Ventral mesh rectopexy (VMR) is an established treatment for rectal prolapse with favourable functional outcomes. However, long-term real-world data beyond 5 years on recurrence and mesh-related complications remain limited. This study evaluates long-term recurrence, short-term morbidity, mesh-related complications, and outcomes after laparoscopic and robotic VMR. A retrospective single-centre cohort study was conducted including all consecutive patients who underwent VMR for rectal prolapse at a tertiary centre between 2012 and 2023, with a median follow-up of 69 months (range 12–143). Demographic, clinical, operative, and follow-up data were analysed. Recurrence, defined as clinical or radiological evidence of prolapse, and postoperative complications classified using the Clavien–Dindo system, including mesh-related events, were assessed, with outcomes stratified by mesh type and surgical approach. Among 208 patients (median age 64 years; 92
Predicting clinical complete response (cCR) to total neoadjuvant therapy (TNT) helps optimize therapeutic strategies and identify the best candidates for a watch-and-wait strategy. This study aimed to develop a machine learning model to predict cCR to TNT in a mixed curative-intent cohort of patients with advanced rectal cancer, including selected oligometastatic cases treated with curative intent. A retrospective analysis of a prospectively maintained database of consecutive rectal cancer patients treated with TNT with curative intent at three South Australian hospitals between 2019 and 2026 was performed. Least absolute shrinkage and selection operator (LASSO) analysis was used for variable selection, and ten machine learning algorithms were employed to develop prediction models for cCR. Model performance was evaluated using the area under the receiver-operating characteristic curve (AUROC). A total of 245 patients were identified, of whom 193 were assigned to the training set and 52 to the external validation set. LASSO identified albumin, clinical T stage, tumour length, systemic immune-inflammation index, extramural vascular invasion, platelet count, and body mass index as key predictors of cCR. Among the ten models, the logistic regression model performed best in the training set, achieving an AUROC of 0.817. In external validation, the multilayer perceptron (MLP) model achieved the highest predictive performance with an AUROC of 0.755. Machine learning models based on seven pretreatment variables show promise in accurately predicting cCR following TNT in patients with advanced rectal cancer. On external validation, the MLP model demonstrated the strongest predictive performance with moderate discrimination. However, further validation in larger, non-metastatic/selected populations may be required before clinical implementation.
Anastomotic leak remains one of the most severe complications following colorectal surgery. While multiple preoperative and intraoperative risk factors have been described, early postoperative clinical signs that may herald anastomotic failure have been scarcely investigated. The clinical significance of early postoperative rectal bleeding remains poorly defined. A retrospective observational study was conducted using a prospectively maintained database including adult patients who underwent colorectal surgery with primary anastomosis between January 2010 and May 2025. Early postoperative rectal bleeding was defined as visible blood loss per rectum within the first 48 postoperative h. Patients were divided into two groups according to the presence or absence of rectal bleeding. Demographic, operative, and postoperative variables were analyzed, and multivariate logistic regression was performed to identify independent predictors of anastomotic leak. A total of 1318 patients were included; 41 (3.1
