Pelvimetric and Nutritional Factors Predicting Surgical Difficulty in Laparoscopic Resection for Rectal Cancer Following Preoperative Chemoradiotherapy

Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer

BACKGROUND

Colorectal surgeons are well aware that performing surgery for rectal cancer becomes more challenging in obese patients with narrow and deep pelvic cavities. Therefore, it is essential for colorectal surgeons to have a comprehensive understanding of pelvic structure prior to surgery and anticipate potential surgical difficulties.

AIM

To evaluate predictive parameters for technical challenges encountered during laparoscopic radical sphincter-preserving surgery for rectal cancer.

METHODS

We retrospectively gathered data from 162 consecutive patients who underwent laparoscopic radical sphincter-preserving surgery for rectal cancer. Three-dimensional reconstruction of pelvic bone and soft tissue parameters was conducted using computed tomography (CT) scans. Operative difficulty was categorized as either high or low, and multivariate logistic regression analysis was employed to identify predictors of operative difficulty, ultimately creating a nomogram.

RESULTS

Out of 162 patients, 21 (13.0%) were classified in the high surgical difficulty group, while 141 (87.0%) were in the low surgical difficulty group. Multivariate logistic regression analysis showed that the surgical approach using laparoscopic intersphincteric dissection, intraoperative preventive ostomy, and the sacrococcygeal distance were independent risk factors for highly difficult laparoscopic radical sphincter-sparing surgery for rectal cancer (P < 0.05). Conversely, the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance was identified as a protective factor (P < 0.05). A nomogram was subsequently constructed, demonstrating good predictive accuracy (C-index = 0.834).

CONCLUSION

The surgical approach, intraoperative preventive ostomy, the sacrococcygeal distance, and the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance could help to predict the difficulty of laparoscopic radical sphincter-preserving surgery.

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.

Establishment of Surgical Difficulty Grading System and Application of MRI-Based Artificial Intelligence to Stratify Difficulty in Laparoscopic Rectal Surgery

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis.

Development and validation of a preoperative nomogram for predicting the surgical difficulty of laparoscopic colectomy for right colon cancer: a retrospective analysis

BACKGROUND

In laparoscopic right hemicolectomy for right colon cancer, complete mesocolic excision is a standard procedure that involves extended lymphadenectomy and blood vessel ligation. This study aimed to establish a nomogram to facilitate evaluation of the surgical difficulty of laparoscopic right hemicolectomy based on preoperative parameters.

MATERIALS AND METHODS

The preoperative clinical and computed tomography-related parameters, operative details, and postoperative outcomes were analyzed. The difficulty of laparoscopic colectomy was defined using the scoring grade reported by Escal et al . with modifications. Multivariable logistic analysis was performed to identify parameters that increased the surgical difficulty. A preoperative nomogram to predict the surgical difficulty was established and validated.

RESULTS

A total of 418 consecutive patients with right colon cancer who underwent laparoscopic radical resection at a single tertiary medical center between January 2016 and May 2022 were retrospectively enrolled. The patients were randomly assigned to a training data set ( n =300, 71.8%) and an internal validation data set ( n =118, 28.2%). Meanwhile, an external validation data set with 150 consecutive eligible patients from another tertiary medical center was collected. In the training data set, 222 patients (74.0%) comprised the non-difficulty group and 78 (26.0%) comprised the difficulty group. Multivariable analysis demonstrated that adipose thickness at the ileocolic vessel drainage area, adipose area at the ileocolic vessel drainage area, adipose density at the ileocolic vessel drainage area, presence of the right colonic artery, presence of type III Henle's trunk, intra-abdominal adipose area, plasma triglyceride concentration, and tumor diameter at least 5 cm were independent risk factors for surgical difficulty; these factors were included in the nomogram. The nomogram incorporating seven independent predictors showed a high C-index of 0.922 and considerable reliability, accuracy, and net clinical benefit.

CONCLUSIONS

The study established and validated a reliable nomogram for predicting the surgical difficulty of laparoscopic colectomy for right colon cancer. The nomogram may assist surgeons in preoperatively evaluating risk and selecting appropriate patients.

Development of artificial blood loss and duration of excision score to evaluate surgical difficulty of total laparoscopic anterior resection in rectal cancer

Purpose

Total laparoscopic anterior resection (tLAR) has been gradually applied in the treatment of rectal cancer (RC). This study aims to develop a scoring system to predict the surgical difficulty of tLAR.

