Preoperative Prediction of Difficult Laparoscopic Cholecystectomy: Evolution, Validation, and Emerging Machine-Learning Approaches-A Scoping Review.
- 1Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Târgu Mureș, 540139 Targu Mures, Romania.
- 2General Surgery Clinic No. 1, County Emergency Clinical Hospital of Târgu-Mureș, 540136 Targu Mures, Romania.
Abstract
Background and Objectives: Preoperative prediction of difficult laparoscopic cholecystectomy (LC) has expanded from point-based scores to regression, nomogram, machine-learning, and quantitative imaging approaches. We examined whether this diversification was accompanied by stronger validation and clinical translation. Materials and Methods: Following a registered Open Science Framework protocol, we conducted a scoping review using Joanna Briggs Institute methodology and PRISMA-ScR. PubMed/MEDLINE, Scopus, and Web of Science were searched on 8 August 2026 for English-language publications from 1 January 2010 through 31 July 2026. Eligible studies evaluated structured multivariable tools using exclusively preoperative inputs. Screening and data charting were performed independently and in duplicate. Results: Searches identified 893 records; removal of 55 records outside the publication window and 372 duplicates left 466 for screening. Sixty reports were assessed as full text, and 59 were included; 47 (79.7%) were published from 2020 onward. Point-based scores predominated (45/59, 76.3%), followed by nomograms (5/59, 8.5%), regression-based probability models (4/59, 6.8%), machine-learning models (4/59, 6.8%), and one quantitative imaging tool (1/59, 1.7%). Four model-development publications (6.8%) included a geographic external validation component, including one with mixed temporal and geographic validation. Calibration was reported in nine publications (15.3%), decision-curve analysis in seven (11.9%), and SHAP-based explainability in two (3.4%). Two publications evaluated scores during clinical use; no controlled clinical-impact study was identified. Conclusions: Evidence of transportability and clinical impact remains limited. Future studies should prioritize standardized prediction targets, complete model specification, calibration, independent validation, and controlled evaluation of model-guided care.
Related
- Development and Internal Validation of a Preoperative Risk Stratification Model for Difficult Laparoscopic Cholecystectomy in Acute Cholecystitis
- Validation of the Nassar Preoperative Risk Scale to Predict Intraoperative Difficulty in Patients Undergoing Laparoscopic Cholecystectomy.
- Development and validation of a machine learning model for preoperative prediction of intraoperative hypothermia in gynecological laparoscopic surgery: a two-center cohort study
- Association between preoperative geriatric nutritional risk index and early postoperative cognitive decline after laparoscopic cholecystectomy
- Incremental value of laboratory and ultrasonographic markers for preoperative risk stratification of difficult laparoscopic cholecystectomy: a cross-validated cohort study
BibTeX
@article{botoncea2026preoperative,
title = {{Preoperative Prediction of Difficult Laparoscopic Cholecystectomy: Evolution, Validation, and Emerging Machine-Learning Approaches-A Scoping Review.}},
author = {Marian Botoncea and Cătălin Dumitru Cosma and Dragoș Călin Molnar and Vlad Olimpiu Butiurca and Cosmin Lucian Nicolescu},
journal = {Medicina (Kaunas, Lithuania)},
year = {2026},
month = oct,
doi = {10.3390/medicina62102042},
url = {https://europepmc.org/article/MED/42942574},
note = {CC BY},
}
RIS
TY - JOUR
TI - Preoperative Prediction of Difficult Laparoscopic Cholecystectomy: Evolution, Validation, and Emerging Machine-Learning Approaches-A Scoping Review.
AU - Marian Botoncea
AU - Cătălin Dumitru Cosma
AU - Dragoș Călin Molnar
AU - Vlad Olimpiu Butiurca
AU - Cosmin Lucian Nicolescu
PY - 2026
DA - 2026/10/21
JO - Medicina (Kaunas, Lithuania)
DO - 10.3390/medicina62102042
UR - https://europepmc.org/article/MED/42942574
AB - Background and Objectives: Preoperative prediction of difficult laparoscopic cholecystectomy (LC) has expanded from point-based scores to regression, nomogram, machine-learning, and quantitative imaging approaches. We examined whether this diversification was accompanied by stronger validation and clinical translation. Materials and Methods: Following a registered Open Science Framework protocol, we conducted a scoping review using Joanna Briggs Institute methodology and PRISMA-ScR. PubMed/MEDLINE, Scopus, and Web of Science were searched on 8 August 2026 for English-language publications from 1 January 2010 through 31 July 2026. Eligible studies evaluated structured multivariable tools using exclusively preoperative inputs. Screening and data charting were performed independently and in duplicate. Results: Searches identified 893 records; removal of 55 records outside the publication window and 372 duplicates left 466 for screening. Sixty reports were assessed as full text, and 59 were included; 47 (79.7%) were published from 2020 onward. Point-based scores predominated (45/59, 76.3%), followed by nomograms (5/59, 8.5%), regression-based probability models (4/59, 6.8%), machine-learning models (4/59, 6.8%), and one quantitative imaging tool (1/59, 1.7%). Four model-development publications (6.8%) included a geographic external validation component, including one with mixed temporal and geographic validation. Calibration was reported in nine publications (15.3%), decision-curve analysis in seven (11.9%), and SHAP-based explainability in two (3.4%). Two publications evaluated scores during clinical use; no controlled clinical-impact study was identified. Conclusions: Evidence of transportability and clinical impact remains limited. Future studies should prioritize standardized prediction targets, complete model specification, calibration, independent validation, and controlled evaluation of model-guided care.
SN - 1010-660X
SN - 1648-9144
ER -