years, presented for 18 F-FDG PET/CT exam 7 months after immunotherapy was stopped. A contrast-enhanced CT exam performed 3 weeks prior demonstrated a new small bowel intussusception in the left upper quadrant. The PET/CT demonstrated focal FDG uptake in the segment of small bowel involved in the intussusception. Pathology from small bowel resection demonstrated metastatic renal cell carcinoma., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To develop radiomics-based classifiers for preoperative prediction of fibrous capsule invasion in renal cell carcinoma (RCC) patients by CT images., Methods: In this study, clear cell RCC (ccRCC) patients who underwent both preoperative abdominal contrast-enhanced CT and nephrectomy surgery at our hospital were analysed. By transfer learning, we used base model obtained from Kidney Tumour Segmentation challenge dataset to semi-automatically segment kidney and tumours from corticomedullary phase (CMP) CT images. Dice similarity coefficient (DSC) was measured to evaluate the performance of segmentation models. Ten machine learning classifiers were compared in our study. Performance of the models was assessed by their accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC). The reporting and methodological quality of our study was assessed by the CLEAR checklist and METRICS score., Results: This retrospective study enrolled 163 ccRCC patients. The semiautomatic segmentation model using CMP CT images obtained DSCs of 0.98 in the training cohort and 0.96 in the test cohort for kidney segmentation, and DSCs of 0.94 and 0.86 for tumour segmentation in the training and test set, respectively. For preoperative prediction of renal capsule invasion, the AdaBoost had the best performance in batch 1, with accuracy, precision, recall, and F1-score equal to 0.8571, 0.8333, 0.9091, and 0.8696, respectively; and the same classifier was also the most suitable for this classification in batch 2. The AUCs of AdaBoost for batch 1 and batch 2 were 0.83 (95% CI: 0.68-0.98) and 0.74 (95% CI: 0.51-0.97), respectively. Nine common significant features for classification were found from 2 independent batch datasets, including morphological and texture features., Conclusions: The CT-based radiomics classifiers performed well for the preoperative prediction of fibrous capsule invasion in ccRCC., Advances in Knowledge: Noninvasive prediction of renal fibrous capsule invasion in RCC is rather difficult by abdominal CT images before surgery. A machine learning classifier integrated with radiomics features shows a promising potential to assist surgical treatment options for RCC patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Great strides have been made identifying molecular and genetic changes expressed by various tumor types. These molecular and genetic changes are used as pharmacologic targets for precision treatment using large molecule (LM) proteins with high specificity. Theranostics exploits these LM biomolecules via radiochemistry, creating sensitive diagnostic and therapeutic agents. Intravenous (i.v.) LM drugs have an extended biopharmaceutical half-life thus resulting in an insufficient therapeutic index, permitting only palliative brachytherapy due to unacceptably high rates of systemic nontarget radiation doses to normal tissue. We employ tumor arteriole embolization isolating a tumor from the systemic circulation, and local intra-arterial (i.a.) infusion to improve uptake of a LM drug within a porcine renal tumor (RT)., Methods: In an oncopig RT we assess the in vivo biodistribution of 99m Tc-labeled macroaggregated albumin (MAA) a surrogate for a LM theranostics agent in the RT, kidney, liver, spleen, muscle, blood, and urine. Control animals underwent i.v. infusion and experimental group undergoing arteriography with pseudovascular isolation (PVI) followed by direct i.a. injection., Results: Injected dose per gram (%ID/g) of the LM at 1 min was 86.75 ± 3.76 and remained elevated up to 120 min (89.35 ± 5.77) with i.a. PVI, this increase was statistically significant (SS) compared to i.v. (13.38 ± 1.56 and 12.02 ± 1.05; p = 0.0003 p = 0.0006 at 1 and 120 min respectively). The circulating distribution of LM in the blood was less with i.a. vs i.v. infusion (2.28 ± 0.31 vs 25.17 ± 1.84 for i.v. p = 0.033 at 1 min). Other organs displayed a trend towards less exposure to radiation for i.a. with PVI compared to i.v. which was not SS., Conclusion: PVI followed by i.a. infusion of a LM drug has the potential to significantly increase the first pass uptake within a tumor. This minimally invasive technique can be translated into clinical practice, potentially rendering monoclonal antibody based radioimmunotherapy a viable treatment for renal tumors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objective: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up., Materials and Methods: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis., Results: Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI., Conclusions: This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future., Clinical Relevance Statement: These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics., Key Points: • This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions., (© 2024. The Author(s).)