Methods

RC patients treated with tLAR were collected. The blood loss and duration of excision (BLADE) scoring system was built to assess the surgical difficulty by using restricted cubic spline regression. Multivariate logistic regression was used to evaluate the effect of the BLADE score on postoperative complications. The random forest (RF) algorithm was used to establish a preoperative predictive model for the BLADE score.

Results

A total of 1,994 RC patients were randomly selected for the training set and the test set, and 325 RC patients were identified as the external validation set. The BLADE score, which was built based on the thresholds of blood loss (60 ml) and duration of surgical excision (165 min), was the most important risk factor for postoperative complications. The areas under the curve of the predictive RF model were 0.786 in the training set, 0.640 in the test set, and 0.665 in the external validation set.

Conclusion

This preoperative predictive model for the BLADE score presents clinical feasibility and reliability in identifying the candidates to receive tLAR and in making surgical plans for RC patients.

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

BACKGROUND

In rectal cancer surgery, recent studies have found associations between clinical factors, especially pelvic parameters, and surgical difficulty; however, their findings are inconsistent because the studies use different criteria. This study aimed to evaluate common clinical factors that influence the operative time for the laparoscopic anterior resection of low and middle rectal cancer.

METHODS

Patients who underwent laparoscopic radical resection of low and middle rectal cancer from January 2018 to December 2020 were retrospectively analyzed and classified according to the operative time. Preoperative clinical and magnetic resonance imaging (MRI)-related parameters were collected. Logistic regression analysis was used to identify factors for predicting the operative time.

RESULTS

In total, 214 patients with a mean age of 60.3 ± 8.9 years were divided into two groups: the long operative time group (n = 105) and the short operative time group (n = 109). Univariate analysis revealed that the male sex, a higher body mass index (BMI, ≥ 24.0 kg/m²), preoperative treatment, a smaller pelvic inlet (< 11.0 cm), a deeper pelvic depth (≥ 10.7 cm) and a shorter intertuberous distance (< 10.1 cm) were significantly correlated with a longer operative time (P < 0.05). However, only BMI (OR 1.893, 95% CI 1.064-3.367, P = 0.030) and pelvic inlet (OR 0.439, 95% CI 0.240-0.804, P = 0.008) were independent predictors of operative time. Moreover, the rate of anastomotic leakage was higher in the long operative time group (P < 0.05).

CONCLUSION

Laparoscopic rectal resection is expected to take longer to perform in patients with a higher BMI or smaller pelvic inlet.

Chylous ascites after complete mesocolic excision for right-sided colon cancer with D3 lymphadenectomy: A retrospective cohort-study

AIM

This retrospective study was designed to evaluate risk factors of the occurrence and severity of chylous ascites after complete mesocolic excision (CME) and D3 lymphadenectomy in patients with right-sided colon cancer.

METHODS

Consecutive patients receiving CME and D3 lymphadenectomy for right-sided colon cancer were included. Risk factors of the occurrence and severity of chylous ascites by using logistic analysis were assessed. A nomogram predicting chylous ascites was constructed.

RESULTS

Among 661 patients included in the study, postoperative chylous ascites occurred in 48 (7.3%) patients. Logistic regression analysis demonstrated that prognostic nutritional index (PNI ≤ 47, OR = 2.172, p = 0.016), laparoscopic surgery (OR = 2.798, p = 0.034), operating time (>225 min, OR = 2.645, p = 0.002), and apical lymph node (APN) metastasis (OR = 3.698, p = 0.034) were correlated with the occurrence of postoperative chylous ascites. A nomogram predicting postoperative chylous ascites was constructed (C-index 0.701). 31.2% (15/48) of patients with chylous ascites were resolved in more than 7 days. The number of retrieved lymph nodes (OR = 1.074, 95% CI: 1.002-1.152, p = 0.044) and PNI ≤ 47 (OR = 7.890, 95% CI: 1.224-50.869, p = 0.030) were independently predictive of prolonged chylous ascites resolution (≥7 days).

CONCLUSIONS

In our series, 7.3% of patients developed chylous ascites after right hemicolectomy with CME and D3 lymphadenectomy. Laparoscopic surgery, PNI, operation time, and APN metastasis were independently predictive of postoperative chylous ascites. Lower PNI and more retrieved lymph nodes were correlated with prolonged resolution of chylous ascites.