The natural orifice specimen extraction surgery (NOSES) procedure is an innovative approach to treating colorectal cancer that offers several advantages. First, the NOSES technique is minimally invasive, reducing the incidence of postoperative complications such as wound infection and fat liquefaction. Second, it provides excellent pain relief and improves cosmetic outcomes by requiring fewer abdominal incisions, thereby increasing patient satisfaction. Additionally, NOSES provides all of the advantages of minimally invasive surgery and enables patients to recover quickly, helping patients return to their normal lives faster. This approach aligns with the concept of patient-centered care and significantly improves patient quality of life. The positive results noted at both short- and long-term postoperative follow-up indicate that NOSES is a valuable option in cancer care. Despite its many benefits, NOSES has also been challenged by bacteriology, oncology, and ethics. This paper presents a review of recent research progress regarding NOSES in the treatment of colorectal cancer. In this article, we examined the advantages and disadvantages of NOSES in clinical practice and discussed potential issues and solutions.

Background: Although the surgical treatment strategy for rectal cancer (RC) is usually based on the preoperative diagnosis of lymph node metastasis (LNM), the accurate diagnosis of LNM has been a clinical challenge. In this study, we developed machine learning (ML) models to predict the LNM status before surgery based on a privacy-preserving computing platform (PPCP) and created a web tool to help clinicians with treatment-based decision-making in RC patients. Patients and methods: A total of 6578 RC patients were enrolled in this study. ML models, including logistic regression, support vector machine, extreme gradient boosting (XGB), and random forest, were used to establish the prediction models. The areas under the receiver operating characteristic curves (AUCs) were calculated to compare the accuracy of the ML models with the US guidelines and clinical diagnosis of LNM. Last, model establishment and validation were performed in the PPCP without the exchange of raw data among different institutions. Results: LNM was detected in 1006 (35.3%), 252 (35.3%), 581 (32.9%), and 342 (27.4%) RC patients in the training, test, and external validation sets 1 and 2, respectively. The XGB model identified the optimal model with an AUC of 0.84 [95% confidence interval (CI), 0.83–0.86] compared with the logistic regression model (AUC, 0.76; 95% CI, 0.74–0.78), random forest model (AUC, 0.82; 95% CI, 0.81–0.84), and support vector machine model (AUC, 0.79; 95% CI, 0.78–0.81). Furthermore, the XGB model showed higher accuracy than the predictive factors of the US guidelines and clinical diagnosis. The predictive XGB model was embedded in a web tool (named LN-MASTER) to predict the LNM status for RC. Conclusion: The proposed easy-to-use model showed good performance for LNM prediction, and the web tool can help clinicians make treatment-based decisions for patients with RC. Furthermore, PPCP enables state-of-the-art model development despite the limited local data availability.