The effect of robotic surgery on low anterior resection syndrome in patients with lower rectal cancer: a propensity score-matched analysis.

BACKGROUND: Many patients experience anorectal dysfunction after rectal surgery, which is known as low anterior resection syndrome (LARS). Robotic systems have many technical advantages that may be suitable for functional preservation after low rectal resection. Thus, the study aimed to explore whether robotic surgery can reduce the incidence and severity of LARS. METHODS: Patients undergoing minimally invasive sphincter-sparing surgery for low rectal cancer were enrolled between January 2015 and December 2020. The patients were divided into robotic or laparoscopic groups. The LARS survey was conducted at 6, 12 and 18 months postoperatively. Major LARS scores were analysed as the primary endpoint. In order to reduce confounding factors, one-to-two propensity score matches were used. RESULTS: In total, 342 patients were enrolled in the study. At 18 months postoperatively, the incidence of LARS was 68.7% (235/342); minor LARS was identified in 112/342 patients (32.7%), and major LARS in 123/342 (36.0%). After matching, the robotic group included 74 patients, and the laparoscopic group included 148 patients. The incidence of major LARS in the robotic group was significantly lower than that in the laparoscopic group at 6, 12, and 18 months after surgery. In multivariate logistic regression analysis, tumour location, laparoscopic surgery, intersphincteric resection, neoadjuvant therapy, and anastomotic leakage were independent risk factors for major LARS after minimally invasive sphincter-sparing surgery for low rectal cancer. Furthermore, a major LARS prediction model was constructed. Results of model evaluation showed that the nomogram had good prediction accuracy and efficiency. CONCLUSIONS: Patients with low rectal cancer may benefit from robotic surgery to reduce the incidence and severity of LARS. Our nomogram could aid surgeons in setting an individualized treatment program for low rectal cancer patients.

Establish a novel tumor budding-related signature to predict prognosis and guide clinical therapy in colorectal cancer.

Tumor budding is a long-established independent adverse prognostic marker for colorectal cancer (CRC), yet assessment of tumor budding was not reproducible. Therefore, development of precise diagnostic approaches to tumor budding is in demand. In this study, we first performed bioinformatic analysis in our single-center CRC patients' cohort (n = 84) and identified tumor budding-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify hub genes and construct a prognostic signature. Nomogram model was used to identified hub genes score for tumor budding, and the receiver operating characteristic (ROC) curve and calibration plot indicated high accuracy and stability of hub gene score for predicted the prognosis of CRC. The association between budding-associated hub genes and score and prognosis of CRC were further verified in TCGA CRC cohort (n = 342). Then gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to explore the signaling pathways related to the tumor budding and validated by immunohistochemistry (IHC) of our clinical samples. Subsequently, immune infiltration analysis demonstrated that there was a high correlation between hub genes score and M2-like macrophages infiltrated in tumor tissue. In addition, somatic mutation and chemotherapeutic response prediction were analyzed based on the risk signature. In summary, we established a tumor budding diagnostic molecular model, which can improve tumor budding assessment and provides a promising novel molecular marker for immunotherapy and prognosis of CRC.

Learning process analysis of robotic lateral pelvic lymph node dissection for local advanced rectal cancer: the CUSUM curve of 78 consecutive patients.

PURPOSE: Robotic lateral lymph node dissection (LLND) has been described as a safe and feasible procedure for local advanced rectal cancer. The aim of this study was to evaluate the learning curve for robotic-assisted LLND. METHODS: We collected data on 78 consecutive patients who underwent robotic-LLND at our hospital. The learning curve was analyzed using the cumulative sum (CUSUM) method to assess changes in the unilateral LLND operative times across the case sequence. RESULTS: Among the 78 patients, 52 underwent bilateral LLND and 26 underwent unilateral LLND. A total of 130 consecutive data were recorded. We arranged unilateral robotic-LLND operative times and calculated cumulative sum values, allowing the differentiation of three phases: phase I (learning period, cases 1-51); phase II (proficiency period, cases 52-83); and phase III (mastery period, cases 84-130). As the learning curve accumulated, the operation time and estimated blood loss of unilateral robotic-LLND decreased significantly with each phase (P < 0.05). By 12 months after surgery, the International Prostatic Symptom Score of patients at phase III was significantly lower than at phase I (P < 0.05). CONCLUSION: The CUSUM curve shows three phases in the learning of robotic-LLND. The estimated learning curve for robotic-assisted rectal-LLND is achieved after 51 cases.

The clinical predictive value of geriatric nutritional risk index in elderly rectal cancer patients received surgical treatment after neoadjuvant therapy.

Objective: The assessment of nutritional status has been recognized as crucial in the treatment of geriatric cancer patients. The objective of this study is to determine the clinical predictive value of the geriatric nutritional risk index (GNRI) in predicting the short-term and long-term prognosis of elderly rectal cancer (RC) patients who undergo surgical treatment after neoadjuvant therapy. Methods: Between January 2014 and December 2020, the clinical materials of 639 RC patients aged ≥70 years who underwent surgical treatment after neoadjuvant therapy were retrospectively analysed. Propensity score matching was performed to adjust for baseline potential confounders. Logistic regression analysis and competing risk analysis were conducted to evaluate the correlation between the GNRI and the risk of postoperative major complications and cumulative incidence of cancer-specific survival (CSS). Nomograms were then constructed for postoperative major complications and CSS. Additionally, 203 elderly RC patients were enrolled between January 2021 and December 2022 as an external validation cohort. Results: Multivariate logistic regression analysis showed that GNRI [odds ratio = 1.903, 95% confidence intervals (CI): 1.120-3.233, p = 0.017] was an independent risk factor for postoperative major complications. In competing risk analysis, the GNRI was also identified as an independent prognostic factor for CSS (subdistribution hazard ratio = 3.90, 95% CI: 2.46-6.19, p < 0.001). The postoperative major complication nomogram showed excellent performance internally and externally in the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). When compared with other models, the competing risk prognosis nomogram incorporating the GNRI achieved the highest outcomes in terms of the C-index, AUC, calibration plots, and DCA. Conclusion: The GNRI is a simple and effective tool for predicting the risk of postoperative major complications and the long-term prognosis of elderly RC patients who undergo surgical treatment after neoadjuvant therapy.