Rationale and Objectives: To establish a computed tomography (CT)-based radiomics model to predict Fork head box M1(FOXM1) expression levels and develop a combined model for prognostic prediction in patients with clear cell renal cell carcinoma (ccRCC)., Materials and Methods: A total of 529 patients were utilized to assess the prognostic significance of FOXM1 expression and were subsequently categorized into low and high FOXM1 expression groups. 184 patients with CT images were randomly divided into training and validation cohorts. Radiomics signature (Rad-score) for predicting FOXM1 expression level was developed in the training cohort. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. A clinical model based on clinical factors and a combined model incorporating clinical factors and Rad-score were developed to predict ccRCC prognosis using Cox regression analyses. The concordance index(C-index) was employed to assess and compare the predictive capabilities of the Rad-score, TNM stage, clinical model, and combined model. The likelihood ratio test was used to compare the models' performance., Results: The Rad-score demonstrated high predictive accuracy for high FOXM1 expression with areas under the ROC curves of 0.713 and 0.711 in the training and validation cohorts. In the training cohort, the C-indexes for the Rad-score, TNM Stage, clinical model, and combined model were 0.657, 0.711, 0.737, and 0.741, respectively. Correspondingly, in the validation cohort, the C-indexes were 0.670, 0.712, 0.736, and 0.745. The combined model had the highest C-index, significantly outperforming the other models., Conclusion: The Rad-score accurately predicts FOXM1 expression levels and is an independent prognostic factor for ccRCC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Automatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra- and inter-operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their uncertainty., Purpose: This work aims to evaluate the uncertainty quantification provided by two Bayesian and two non-Bayesian approaches for a multi-class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC)., Methods: Four uncertainty quantification approaches were implemented in this work, based on a benchmark CNN currently employed in medical image segmentation: two Bayesian CNNs with different regularizations (Dropout and DropConnect), named BDR and BDC, an ensemble method (Ens) and a test-time augmentation (TTA) method. They were compared in terms of segmentation accuracy, using the Dice score, uncertainty quantification, using the ratio of correct-certain pixels (RCC) and incorrect-uncertain pixels (RIU), and with respect to inter-observer variability in manual segmentation. They were trained with the Kidney and Kidney Tumor Segmentation Challenge launched in 2021 (Kits21), for which multi-class segmentations of kidney, RC, and cyst on 300 CT volumes are available. Moreover, they were tested considering this and other two public renal CT datasets., Results: Accuracy results achieved large differences across the structures of interest for all approaches, with an average Dice score of 0.92, 0.58, and 0.21 for kidney, tumor, and cyst, respectively. In terms of uncertainties, TTA provided the highest uncertainty, followed by Ens and BDC, whereas BDR provided the lowest, and minimized the number of incorrect certain pixels worse than the other approaches. Again, large differences were seen across the three structures in terms of RCC and RIU. These metrics were associated with different risk propensity, as BDR was the most risk-taking approach, able to provide higher accuracy in its prediction, but failing to assign uncertainty on incorrect segmentation in every case. The other three approaches were more conservative, providing large uncertainty regions, with the drawback of giving alert also on correct areas. Finally, the analysis of the inter-observer segmentation variability showed a significant variation among the four approaches on the external dataset, with BDR reporting the lowest agreement (Dice = 0.82), and TTA obtaining the highest score (Dice = 0.94)., Conclusions: Our outcomes highlight the importance of quantifying the segmentation uncertainty and that decision-makers can choose the approach most in line with the risk propensity degree required by the application and their policy., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019)., Materials and Methods: The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T 2 -weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis., Results: MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T 2 WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%., Conclusion: The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Purpose: To develop a highly accelerated multi-echo spin-echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T 2 mapping., Methods: TEMPURA merges several adjacent echoes into one k-space by either combining independent echoes or sharing one echo between k-spaces. The combined k-space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath-hold sequence; and a high-resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors., Results: The fast TEMPURA method reduced the acquisition time from 3-5 min to one breath-hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory-triggered sequence (7.4%), but much lower than a sequence accelerated by purely k-t undersampling (21.8%). High-resolution TEMPURA reduced the in-plane voxel size from 3 × 3 to 1 × 1 mm 2 , resulting in improved visualization of the detailed anatomical structure. In vivo T 2 measurements demonstrated good agreement (fast: MAPE = 1.3%-2.5%; high-resolution: MAPE = 2.8%-3.3%) and high correlation coefficients (fast: R = 0.85-0.98; high-resolution: 0.82-0.96) with the standardized method, outperforming k-t undersampling alone (MAPE = 3.3-4.5%, R = 0.57-0.59)., Conclusion: TEMPURA provides fast and high-resolution renal T 2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

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., (Published 2024. This article is a U.S. Government work and is in the public domain in the USA.)

