RESUMEN
Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.
RESUMEN
Feminizing adrenocortical tumors (FATs) are exceptionally rare primary adrenal neoplasms that cause high estrogen and low testosterone levels. They are most common in adult males, typically presenting with gynecomastia, hypogonadism, and weight loss. They are almost always malignant, with a poor prognosis and a high recurrence rate. We report a case of a 35-year-old man with an adrenal FAT with high estrogen (181 pg/mL) and low testosterone (37 ng/dL) who presented with gynecomastia, erectile dysfunction, subclinical Cushing syndrome, and pain localizing to different regions of the torso. There was no evidence of metastatic disease initially as seen by visualization of a well-marginated mass on computed tomography scan. Surgical resection of the FAT was performed, and the mass was confirmed to be a low-grade tumor. Clinical symptoms were resolved after surgery. Despite complete resection with negative margins, the patient subsequently had two separate local metastatic recurrences within a few years, treated with a combination of further surgery and medical intervention. This case highlights the unique features of an exceedingly rare adrenal tumor and stresses the importance of early detection and vigilant surveillance following resection due to high recurrence rates.
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Multifocal ganglioneuromas are characterized by the presence of multiple benign neuroepithelial tumor nodules and are less common than solitary tumors. A small percentage of ganglioneuromas present with a fatty appearance. Only a few cases of multifocal ganglioneuromas have been reported, due to both their rarity and minimal symptomatic presentation; therefore, generalizations about risk factors and predictive markers are very difficult. Here, we report a case of multifocal retroperitoneal ganglioneuroma with an infiltrative appearance on computed tomography (CT). The tumor demonstrated slow growth on multiple imaging studies and was associated with abdominal and flank pain. The aggressive appearance eventually led to surgical resection 18 months after the initial incidental finding on CT. Postsurgical analysis of the tumor on imaging was crucial in revealing its nodularity and infiltration, as well as for clarifying its retroperitoneal location inseparable from the adrenal gland. Histology demonstrated Schwann cells and ganglion cells without atypia or increased cellularity, and with no mitosis or necrosis seen. Our case highlights the consideration of ganglioneuroma with fatty infiltration in the differential diagnosis of a fatty tumor in the mediastinum or retroperitoneum. Additionally, our report differentiates multifocal ganglioneuroma with fatty infiltration from lipomatous ganglioneuroma on radiology and histopathology.
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BACKGROUND: Challenges remain in determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy in renal cell carcinoma. The objective of this review is to provide a comprehensive overview of biomarkers in metastatic renal cell carcinoma (mRCC) and their utility in prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease. METHODS: A systematic literature search was conducted using the PubMed database for relevant studies published between January 2017 and December 2022. The search focused on biomarkers associated with mRCC and their relationship to immune checkpoint inhibitors, targeted therapy, and VEGF inhibitors in the adjuvant, neoadjuvant, and metastatic settings. RESULTS: The review identified various biomarkers with predictive, prognostic, and therapeutic monitoring potential in mRCC. The review also discussed the challenges associated with anti-angiogenic and immune-checkpoint monotherapy trials and highlighted the need for personalized therapy based on molecular signatures. CONCLUSION: This comprehensive review provides valuable insights into the landscape of biomarkers in mRCC and their potential applications in prediction of treatment response, prognosis, and therapeutic monitoring. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in mRCC.
RESUMEN
Introduction: Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method: The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results: The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9. Discussion: Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.
