Long-term histological and hemodynamic findings of repairing penile tunica albuginea defects with collagen fleece in dogs.

Clinically, collagen fleece patching of the penile tunica albuginea (TA) has been successful. However, the histopathological and hemodynamic outcomes are not known. We studied in vivo TachoSil® patching in two beagle dogs weighing 16.8 (16.7-16.9) Kg. Bilateral intracavernous pressures (ICP) response to 10 mg papaverine hydrochloride were measured. A full-thickness defect was created on the left side in TA 1 × 0.5 cm, and four transverse incisions 1 cm long were made on the right side, placed 0.5 cm apart, and covered with TachoSil®. Six months later, ICP measurements were repeated, and the penis was excised for histopathology. Grossly, the graft site was indistinguishable. The mean baseline ICP was 19.3 ± 2.98 mmHg and increased after papaverine injection to a mean peak ICP of 122 ± 26.1 mmHg. The ICP measurement before and after grafting did not show a significant difference in the baseline (p = 0.068) or the peak pressure (p = 0.465). Histologically, minimal foreign body reaction was seen, and the TA was completely regenerated. The underlying cavernous tissue did not show inflammation or necrosis. The study is the first to show the long-term histopathologic regeneration of TA after collagen fleece patching while maintaining the hemodynamic response to papaverine.

Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients.

Objectives: Gastrointestinal stromal tumors (GISTs) can occur synchronously with other neoplasms, including the genitourinary (GU) system. Machine learning (ML) may be a valuable tool in predicting synchronous GU tumors in GIST patients, and thus improving prognosis. This study aims to evaluate the use of ML algorithms to predict synchronous GU tumors among GIST patients in a specialist research center in Saudi Arabia. Materials and Methods: We analyzed data from all patients with histopathologically confirmed GIST at our facility from 2003 to 2020. Patient files were reviewed for the presence of renal cell carcinoma, adrenal tumors, or other GU cancers. Three supervised ML algorithms were used: logistic regression, XGBoost Regressor, and random forests (RFs). A set of variables, including independent attributes, was entered into the models. Results: A total of 170 patients were included in the study, with 58.8% (n = 100) being male. The median age was 57 (range: 9-91) years. The majority of GISTs were gastric (60%, n = 102) with a spindle cell histology. The most common stage at diagnosis was T2 (27.6%, n = 47) and N0 (20%, n = 34). Six patients (3.5%) had synchronous GU tumors. The RF model achieved the highest accuracy with 97.1%. Conclusion: Our study suggests that the RF model is an effective tool for predicting synchronous GU tumors in GIST patients. Larger multicenter studies, utilizing more powerful algorithms such as deep learning and other artificial intelligence subsets, are necessary to further refine and improve these predictions.