Citations

Abstract Description Many cancers, including glioblastoma (GBM), exhibit a male-biased incidence and outcome disparity, partially driven by sex hormones. While androgens are known to suppress anti-tumor T cell immunity in certain solid tumors, we found that androgens instead enhance anti-tumor immunity in brain tumors. Upon castration, tumor growth was accelerated in brain tumor models, while the opposite effect was observed for tumors located outside the brain. Decreased T cell function was observed in both tumors and peripheral lymphoid organs in castrated mice, indicated by reduced anti-tumor cytokines and increased T cell exhaustion. Mechanistically, this systemic immunosuppression was driven by hyperactivation of the hypothalamus-pituitary-adrenal (HPA) axis in castrated mice with brain tumors. Blocking glucocorticoid receptors reversed the accelerated tumor growth in these mice, confirming the role of HPA axis dysregulation. This mechanism was brain-specific rather than GBM-specific, as hyperactivation of the HPA axis was also observed with intracranial implantation of non-GBM tumors. Furthermore, elevated production of proinflammatory cytokines (IL-1β, TNF) in the brain of castrated mice was identified as a driver of HPA axis activation. Taken together, these findings reveal unique endocrine-mediated mechanisms by which brain tumor modulate systemic immunity in the androgen-deprived setting and highlight the importance of organ-specific effects of anti-tumor immunity. Funding Sources NIH P01CA245705; NIH R35NS127083; American Brain Tumor Association; Cleveland Clinic; Case comprehensive cancer center Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Abstract Introduction Penile augmentation or penis enlargement surgery refers to any technique aimed at enhancing penile length or girth. These male enhancement surgical techniques can increase total penile length, either flaccid or erect, while others add to the penile shaft’s girth and/or glans size. Proposed strategies include physical methods such as various penile exercises, mechanical traction devices, and medical therapy, while surgical interventions include cosmetic and phallic reconstructive techniques and use of fillers, sleeves, grafts, and flaps to augment penile size. Objectives The International Consultation on Sexual Medicine (ICSM) consensus statements provide evidence-based recommendations and aim to guide a clinical framework in penile aesthetic and augmentation. Methods A comprehensive literature search was conducted to identify published literature relevant to penile augmentation or enhancement. Preliminary consensus was presented to the delegates attending the 5th ICSM meeting, and feedback was incorporated into the final document. Results Twenty new recommendations are provided with corresponding levels of evidence and grades of recommendation. Conclusions Due to the controversies and lack of longer-term data regarding penile augmentation and cosmetic procedures, comprehensive assessments and careful patient counseling weighing the pros and cons, as well as potential complications, are mandatory before embarking on any treatment. Surgeons should be experienced in anatomy and various surgical techniques, and it is important to individualize surgical options based on patient condition(s) and needs, surgeon expertise, and local resources.

Abstract Introduction There is no ideal treatment for Peyronie’s Disease (PD) since every patient has different needs with varying perceived risks versus benefits of individual treatment options, coupled with the degree of impact relating to PD on sexual function and beyond. Objectives The 5th International Consultation on Sexual Medicine (ICSM) guideline on PD provides an update to the previous ICSM with an aim to provide a clinical framework and best-evidenced consensus recommendations to assist clinicians in managing PD. Methodology Available literature was reviewed, and a modified Delphi consensus was undertaken by leading sexual medicine experts to achieve a set of Quality of Evidence and strength of recommendations. This guideline was developed as an authoritative best-evidenced consensus incorporating state-of-the-art knowledge and contemporary literature. Results A total of thirty-one recommendations were made on PD guidelines. New updates to the 4th ICSM on PD have been made in definition, pathophysiology, and treatment strategies. The presence of penile pain with erection could occur in the chronic phase due to torque or stretch on the penile scar and surrounding tissue. Oral therapy works best during the acute phase and should be provided concurrently with other treatment modalities. Penile traction therapy can reduce penile deformity as monotherapy or may be offered as part of a multimodal therapy approach. There is no convincing evidence supporting cellular-based therapy. The type of plication procedure used depends on the surgeon and patient preference, although excision of the tunica followed by plication may have a higher complication rate. There is currently no ideal graft, and graft contracture can occur postoperatively. Adjunctive procedures such as penile modeling, tunical plication, plaque incision/excision and grafting can be performed when penile deformity and/or penile curvature persist following penile prosthesis implantation. Conclusions The management of PD should be individualized based on the patient’s needs and preferences while balancing the benefits and risks of each treatment option.

