PDF herunterladen
Open Access
CC BY 4.0 · Journal of Coloproctology 2025; 45(04): s00451813736
DOI: 10.1055/s-0045-1813736
Review Article

Predictive Analytics in Obstructed Colon Cancer: A Comparative Narrative Review of Clinical and AI-Based Models

Autor*innen

  • Sreejith Kannummal Veetil 

    1   Department of General and Laparoscopic Surgery, Christian Medical College, Ludhiana, Punjab, India

  • Parvez David Haque 

    1   Department of General and Laparoscopic Surgery, Christian Medical College, Ludhiana, Punjab, India

  • Deepak Jain 

    1   Department of General and Laparoscopic Surgery, Christian Medical College, Ludhiana, Punjab, India

  • Binni Sharma 

    2   Department of Trauma and Emergency Bhatti Hospital, Ludhiana, Punjab, India
Weitere Informationen
Auch verfügbar auf
  Lizenzen und Reprints

Abstract

Introduction

Malignant large-bowel obstruction (LBO) occurs in 8 to 15% of colorectal cancer cases, and it is linked to high rates of complications and death. Identifying patient risk before surgery is essential to choose between emergency surgery and stenting.

Objective

To predict outcomes in patients with obstructed colon cancer by comparing traditional clinical scoring systems (such as the American Society of Anesthesiologists [ASA] Physical Status Classification System, the Physiological and Operative Severity Score for Enumeration of Mortality and Morbidity [POSSUM], and the Association of Coloproctology of Great Britain & Ireland [ACPGBI] score) with modern artificial intelligence (AI) and machine learning (ML) models (such as Random Forest [RF], K-Nearest Neighbors [KNN], Extreme Gradient Boosting [XGBoost], and nomograms).

Materials and Methods

We conducted a narrative review of peer-reviewed articles on prognostic models for malignant LBO. For each study, we recorded the variables used by the model, how user-friendly and interpretable it was, its performance metrics (such as the area under the receiver operating characteristic [AUROC] curve and accuracy), and its potential for clinical use. We organized the findings into five summary tables, comparing conventional risk scores and AI-based approaches in multiple dimensions. The focus is on practical, clinically-relevant insights rather than detailed algorithmic explanations.

Results

Traditional scores rely on a fixed set of clinical and operative variables. While easy to calculate and widely understood, they perform suboptimally in emergent LBO, with the Colorectal POSSUM (CR-POSSUM) showing an AUROC of approximately 0.65, and the ACPGBI reaching an AUROC of approximately 0.80 in elective settings. Conversely, AI models that leverage multiple perioperative inputs achieve higher accuracy: a Random Forest model yielded an AUROC of approximately 0.79 (95%CI: 0.71–0.87) on training and ranging from 0.75 to 0.82 on validation; a KNN model recorded approximately 88% of accuracy (AUROC: ∼ 0.77); and logistic regression nomograms attained AUC values near 0.84 for specific outcomes.

Conclusion

Models based on AI generally outperform traditional risk scores in predicting short-term outcomes of malignant bowel obstruction, but they require high-quality electronic data and technological infrastructure. Traditional scores offer ease-of-use and interpretability, but they are less accurate in this setting. Clinicians should consider hybrid strategies, such as using familiar scores (ASA, the Charlson Comorbidity Index [CCI]) for quick triage, and AI/nomogram tools for detailed risk when data is available. Future work must address implementation barriers (such as data integration, model explainability, external validation, and prospective impact studies) to translate these tools into real-world perioperative decision-making.

Keywords

malignant large-bowel obstruction - risk stratification - predictive analytics - clinical risk scores - machine-learning models - artificial intelligence

Authors' Contributions

Sreejith Kannummal Veetil: Conceptualization; Methodology; Formal Analysis; Data Curation; Writing – Original Draft; Visualization.

Parvez David Haque: Investigation; Resources; Data Curation; Writing – Review & Editing.

Deepak Jain: Methodology; Software; Validation; Writing – Review & Editing.

Binni Sharma: Project Administration; Funding Acquisition; Supervision; Writing – Review & Editing.




Publikationsverlauf

Eingereicht: 09. Juni 2025

Angenommen: 11. August 2025

Artikel online veröffentlicht:
29. Dezember 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

Thieme Revinter Publicações Ltda.
Rua Rego Freitas, 175, loja 1, República, São Paulo, SP, CEP 01220-010, Brazil

Bibliographical Record
Sreejith Kannummal Veetil, Parvez David Haque, Deepak Jain, Binni Sharma. Predictive Analytics in Obstructed Colon Cancer: A Comparative Narrative Review of Clinical and AI-Based Models. Journal of Coloproctology 2025; 45: s00451813736.
DOI: 10.1055/s-0045-1813736