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Objective: This case report aims to present a rare occurrence of fallopian tube prolapse into the vaginal vault following hysterectomy, underscoring the importance of recognizing this uncommon complication. Case Report: A 45-year-old woman, with a history of hysterectomy for adenomyosis, presented with symptoms mimicking a vaginal tumor, including persistent discharge, abdominal pain, postcoital bleeding, and weight loss. Imaging raised concerns of malignancy, but surgery revealed a prolapsed fallopian tube with chronic inflammation. This case highlights the diagnostic challenges, with less than 100 reported instances. Differential diagnoses encompassed diverse pelvic pathologies, necessitating histological confirmation. Factors contributing to prolapse include poor vault closure and early sexual activity. Awareness of this complication is pivotal when evaluating post-hysterectomy symptoms, ensuring precise diagnosis and appropriate management. Conclusion: Fallopian tube prolapse into the vaginal vault is a rare post-hysterectomy complication. Early recognition and accurate diagnosis are essential for effective management of patients presenting with post-hysterectomy symptoms, such as vaginal bleeding and discharge.

Background & Aims: Hepatocellular carcinoma (HCC) is characterized by a high mortality rate. The Liver Imaging Reporting and Data System (LI-RADS) results in a considerable number of indeterminate observations, rendering an accurate diagnosis difficult. Methods: We developed four deep learning models for diagnosing HCC on computed tomography (CT) via a training–validation–testing approach. Thin-slice triphasic CT liver images and relevant clinical information were collected and processed for deep learning. HCC was diagnosed and verified via a 12-month clinical composite reference standard. CT observations among at-risk patients were annotated using LI-RADS. Diagnostic performance was assessed by internal validation and independent external testing. We conducted sensitivity analyses of different subgroups, deep learning explainability evaluation, and misclassification analysis. Results: From 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under receiver-operating-characteristic curves (AUCs) of 0.919 (95% CI, 0.903–0.935) and 0.901 (95% CI, 0.879–0.924) at the observation (n = 1,077) and patient (n = 685) levels, respectively during internal validation, compared with 0.839 (95% CI, 0.814–0.864) and 0.822 (95% CI, 0.790–0.853), respectively for standard of care radiological interpretation. The negative predictive values of ST3DCN were 0.966 (95% CI, 0.954–0.979) and 0.951 (95% CI, 0.931–0.971), respectively. The observation-level AUCs among at-risk patients, 2–5-cm observations, and singular portovenous phase analysis of ST3DCN were 0.899 (95% CI, 0.874–0.924), 0.872 (95% CI, 0.838–0.909) and 0.912 (95% CI, 0.895–0.929), respectively. In external testing (551/717 patients/observations), the AUC of ST3DCN was 0.901 (95% CI, 0.877–0.924), which was non-inferior to radiological interpretation (AUC 0.900; 95% CI, 0.877–-923). Conclusions: ST3DCN achieved strong, robust performance for accurate HCC diagnosis on CT. Thus, deep learning can expedite and improve the process of diagnosing HCC. Impact and implications:: The clinical applicability of deep learning in HCC diagnosis is potentially huge, especially considering the expected increase in the incidence and mortality of HCC worldwide. Early diagnosis through deep learning can lead to earlier definitive management, particularly for at-risk patients. The model can be broadly deployed for patients undergoing a triphasic contrast CT scan of the liver to reduce the currently high mortality rate of HCC.