Abstract INTRODUCTION Glioblastoma is the most common malignant adult brain tumor with poor prognosis, largely due to its heterogeneous landscape. The AI-RANO and RANO-RGP groups organized the BraTS-Pathology 2024 challenge, providing a publicly-available dataset and a benchmarking environment for AI models to automatically identify six clinically-relevant histopathologic sub-regions in H&E-stained whole slide images (WSIs). METHODS We reclassified the TCGA-GBM and TCGA-LGG data according to the WHO 2021 criteria and identified diagnostic WSIs of 188 glioblastoma (IDH-wt, CNS WHO Gr.4) patients. A well-defined annotation protocol was communicated to eight neuropathologists, across eight institutions, who created “ground truth” annotations for Cellular Tumor (CT), Geographic Necrosis, Cortical Infiltration, Pseudopalisading Necrosis (PN), Microvascular Proliferation, and Penetration into White Matter (PWM). Comprehensive WSI curation for elimination of artifactual content and follow up partitioning resulted in approximately 300,000 data samples/patches. FINDINGS Inter-observer variability assessment highlighted difficulties in establishing a “gold standard”. The least annotated sub-regions (PWM and PN) were the most challenging to the participating methods, whereas all methods achieved their highest accuracy in the most annotated sub-region (CT). The highest average performance across sub-regions was obtained by a foundation model-based method. However, this top-ranked solution achieved an F1 score of 0.51 (Accuracy=0.65, Sensitivity=0.56, Specificity=0.93). CONCLUSIONS Our findings suggest that achieving state-of-the-art performance, in automatically identifying clinically-relevant glioblastoma sub-regions, remains an unsolved research problem. Analysis of the ground-truth annotations underscores the critical need for more precisely defined and neuropathologically standardized annotation protocol. Current AI algorithms are facing difficulties in addressing glioblastoma’s heterogeneous histologic landscape in the absence of large, comprehensively, and consistently annotated data. Building upon these findings, and towards developing more accurate AI algorithms, the 2025 iteration of the BraTS-Path challenge is currently on-going by leveraging two million data samples, aiming to enhance our disease understanding and improve diagnostic accuracy, ultimately contributing to improved patient outcomes.

Abstract Computer vision now enables deep learning models to link histopathologic features to molecular features in a quantitative manner not previously achievable by human pathologists. However, human interpretability is often limited. We trained a deep neural network to predict gene expression from digital whole slide images (WSIs) using 848 central nervous system (CNS) tumors with paired WSI and RNA-sequencing data. We used inferred RNA expression levels as a surrogate for protein expression of the CNS tumor lineage markers GFAP, OLIG2, and SSTR2, as well as the proliferation marker MKI67 (Ki67). We established thresholds for categorizing the inferred expression levels as positive or negative based on levels observed in cross-validation testing in select tumor types to approximate routine clinical interpretation of immunohistochemical (IHC) staining. We tested the sensitivity and specificity of our ‘virtual’ IHC on an independent, multi-institutional cohort of over 2,000 CNS tumors with objective diagnostic labels derived from DNA methylation-based tumor classification. As a dichotomous variable, inferred GFAP expression showed a sensitivity of 74% and a specificity of 99% as a glial marker in a cohort of gliomas and meningiomas. In the same cohort, OLIG2 showed a sensitivity of 75% and a specificity of 97% as a glial marker, while SSTR2 showed a sensitivity of 83% and a specificity of 97% as a marker of meningioma. In a cohort of ependymomas and gliomas, virtual IHC for OLIG2 showed a sensitivity of 75% and a specificity of 82% in distinguishing ependymomas from gliomas. Finally, in a cohort of gliomas, inferred expression of MKI67 demonstrated a trend consistent with what would be expected by actual IHC, with increasing MKI67 expression with increasing tumor grade. Our model provides the basis for a human interpretable and clinically applicable deep neural network to aid human pathologists in the diagnosis and grading of CNS tumors.

