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

Lv, Jingfang; Guan, Xu; Wei, Ran; Yin, Yanping; Liu, Enrui; Zhao, Zhixun; Chen, Haipeng; Liu, Zheng; Jiang, Zheng; Wang, Xishan

doi:10.3389/fonc.2023.1067414

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…In fact, previous studies have developed models that use machine learning techniques to predict the difficulty of rectal cancer surgery. For example, Lv ( 60 )et al established a blood loss and resection duration (BLADE) scoring system, and used RF algorithm to establish a preoperative prediction model of BLADE score. Our research focuses on early identification of predictors that affect the difficulty of LaTME surgery.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Escal et al [ 17 ]and other studies [ 37 ] included blood loss in the scoring criteria, and we considered that in some surgeries, which are interfered with by factors such as abdominal lavage, the measurement of blood loss may not be completely accurate, which may result in the grouping of surgical difficulty in some patients. Therefore, to formulate the prediction model more objectively, to make the model more persuasive, and to facilitate the generalization of the model, we did not include blood loss in the scoring factors, and we do not deny that intraoperative blood loss may reflect the difficulty of surgery to a certain extent.…”

Section: Discussionmentioning

confidence: 99%

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

Li,

Zhou,

Zhu

et al. 2024

World J Surg Onc

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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.

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

Cited by 2 publications

References 49 publications

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

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

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

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