VRAM flap reconstruction of the perineum is associated with an increased wound healing rate and may protect against pelvic abscess development. However, procedure-related long-term morbidity is incompletely studied and the procedure should be reserved for selected patients.

Deep SSI is a frequent and major complication after rectal surgery for LARC, with high morbidity, increased hospital stay and protracted wound healing. Interestingly, deep SSI did not influence long-term oncologic outcome.

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Background and purposeWe investigated how features relating to pelvic cavity anatomy and tumor hemodynamic factors may influence systemic failure in rectal cancer.Materials and methodsRectal cancer patients (207 women, 343 men), who had been prospectively enrolled onto six cohorts and given curative-intent therapy, were analyzed for the first metastatic event. In one of the cohorts, the diameter of the inferior mesenteric vein (IMV) was assessed on diagnostic abdominal computed tomography images (n = 113). Tumor volume (n = 193) and histologic response to neoadjuvant therapy (n = 445) were recorded from diagnostic magnetic resonance images and surgical specimens, respectively.ResultsMore women than men developed lung metastasis (p = 0.037), while the opposite was the case for liver metastasis (p = 0.040). Wider IMV diameter correlated with larger tumor volume (r = 0.481, p < 0.001) and male sex (p < 0.001). Female sex was the only adverse prognostic factor for lung metastasis. When sex, tumor volume, and histologic response were taken into consideration, poor tumor response remained the only determinant for liver metastasis (p = 0.002).ConclusionsIn a diverse rectal cancer population given curative-intent treatment, women and men had different outcome with regard to the primary metastatic site. Tumor hemodynamic factors should be considered in rectal cancer risk stratification.

Aim We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.