Table. Pathological Features and Molecular Profile of Early-Onset Colorectal Cancer Pathological features Molecular profile Poor differentiation Microsatellite stability Mucinous tumors More likely to exhibit LINE-1 hypomethylation and TP53 sequence variations Signet-ring morphology Less frequently harbor KRAS, BRAF V600E, and APC sequence variations Perineural/venous invasion Promoter methylation of CpG islands Abbreviations: APC, adenomatous polyposis coli; BRAF, B-Raf; KRAS, K-Ras; LINE-1, long interspersed nuclear elements; TP53, tumor protein 53.

Background and AimCancer is one of the foremost causes of morbidity and mortality worldwide. Globally, colorectal cancer (CRC) is the third most diagnosed and fourth most important cause of cancer death. A total of 70% of all CRC‐related deaths occur in low‐ and middle‐income countries. In Sub‐Saharan Africa (SSA), estimating the burden of CRC is difficult. Only 27 of 43 SSA countries have formalized cancer registration systems; data quality is variable and national coverage rare.MethodsThis is a multidisciplinary, longitudinal cohort study started in January 2016. Patients >18 years with histologically confirmed primary adenocarcinoma of the colon and rectum, diagnosed within the previous 12 months, are eligible. Participants were assessed and were followed up for 3 years. Baseline information, including demographics, socioeconomic status, family history, medical and surgical non‐cancer‐related history, dietary history, colonoscopic findings, staging at presentation, treatment, and disease recurrence, is collected, as well as blood tests and histology results. Outcomes include disease recurrence (local and metastatic) and survival.Results and ConclusionThis study aims to describe the clinical presentation, management, and outcomes of adults with CRC in a multiethnic, urban South African population. It will be the first prospective study to describe clinical presentation, demographics, risk factors, treatment, and outcomes according to population group, from both private and state health‐care facilities in Johannesburg, South Africa. The results of this study will be relevant not only to South Africa but also to other SSA countries undergoing similar rates of rapid urbanization and epidemiological transition.

No abstract

No abstract

Background: South Africa (SA) has the highest incidence of colorectal cancer (CRC) in Sub-Saharan Africa (SSA). However, there is limited research on CRC recurrence and survival in SA. CRC recurrence and overall survival are highly variable across studies. Accurate prediction of patients at risk can enhance clinical expectations and decisions within the South African CRC patients population. We explored the feasibility of integrating statistical and machine learning (ML) algorithms to achieve higher predictive performance and interpretability in findings.Methods: We selected and compared six algorithms:- logistic regression (LR), naïve Bayes (NB), C5.0, random forest (RF), support vector machine (SVM) and artificial neural network (ANN). Commonly selected features based on OneR and information gain, within 10-fold cross-validation, were used for model development. The validity and stability of the predictive models were further assessed using simulated datasets.Results: The six algorithms achieved high discriminative accuracies (AUC-ROC). ANN achieved the highest AUC-ROC for recurrence (87.0%) and survival (82.0%), and other models showed comparable performance with ANN. We observed no statistical difference in the performance of the models. Features including radiological stage and patient's age, histology, and race are risk factors of CRC recurrence and patient survival, respectively.Conclusions: Based on other studies and what is known in the field, we have affirmed important predictive factors for recurrence and survival using rigorous procedures. Outcomes of this study can be generalised to CRC patient population elsewhere in SA and other SSA countries with similar patient profiles.

Background: Definitive closure of fistula-in-ano poses an ongoing surgical challenge. The OVESCO OTSC ® Proctology Clip (proctology clip) purports to offer improved preservation of the anal sphincter whilst at the same time curing the fistula by closure. Methods: A retrospective record review was conducted for patients who received the proctology clip as part of the management of fistula-in-ano in the Colorectal Unit at Wits Donald Gordon Medical Centre (WDGMC). Results: There were 19 cases of fistula-in-ano treated with the proctology clip. All were cryptoglandular in origin. The median age was 50 years (IQR 44-56 years) and post-procedure, the median follow-up duration was 145 days (IQR 63-298 days). Overall, 9 procedures were successful (47%). Success rates were higher for simple (66.7%) as opposed to complex (38.5%) fistula-in-ano. For patients who underwent placement of the proctology clip as a primary procedure, the success rate (50%) was slightly better than those who received the clip as a secondary procedure (44.4%). Conclusion:This preliminary data presents our initial experience using the proctology clip. While these data may serve as a "proof of concept", a multi-centre controlled trial comparing this method to the rectal mucosal advancement flap (RMAF) is needed to determine the role of the proctology clip in the management of fistula-in-ano.

Background The multidisciplinary perioperative and anaesthetic management of patients undergoing pelvic exenteration is essential for good surgical outcomes. No clear guidelines have been established, and there is wide variation in clinical practice internationally. This consensus statement consolidates clinical experience and best practice collectively, and systematically addresses key domains in the perioperative and anaesthetic management. Methods The modified Delphi methodology was used to achieve consensus from the PelvEx Collaborative. The process included one round of online questionnaire involving controlled feedback and structured participant response, two rounds of editing, and one round of web-based voting. It was held from December 2019 to February 2020. Consensus was defined as more than 80 per cent agreement, whereas less than 80 per cent agreement indicated low consensus. Results The final consensus document contained 47 voted statements, across six key domains of perioperative and anaesthetic management in pelvic exenteration, comprising preoperative assessment and preparation, anaesthetic considerations, perioperative management, anticipating possible massive haemorrhage, stress response and postoperative critical care, and pain management. Consensus recommendations were developed, based on consensus agreement achieved on 34 statements. Conclusion The perioperative and anaesthetic management of patients undergoing pelvic exenteration is best accomplished by a dedicated multidisciplinary team with relevant domain expertise in the setting of a specialized tertiary unit. This consensus statement has addressed key domains within the framework of current perioperative and anaesthetic management among patients undergoing pelvic exenteration, with an international perspective, to guide clinical practice, and has outlined areas for future clinical research.

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.