Background: To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors., Materials and Methods: In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach., Results: A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original&#95;shape&#95;Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method., Conclusions: The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance., (© 2024. The Author(s).)

Objective: Retrospectively evaluate multimodality imaging features of perinephric myxoid pseudotumor of fat (PMPTF)., Methods: Institutional cases of PMPTF with CT, MRI and/or ultrasound evaluation from 1/1/2020 to 9/1/2023 were retrospectively reviewed. Patient demographics and clinical history were reviewed, and imaging features recorded., Results: 14 patients with pathologically-proven PMPTF were identified (11 M, 3 F; mean age 66.7 ± 17.0 years; range 40-87 years). Three patients (18%) had bilateral lesions; a total of 17 PMPTFs were reviewed. 15/17 (88%) were biopsy-proven; two cases were diagnosed by imaging only in patients with a contralateral biopsy-proven PMPTF. All evaluable specimens were negative for MDM2 amplification. 11/17 (65%) occurred in patients with renal disease, including 4/17 (24%) in patients with renal transplant. 100% (17/17) had CT, 11/17 (65%) MRI, and 6/17 (35%) ultrasound. The mean largest lesion dimension was 10.9 ± 4.6 cm (range 4.3-17.0 cm). Of cases involving native kidneys, 7/13 (54%) presented as multifocal perinephric masses and 5/13 (38%) as a solitary perinephric mass. All four transplant cases presented as infiltrative-appearing masses involving the renal sinus with lesser perinephric involvement. 14/17 (82%) lesions contained macroscopic fat on CT and MRI and 3/17 (18%) showed no macroscopic fat, all involving renal transplants. All cases with MRI demonstrated T2 hyperintensity with signal dropout on opposed-phase imaging. 11/13 (85%) PMPTF showed no or equivocal CT enhancement. Enhancement was better seen on MRI in all cases evaluated by both CT and MRI. Of the six PMPTFs imaged by ultrasound, four (67%) were heterogeneously hypoechoic and two (33%) had mixed regions of hypo-, iso- and hyperechogenicity relative to adjacent renal parenchyma., Conclusions: PMPTF is a rare, benign, and underrecognized lesion that may mimic malignancy, particularly retroperitoneal well-differentiated liposarcoma. The imaging features of this unusual pseudosarcoma differ in native and transplanted kidneys. Improved awareness of this entity will facilitate appropriate patient management and avoid unnecessary intervention., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Horseshoe kidney is the most common renal fusion anomaly, and Wilms tumor is the most frequent renal malignancy in children. The occurrence of Wilms tumor in association with horseshoe kidney is a scarce anomaly. However, the arising of a teratoid type, which is a rare variant of Wilms tumor in a horseshoe kidney, is exceptionally unique., Case Presentation: This report presents a 5-year-old male admitted with horseshoe kidney involved by a large heterogeneous calcified mass that was diagnose on biopsy as Wilms tumor blastemal dominant. According to the local and regional extension and metastatic tumor in the lungs, the patient underwent neoadjuvant chemotherapy and then surgery. Post-operative pathologic findings confirmed the diagnosis of teratoid Wilms tumor., Conclusions: The occurrence of renal anomalies associated with a malignancy might be more frequent in the clinical environment. There are numerous differential diagnoses for renal tumors and masses, but the possibility of exceptional anomalies should not be denied, and clinicians should be prepared for these occasions. Although studies propose that chemotherapy has a trivial effect on teratoid Wilms tumors, it is essential to evaluate the tumor for any possibility of regression in non-teratoid regions before proceeding to upfront tumoral resection., (© 2024. The Author(s).)

Introduction: The effective accumulation of nanoparticles (NPs) in the tumour area is an important goals of current nanotechnology research, and a targeted nanoplatform is an effective solution. So we designed a multifunctional sound-sensitive targeted NP that combines a sonosensitizer to enable precisely targeted, deep-penetration sonodynamic therapy (SDT) in combination with multimodal imaging for the diagnosis and monitoring of renal cell carcinoma (RCC)., Methods: ZnPP@PP NPs (ZnPP@PLGA- PFP NPs) were prepared via a double emulsion method, and G250 was covalently attached to the NPs shell via the carbon diimide method. Physicochemical property tests were conducted on the ZnPP@G-PP NPs, including tests of particle size, potential distribution, encapsulation efficiency and drug loading capability. We assessed the targeting ability, the production of reactive oxygen species (ROS) and permeability of the NPs in vitro. Moreover, we evaluated the nanoparticle's multimodal imaging capabilities and therapeutic ability against RCC, both in vitro and in vivo., Results: The Znpp@G-PP NPs were successfully constructed, and their general properties showed uniform particle size, negative potential and good stability. The nanoparticles were successfully loaded with ZnPP and connected with G250, showing tumor-specific targeting ability. Under LIFU irradiation, the nanoparticles produced 1 O 2 by SDT. For RCC, PA/US multi-modal imaging of Znpp@G-PP NPs provide diagnostic information and monitor therapies in real time in 786-O RCC xenografts, with good biocompatibility. With the UTMD, nanoparticles can be effectively targeted into the tumor cells and penetrate into the tumor interior, significantly improving the SDT effect. Experiments in vitro and in vivo showed that the combination of the nanoparticles and LIFU could suppress the tumor, and the therapeutic effect was confirmed by immunohistochemistry., Conclusion: ZnPP@G-PP NPs provide a promising theranostic strategy for RCC and a platform for further research on improving the efficacy of diagnosis and treatment., Competing Interests: The authors report no conflicts of interest in this work., (© 2024 Dai et al.)

