Does the visceral fat area impact the histopathology specimen metrics after total mesorectal excision for distal rectal cancer?

Robotic surgery is associated with a decreased risk of circumferential resection margin positivity compared with conventional laparoscopic surgery in patients with rectal cancer undergoing mesorectal excision: A systematic review and meta-analysis

OBJECTIVE

To investigate whether robotic surgery (RS) decreases the risk of circumferential resection margin (CRM) positivity compared with conventional laparoscopic surgery (LS) in patients with rectal cancer (RC) undergoing mesorectal excision (ME).

BACKGROUND

Although it is well known that CRM positivity affects postoperative outcomes in patients with RC undergoing ME, few studies have investigated whether RS is superior to conventional LS for the risk of CRM positivity.

METHODS

We performed a comprehensive electronic search of the literature up to December 2022 to identify studies that compared the risk of CRM positivity between patients with RC undergoing robotic and conventional laparoscopic surgery. A meta-analysis was performed using random-effects models to calculate risk ratios (RRs) and 95 % confidence intervals (CIs), and heterogeneity was analyzed using I2 statistics.

RESULTS

Eighteen studies, consisting of 4 randomized controlled trials (RCTs) and 14 propensity score matching (PSM) studies, involved a total of 9203 patients with RC who underwent ME were included in this meta-analysis. The results demonstrated that RS decreased the overall risk of CRM positivity (RR, 0.82; 95 % CI, 0.73-0.92; P = 0.001; I2 = 0 %) compared with conventional LS. Results of a meta-analysis of the 4 selected RCTs also showed that RS decreased the risk of CRM positivity (RR, 0.62; 95 % CI, 0.43-0.91; P = 0.01; I2 = 0 %) compared with conventional LS.

CONCLUSIONS

This meta-analysis revealed that RS is associated with a decreased risk of CRM positivity compared with conventional LS in patients with RC undergoing ME.

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer

BACKGROUND

The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure.

METHODS

This study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal's et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively.

RESULTS

In the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension.

CONCLUSION

In this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.

Impact of visceral fat area on short-term outcomes in robotic surgery for mid and low rectal cancer

Rectal cancer is one of the most prevalent cancers that arise in the digestive tract. The purpose of this retrospective study was to investigate the impact of visceral fat area (VFA) on postoperative outcomes in mid and low rectal cancer patients undergoing robotic surgery (RS). Data were collected on patients who underwent robotic anterior rectal resection in a single center from December 2019 to October 2023. Clinical pathology information was analyzed. Statistical analysis was done on the computed tomography (CT) imaging data. A total of 277 patients were included in the study, including 121 cases with visceral obesity (VO) and 156 cases without VO. There was no statistically significant disparity in the lymph node dissection count, blood loss, duration of hospitalization, time to first liquid diet, early postoperative complications, histopathologic specimen indices (quality of TME and CRM involvement rate), and or the rate of conversion to open surgery between VO and non-VO group (P > 0.05). Nevertheless, the group of individuals with VO experienced a lengthier duration of surgery (P < 0.001) and a delayed time until the first passage of flatus (P < 0.001) in comparison to the group without VO. The study suggests that VO does not significantly impact early complications or the quality of surgical outcomes in mid and low rectal cancer patients undergoing robotic surgery. The findings support the continued use of RS as an optimal strategy for technically demanding patients.