Abstract Molecular testing has become critical components in the diagnostic classification of the central nervous system (CNS) tumors. However, these methods require substantial resources, limiting accessibility for many patients. Recent advances in artificial intelligence (AI) and computer vision empower deep learning models to infer molecular features from histopathology images, which may often be more readily available than molecular testing, to classify CNS tumors. We trained pan-CNS tumor models to predict DNA methylation and gene expression from whole-slide images (WSIs). These molecular predictions were then used in a hierarchical machine learning framework, termed Neuropath-AI, to predict nine broad tumor families and 52 tumor types using a large diverse cohort of 5,715 histopathology samples, of which 2,988 had paired DNA methylation profiling and 848 had paired RNA-sequencing. We evaluated Neuropath-AI on another large independent multi-institutional cohort of 5,516 CNS tumors, for which Neuropath-AI predicted tumor class with an associated confidence score. Neuropath-AI designated 46% of test samples as predictable with high-confidence, which it then accuracy classified in 97% of samples. It made moderate-confidence (and above) predictions for 87% of test samples, for which it achieved a diagnostic accuracy of 80% for the top-1 prediction and 86% accuracy for a top-2 predictions. A comparison with human neuropathologists showed that Neuropath-AI is comparable at classifying tumors. A second test showed that human neuropathologists were more accurate when the Neuropath-AI classifier results were provided to them, testifying to the potential translational benefit of integrating AI classification tools into the pathologist’s workflow. Our model provides the basis for a clinically applicable deep learning assistant to improve human efficiency and accuracy of CNS tumor diagnosis. The model will be made publicly available and can be readily implemented to assist human pathologists in clinical workflows, in further expanded prospective studies.

Background The Woven EndoBridge (WEB) device represents an innovative solution for cerebral aneurysm occlusion, particularly for challenging wide-neck bifurcation aneurysms. However, factors affecting sustained occlusion remain poorly understood. We utilized machine learning to attempt to identify predictors of favorable long-term outcomes following WEB treatment. Methods In this multicenter retrospective study, we collected patient demographics, aneurysm characteristics, procedural details, and clinical outcomes. The primary endpoint was improvement in occlusion status, defined as maintained Raymond-Roy Occlusion Classification (RROC) grade 1, or improvement from grade 2 to 1, or from grade 3 to either 2 or 1 on final angiographic follow up. The dataset was split into training (75%) and validation (25%) sets. The CatBoost algorithm was selected based on performance metrics, with Shapley Additive exPlanations (SHAP) values calculated to determine feature importance. Furthermore, a multivariable binomial logistic regression model was performed to validate machine learning findings. Results Among 720 aneurysms from 36 hospitals, 84% showed improvement in occlusion at follow up. Both machine learning and multivariable logistic regression identified aneurysm height as the most consistent correlate of nonimprovement (odds ratio (OR) 0.90 per mm, p = 0.022). In the CatBoost model, the highest-ranking features by SHAP included aneurysm height, patient age, treatment acuity, ACom location, WEB-SLS device, bifurcation anatomy, aneurysm multiplicity, baseline modified Rankin Scale, access route, and partial thrombosis. Conclusions Machine-learning and regression analyses identified consistent predictors of occlusion improvement after WEB treatment, with aneurysm height most strongly linked to nonimprovement. These insights may guide patient selection and follow up. Findings require cautious interpretation and external validation in larger cohorts.