Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.

Autor: Lippenberger F; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany., Ziegelmayer S; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany., Berlet M; Department of Surgery, School of Medicine, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.; Research Group MITI, Klinikum rechts der Isar, Technical University Munich, Munich, Germany., Feussner H; Department of Surgery, School of Medicine, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.; Research Group MITI, Klinikum rechts der Isar, Technical University Munich, Munich, Germany., Makowski M; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany., Neumann PA; Department of Surgery, School of Medicine, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany., Graf M; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany., Kaissis G; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.; Institute for Artificial Intelligence in Medicine and Healthcare, School of Medicine and Faculty of Informatics, Technical University of Munich, Munich, Germany., Wilhelm D; Department of Surgery, School of Medicine, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.; Research Group MITI, Klinikum rechts der Isar, Technical University Munich, Munich, Germany., Braren R; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.; German Cancer Consortium (DKTK, Partner Site Munich) and German Cancer Research Center, DKFZ, Heidelberg, Germany., Reischl S; Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. stefan.reischl@tum.de.
Jazyk: angličtina
Zdroj: International journal of colorectal disease [Int J Colorectal Dis] 2024 Jan 25; Vol. 39 (1), pp. 21. Date of Electronic Publication: 2024 Jan 25.
DOI: 10.1007/s00384-024-04593-z
Abstrakt: Purpose: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.
Methods: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC).
Results: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22).
Conclusion: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.
(© 2024. The Author(s).)
Databáze: MEDLINE
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