BMI and pelvimetry help to predict the duration of laparoscopic resection for low and middle rectal cancer

Splenic flexure mobilization: does body topography matter?

BACKGROUND

Splenic flexure mobilization can be technically challenging, and its oncological benefits remain uncertain. This study aims to explore the relationship between patient and clinical characteristics and splenic flexure mobilization time as well as the implications of prolonged splenic flexure mobilization duration.

METHODS

This retrospective cohort study includes 105 patients who underwent laparoscopic distal colorectal cancer surgery between 2013 and 2018. The study analyzed patient characteristics, duration of surgical steps, and postoperative outcomes. Splenic flexure mobilization time was assessed using operation videos, and the impact of patient-related factors on splenic flexure mobilization complexity was examined.

RESULTS

The study identified significant correlations of higher body mass index (BMI) (p = 0.0086), weight (p = 0.002), and height (p = 0.043) with longer splenic flexure mobilization time. Gender did not significantly influence splenic flexure mobilization duration. Splenic flexure mobilization time was correlated with the durations of other individual surgical steps (Step 1: medial-to-lateral dissection [p = 0.0013], Step 2: pelvic dissection [p = 0.067], Step 3: dissection of white line and mobilization of descending colon [p = 0.0088], Step 5: stapling, resection, extraction of the specimen, and anastomosis [p = 0.04]) and the overall operation time (p < 0.0001). A 10-min cutoff point predicts the total operation time more efficiently than other potential thresholds.

CONCLUSION

This research suggests that patient characteristics including BMI, weight, and height may serve as indicators for prolonged splenic flexure mobilization time in laparoscopic distal colorectal cancer surgery. Longer splenic flexure mobilization durations were correlated with extended durations of other surgical steps. A BMI-based approach to anticipate SFM duration may enhance preoperative planning, potentially aiding in surgical decision-making.

TRIAL REGISTRATION

E-10840098-772.02-61604 2.2.2019.

Simple pelvimetry predicts the pelvic manipulation time in robot-assisted low and ultra-low anterior resection for rectal cancer

PURPOSE

This study explored the difficulty factors in robot-assisted low and ultra-low anterior resection, focusing on simple measurements of the pelvic anatomy.

METHODS

This was a retrospective analysis of the clinical data of 61 patients who underwent robot-assisted low and ultra-low anterior resection for rectal cancer between October 2018 and April 2023. The relationship between the operative time in the pelvic phase and clinicopathological data, especially pelvic anatomical parameters measured on X-ray and computed tomography (CT), was evaluated. The operative time in the pelvic phase was defined as the time between mobilization from the sacral promontory and rectal resection.

RESULTS

Robot-assisted low and ultra-low anterior resections were performed in 32 and 29 patients, respectively. The median operative time in the pelvic phase was 126 (range, 31-332) min. A multiple linear regression analysis showed that a short distance from the anal verge to the lower edge of the cancer, a narrow area comprising the iliopectineal line, short anteroposterior and transverse pelvic diameters, and a small angle of the pelvic mesorectum were associated with a prolonged operative time in the pelvic phase.

CONCLUSION

Simple pelvic anatomical measurements using abdominal radiography and CT may predict the pelvic manipulation time in robot-assisted surgery for rectal cancer.

Body fat ratio as a novel predictor of complications and survival after rectal cancer surgery

Background

The present study aimed to evaluate the association between body fat ratio (BFR), visceral fat area (VFA), body mass index (BMI) and visceral fat density (VFD) and assess their reliability in assessing risk of postoperative complications and survival status in patients with rectal cancer (RC).

Materials and methods

The present study retrospectively included 460 patients who underwent surgical treatment for RC at the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College, Wuhu, China) between September 2018 and July 2021. BFR, VFA, BMI, and VFD were measured and basic information, clinical data, complications and survival were recorded.

Results

Statistical analysis was performed to determine optimal BFR cut-off and evaluate group differences. BFR demonstrated a significant positive correlation with VFA (R = 0.739) and BMI (R = 0.783) and significant negative correlation with VFD (R = -0.773). The areas under the receiver operating characteristic curve of BFR, VFA, BMI, and VFD in predicting postoperative complications in RC were all >0.7 and the optimal cut-off value of BFR was 24.3. Patients in the BFR-low group had fewer postoperative complications, lower intraoperative indices, shorter hospitalization times and lower costs than those in the BFR-high group. BFR predicted complications with high diagnostic significance and was validated by multiple models. Furthermore, patients in the BFR-high group had a longer overall survival compared with patients in the BFR-low group.

Conclusion

BFR was associated with BMI, VFA, and VFD. A BFR threshold of 24.3 was correlated with decreased complications and enhanced long-term survival.

Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer

Background

Laparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models' performance.

Methods

We retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model.

Results

A total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors.

Conclusions

This study developed an XGBoost model to predict the difficulty of LaTME surgery. This model can help clinicians quickly and accurately predict the difficulty of surgery and adopt individualized surgical methods.

Creation of a Novel, Race-Adjusted, and Risk-Adapted Scoring System to Predict Positive Surgical Margins and Prolonged Operative Time During Robotic Radical Prostatectomy

Objectives: To compare racial differences and pelvis dimensions between Caucasians and African Americans (AAs) and to develop a risk calculator and scoring system to predict the risk of prolonged operative time and presence of positive surgical margins (PSM) based on these dimensions. Materials and Methods: A retrospective review of 88 consecutive patients undergoing robot-assisted laparoscopic prostatectomy with a preoperative prostate MRI conducted. Data extraction included demographic, perioperative, and postoperative oncologic outcomes. Prostate-specific antigen (PSA) was obtained within 3 months postsurgery. Wilcoxon rank sum and Fisher's exact tests were used to compare continuous and categorical data, respectively. Single and multivariable regression analysis were used to determine contribution of each factor to the composite outcomes. A risk score was created based on this analysis for predicting the composite outcome. Results: We identified 88 consecutive patients with localized prostate cancer that underwent a preoperative prostate MRI. No statistically significant differences were found with respect to age, body mass index, or any postoperative outcome. PSA was lower at diagnosis (6.49 vs 9.72, p = 0.006) and operative times were shorter in Caucasians. Rates of PSM (13 vs 14, p = 0.35), biochemical recurrence (4 vs 2, p = 0.69), and complications did not vary between the groups. Caucasians had wider/shallower pelvis dimensions. Based on these variables, we found that the log (odds of OR time >3 hours or PSM) = -5.333 + 1.158 (if AA) +0.105 × PSA +0.076 × F -0.035 × G with an area under the receiver operating characteristic curve = 0.73. Using the predefined variables, patients can be risk stratified for PSM or prolonged operative times. Conclusions: Several pelvis dimensions were found to be shorter/narrower in AAs and were associated with longer operative times. The presented risk calculator and stratification system may be used to predict prolonged operative time or having PSM.