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

Teng, Wenhao; Liu, Jingfu; Chen, Meimei; Zang, Weidong; Wu, Aiwen

doi:10.1186/s12893-022-01840-4

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In a previous study by Kawai et al, body weight has been found to be correlated with lengthened SFM in univariable analysis, but the study does not include BMI and height. On the other hand, although several studies have shown that increased BMI is associated with di cult operation scores, lengthened operation time and increased risk for conversion during laparoscopic rectal cancer surgery, they have not speci cally investigated the correlation between SFM duration and BMI (17)(18)(19). The current study has demonstrated that higher BMI and weight may be indicators for lengthened SFM.…”

Section: Discussionmentioning

confidence: 57%

Prediction and Outcomes of Splenic Flexure Mobilization Duration during Laparoscopic Distal Colectomy for Colorectal Cancer: An Analysis of Operation Videos

Akyol,

Arslan,

Kocak

et al. 2023

Preprint

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Purpose: Splenic flexure mobilization (SFM) is a challenging step during laparoscopic colorectal operations. This study aims to inspect the predicting measures and consequences of lengthened SFM duration during laparoscopic distal colorectal cancer surgery. Methods: Patients underwent elective laparoscopic anterior or low anterior resection (AR or LAR) for distal sigmoid or rectal cancer were included. Data were abstracted from a prospectively designed database. A blinded observer to the patients and procedures watched the operation videos and tiered the steps of the procedures into 5 as follows: 1) medial-to-lateral dissection, 2) pelvic dissection, 3) dissection of the white line, 4) SFM, and 5) stapling, resection, extraction of the specimen and anastomosis. Patient and disease related measures predicting SFM duration and consequences of lengthened SFM duration were investigated. Results: A total of 105 patients (62 male, 59%) treated with AR (n=41) or LAR (N=64) were included to the study. No other patient or disease related features, but patients’ body mass index (BMI), weight and height were found to be significant predicting measures for lengthened SFM duration (p=0.0086, p=0.002 and p=0.43, respectively). The duration of SFM was correlating with the intervals of steps 1 (p=0.0013), 3 (p=0.0088) and 5 (p=0.04) and total operation time (p<0.0001). Lengthened SFM did not worsen postoperative measures including complications, hospitalization period and early oncological outcomes. Conclusions: Patients’ BMI, weight and height are the predicting measures for SFM duration at the time of laparoscopic distal colectomies. SFM duration shows a strong correlation with the durations of almost all other steps and total operation time but does not affect perioperative measures. Trial registration: E-10840098-772.02-61604 2.2.2019

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Section: Discussionmentioning

confidence: 57%

Prediction and Outcomes of Splenic Flexure Mobilization Duration during Laparoscopic Distal Colectomy for Colorectal Cancer: An Analysis of Operation Videos

Akyol,

Arslan,

Kocak

et al. 2023

Preprint

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“…Some patients underwent ileostomy at the same time of resection. In order to reduce the impact of this operation, the operation time of these patients was recorded as the initial time minus 15 minutes ( 17 ). When the entire operation cannot be completed by laparoscopy, it should be changed to acombined approach (transabdominal and transanal surgery).…”

Section: Methodsmentioning

confidence: 99%

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

Yu,

Yuan,

et al. 2024

Front. Oncol.

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BackgroundLaparoscopic 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.MethodsWe 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.ResultsA 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.ConclusionsThis 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.

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Workspace Analysis for Laparoscopic Rectal Surgery: A Preliminary Study

Thomieres,

Khanzode,

Duchalais

et al. 2024

Mechanisms and Machine Science

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

Cited by 6 publications

References 33 publications

Prediction and Outcomes of Splenic Flexure Mobilization Duration during Laparoscopic Distal Colectomy for Colorectal Cancer: An Analysis of Operation Videos

Prediction and Outcomes of Splenic Flexure Mobilization Duration during Laparoscopic Distal Colectomy for Colorectal Cancer: An Analysis of Operation Videos

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

Workspace Analysis for Laparoscopic Rectal Surgery: A Preliminary Study

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