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Purpose: Renal cell carcinoma (RCC) is the most common type of kidney cancer worldwide. Although radiologists assess enhancement patterns of renal tumors to predict tumor pathology report, to our knowledge, no formal scoring system has been created and validated to assess the level of neovascularity in RCC, despite its critical role in cancer metastases. In this study, we characterized and analyzed the level of angiogenesis in tumor-burdened kidneys and their benign counterparts. We then created and validated a scoring scale for neovascularity that can help predict tumor staging for RCC. Methods: After Institutional Review Board approval, the charts of patients who had undergone operation for RCC between January 13, 2014 and February 4, 2020 were retrospectively reviewed for inclusion in this study. Inclusion criteria were a diagnosis of RCC, simple/radical nephrectomy, preoperative contrast-enhanced CT scans, and complete pathology reports. Neovascularity was scored on a scale of 0–4 where 0 = no neovascularity detected, 1 = a single vessel <3 mm wide, 2 = a single vessel ≥3 mm wide, 3 = multiple vessels <3 mm wide, and 4 = multiple vessels ≥3 mm wide. Results: A total of 227 patients were included in this study. Most of the tumor pathology reports were clear cell carcinoma, regardless of tumor staging. The average neovascularity score was 1.07 for pT1x tumors, 2.83 for pT2x tumors, and 3.04 for pT3x tumors. There was a significant difference in neovascularity score between pT1x and pT2x tumors (p = 0.0046), pT1x and pT3x tumors (p < 0.0001), and benign kidneys and kidneys with RCC (p < 0.0001). Conclusion: Our novel vascular scoring system for RCC demonstrates significant correlation with RCC pathological tumor staging. This scoring system may be utilized as part of a comprehensive radiological assessment of renal tumors, potentially improving tumor characterization and clinical decision making. [ABSTRACT FROM AUTHOR]

Vesicular monoamine transporters (VMATs) mediate the transport of dopamine (DA), serotonin (5HT), and other monoamines into secretory vesicles. The regulation of mammalian VMAT and the related vesicular acetylcholine transporter (VAChT) has been proposed to involve membrane trafficking, but the mechanisms remain unclear. To facilitate a genetic analysis of vesicular transporter function and regulation, we have cloned the Drosophila homolog of the vesicular monoamine transporter ( dVMAT). We identify two mRNA splice variants (DVMAT-A and B) that differ at their C-terminus, the domain responsible for endocytosis of mammalian VMAT and VAChT. DVMAT-A contains trafficking motifs conserved in mammals but not C. elegans, and internalization assays indicate that the DVMAT-A C-terminus is involved in endocytosis. DVMAT-B contains a divergent C-terminal domain and is less efficiently internalized from the cell surface. Using in vitro transport assays, we show that DVMAT-A recognizes DA, 5HT, octopamine, tyramine, and histamine as substrates, and similar to mammalian VMAT homologs, is inhibited by the drug reserpine and the environmental toxins 2,2,4,5,6-pentachlorobiphenyl and heptachlor. We have developed a specific antiserum to DVMAT-A, and find that it localizes to dopaminergic and serotonergic neurons as well as octopaminergic, type II terminals at the neuromuscular junction. Surprisingly, DVMAT-A is co-expressed at type II terminals with the Drosophila vesicular glutamate transporter. Our data suggest that DVMAT-A functions as a vesicular transporter for DA, 5HT, and octopamine in vivo, and will provide a powerful invertebrate model for the study of transporter trafficking and regulation. © 2005 Wiley Periodicals, Inc. J Neurobiol, 2005 [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The expansion of large language models to process images offers new avenues for application in radiology. This study aims to assess the multimodal capabilities of contemporary large language models, which allow analysis of image inputs in addition to textual data, on radiology board-style examination questions with images., Materials and Methods: 280 questions were retrospectively selected from the AuntMinnie public test bank. The test questions were converted into three formats of prompts; (1) Multimodal, (2) Image-only, and (3) Text-only input. Three models, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet, were evaluated using these prompts. The Cochran Q test and pairwise McNemar test were used to compare performances between prompt formats and models., Results: No difference was found for the performance in terms of % correct answers between the text, image, and multimodal prompt formats for GPT-4V (54%, 52%, and 57%, respectively; p = .31) and Gemini 1.5 Pro (53%, 54%, and 57%, respectively; p = .53). For Claude 3.5 Sonnet, the image input (48%) significantly underperformed compared to the text input (63%, p < .001) and the multimodal input (66%, p < .001), but no difference was found between the text and multimodal inputs (p = .29). Claude significantly outperformed GPT and Gemini in the text and multimodal formats (p < .01)., Conclusion: Vision-capable large language models cannot effectively use images to increase performance on radiology board-style examination questions. When using textual data alone, Claude 3.5 Sonnet outperforms GPT-4V and Gemini 1.5 Pro, highlighting the advancements in the field and its potential for use in further research., 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.)

In this case report, we describe a diagnosis of papillary renal cell carcinoma in a 76-year-old male patient who was incidentally found to have a left adrenal mass during routine aneurysm surveillance. Computed tomography demonstrated a left adrenal mass and left renal structure which was concerning for renal cell carcinoma. He underwent left adrenalectomy and initial histopathology demonstrated papillary renal cell carcinoma. He subsequently underwent left radical nephrectomy with lymph node dissection. Histopathological analysis of the removed left renal and nodal specimens revealed papillary renal cell carcinoma with lymph node metastasis. However, re-review of the adrenal pathology slides determined the specimen as represented by primary kidney tumor and not adrenal metastasis. This report reviews the presentation and radiological findings of synchronous papillary renal cell carcinoma and differential diagnosis for indeterminate adrenal mass on computed tomography., (© 2024 The Authors. Published by Elsevier Inc. on behalf of University of Washington.)

Objective: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI., Materials and Methods: This retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation., Results: The CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974., Conclusion: A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.