Background: PET/CT imaging with Zirconium-89 labeled [89Zr]Zr-DFO-girentuximab, which targets tumor antigen CAIX, may aid in the differentiation and characterization of clear cell renal cell carcinomas (RCC) and other renal and extrarenal lesions, and has been studied in European and American cohorts. We report results from a phase I study that evaluated the safety profile, biodistribution, and dosimetry of [89Zr]Zr-DFO-girentuximab in Japanese patients with suspected RCC., Methods: Eligible adult patients received 37 MBq (± 10%; 10 mg mass dose) of intravenous [89Zr]Zr-DFO-girentuximab. Safety and tolerability profile was assessed based on adverse events, concomitant medications, physical examination, vital signs, hematology, serum chemistry, urinalysis, human anti-chimeric antibody measurement, and 12-lead electrocardiograms at predefined intervals. Biodistribution and normal organ and tumor dosimetry were evaluated with PET/CT images acquired at 0.5, 4, 24, 72 h and Day 5 ± 2 d after administration., Results: [89Zr]Zr-DFO-girentuximab was administered in six patients as per protocol. No treatment-emergent adverse events were reported. Dosimetry analysis showed that radioactivity was widely distributed in the body, and that the absorbed dose in healthy organs was highest in the liver (mean ± standard deviation) (1.365 ± 0.245 mGy/MBq), kidney (1.126 ± 0.190 mGy/MBq), heart wall (1.096 ± 0.232 mGy/MBq), and spleen (1.072 ± 0.466 mGy/MBq). The mean effective dose, adjusted by the radioactive dose administered, was 0.470 mSv/MBq. The radiation dose was highly accumulated in the targeted tumor, while any abnormal accumulation in other organs was not reported., Conclusions: This study demonstrates that [89Zr]Zr-DFO-girentuximab administered to Japanese patients with suspected RCC has a favorable safety profile and is well tolerated and has a similar dosimetry profile to previously studied populations., (© The Author(s) 2024. Published by Oxford University Press.)

Our objective was to investigate the long-term functional outcomes of robot-assisted partial nephrectomy (RAPN) combined with three-dimensional (3D) imaging. The 3D images, reconstructed using computed tomography, were introduced in RAPN procedures. The demographic, oncological, functional, and volumetric outcomes of 296 patients who underwent RAPN with and without 3D imaging between 2013 and 2021 were analyzed retrospectively. Propensity score matching (1:1) was performed to adjust for potential baseline confounders. After matching, 71 patients were allocated to each group. In the 3D RAPN (3DRPN) group, functional outcomes significantly improved: the number of patients with over 90% estimated glomerular filtration rate (eGFR) preservation rate (40 vs. 43, P = 0.044), eGFR preservation rate (88.0% vs. 91.6%, P = 0.006), the number of patients with chronic kidney disease (CKD) upstaging (26 vs. 13, P = 0.023), and split renal function preservation rate (operated kidney: 84.9% vs. 88.5%, P = 0.015). The 3DRPN group showed superiority in terms of >90% eGFR preservation (P = 0.010), CKD upstaging-free survival rates (P < 0.001), and volumetric outcomes (excess parenchymal volume: 27.9 vs. 17.7 mL, P = 0.030; parenchyma volume preservation rate: 81.6% vs. 88.8%, P = 0.006). Three-dimensional imaging was positively associated with eGFR preservation (P = 0.023, odds ratio: 2.34) and prevention of CKD upstaging (P = 0.013, odds ratio: 2.90). In this study, RAPN combined with 3D imaging underscored the preservation of eGFR > 90% and the prevention of CKD upstaging by improving the preservation rate of renal parenchyma and split renal function., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)