*Result*: Artificial Intelligence Models to Predict Recurrence Risk Prediction in Early-Stage Non-Small Cell Lung Cancer: A Systematic Review.

Title:
Artificial Intelligence Models to Predict Recurrence Risk Prediction in Early-Stage Non-Small Cell Lung Cancer: A Systematic Review.
Authors:
Yang Y; Department of Cardiothoracic Surgery, Heart and Vascular Center, Maastricht University Medical Center, Maastricht 6229 HX, The Netherlands., He H; Department of Cardiothoracic Surgery, Heart and Vascular Center, Maastricht University Medical Center, Maastricht 6229 HX, The Netherlands.; Department of Cardiothoracic Surgery, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht 6229 HX, The Netherlands., Yu C; Department of Cardiothoracic Surgery, Heart and Vascular Center, Maastricht University Medical Center, Maastricht 6229 HX, The Netherlands., Sardari Nia P; Department of Cardiothoracic Surgery, Heart and Vascular Center, Maastricht University Medical Center, Maastricht 6229 HX, The Netherlands.; Department of Cardiothoracic Surgery, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht 6229 HX, The Netherlands.
Source:
European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery [Eur J Cardiothorac Surg] 2026 Feb 05; Vol. 68 (2).
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: Germany NLM ID: 8804069 Publication Model: Print Cited Medium: Internet ISSN: 1873-734X (Electronic) Linking ISSN: 10107940 NLM ISO Abbreviation: Eur J Cardiothorac Surg Subsets: MEDLINE
Imprint Name(s):
Publication: 2012-: Oxford, England : Oxford University Press
Original Publication: [Berlin] : Springer International ; [Secaucus, NJ, USA : Springer-Verlag New York Inc., distributor, c1987-
MeSH Terms:
Carcinoma, Non-Small-Cell Lung*/diagnosis , Carcinoma, Non-Small-Cell Lung*/pathology , Carcinoma, Non-Small-Cell Lung*/surgery , Lung Neoplasms*/diagnosis , Lung Neoplasms*/pathology , Lung Neoplasms*/surgery , Neoplasm Recurrence, Local*/diagnosis , Neoplasm Recurrence, Local*/epidemiology , Neoplasm Recurrence, Local*/etiology , Predictive Learning Models*, Risk Assessment/methods ; Humans ; Neoplasm Staging ; Random Forest ; Area Under Curve ; Predictive Value of Tests ; Logistic Models ; Postoperative Period
Contributed Indexing:
Keywords: AI model; NSCLC; early-stage; recurrence risk
Entry Date(s):
Date Created: 20260210 Date Completed: 20260216 Latest Revision: 20260216
Update Code:
20260216
DOI:
10.1093/ejcts/ezag072
PMID:
41666303
Database:
MEDLINE

*Further Information*

*Objectives: The purpose of this study was to systematically evaluate predictive models for assessing the risk of postoperative recurrence in patients with early-stage non-small cell lung cancer, and to determine the effect of integrating different data modalities on model performance.
Methods: A systematic search of PubMed, Embase, and Web of Science databases up to April 30, 2025 identified eligible studies. Seventeen original studies were included after screening 2672 records and reviewing 133 full texts. Data extraction focused on study characteristics, types of data used, modelling strategies, and predictive performance. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST)+artificial intelligence (AI) tool.
Results: Random forest and random survival forest models performed robustly on single-modality data, while the integration of multimodality data significantly improved model performance (area under the curve [AUC] range: 0.72-0.94). Notably, the DeepRePath model based on XGBoost achieved an AUC of 0.94 in pathological image analysis, while graph neural networks also performed well in multicentre CT data analysis (AUC 0.785). However, models generally face the risk of overfitting. PROBAST+AI tool assessments revealed that 7 studies were classified as high-risk during the model development phase due to improper sample handling, while 13 studies exhibited high bias risk during the validation phase due to insufficient test set size (<100) or reliance on apparent performance.
Conclusions: Predictive models show promising accuracy for recurrence risk assessment in early-stage non-small cell lung cancer, with multimodal data integration improving generalizability.
Prospero: CRD42024629196.
(© The Author(s) 2026. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery.)*