Retrorectal tumors are rare tumors that require resection for symptoms, malignancy and potential malignant transformation. Traditional approaches have included laparotomy, perineal excision or a combination. Multiple minimally invasive techniques are available which have the potential to minimize morbidity and enhance recovery. We performed a systematic review of the literature to determine the feasibility and surgical outcomes of retrorectal tumors approached using minimally invasive surgical techniques. Publications in which adult patients (≥ 18 years) had a minimally invasive approach (laparoscopic or robotic) for resection of a primary retrorectal tumor were included. Data were collected on approach, preoperative investigation, size and sacral level of the tumor, operating time, length of stay, perioperative complications, margins and recurrence. Thirty-five articles which included a total of 82 patients met the inclusion criteria. The majority of patients were female (n = 65; 79.2%), with a mean age of 41.7 years (range 18-89 years). Seventy-three patients (89.0%) underwent laparoscopic or combined laparoscopic-perineal resection, and 9 (10.8%) had a robotic approach. The conversion rate was 5.5%. The overall 30-day morbidity rate was 15.7%, including 1 intraoperative rectal injury (1.2%). Ninety-five percent (n = 78) of the retrorectal tumors were benign. Median length of stay was 4 days for both laparoscopic and robotic groups, with ranges of 1-8 and 2-10 days, respectively. No tumor recurrence was noted during follow-up [median 28 months (range 5-71 months)]. A minimally invasive approach for the resection of retrorectal tumors is feasible in selected patients. Careful patient selection is necessary to avoid incomplete resection and higher morbidity than traditional approaches.

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Surgery remains the cornerstone of rectal cancer treatment. However, there is significant morbidity and mortality associated with pelvic surgery, and the past decade has illustrated that a cohort of rectal cancer patients sustain a remission of local disease with chemoradiation alone. Thus, questions remain regarding the optimal management for rectal cancer; namely, accurately identifying patients who have a complete pathologic response and determining the oncologic safety of the observational approach for this patient group. This review aims to summarize the current evidence to provide an overview to the 'watch and wait' approach in rectal cancer patients with a complete response to neoadjuvant chemoradiation therapy.

Both ypT and ypN status is of prognostic significance following neoadjuvant therapy for rectal cancer.

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.