Four Different Artificial Intelligence ModelsVersusLogistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background/Aim: This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression (LR) in enhancing the performance of the fecal immunochemical test (FIT) for the detection of colorectal carcinoma (CRC). Patients and Methods: The study cohort comprised 544 patients with colorectal neoplasia (CRN), including 58 CRC and 486 non-CRC cases, recruited from the Barretos Cancer Hospital. Each patient provided three consecutive fecal samples, which were analyzed using two fecal occult blood (FOB) assays: ColonView-FIT (CV) and HemoccultSENSA. Four AI models – gradient boosting machine (GBM), neural network (NN), random forest (RF), and support vector machine (SVM) – were developed, incorporating clinical features and CV results. Diagnostic performance was assessed via hierarchical summary receiver operating characteristic (HSROC) curves. Results: In conventional analysis, the area under the curve (AUC) values for different AI models ranged from 0.926 to 0.977, while the highest AUC values were reached by gradient boosting machine (GBM), neural network (NN), and random forest (RF) models (0.974, 0.976 and 0.977, respectively). In the HSROC analysis, the AUC values for i) ‘low risk’ variables, ii) ‘high risk’ variables, and iii) AI models were as follows: i) AUC=0.503 (95% CI=0.390-0.613), ii) AUC=0.773 (95% CI=0.713-0.837), and iii) AUC=0.958 (95% CI=0.930-0.989). In all comparisons of the AUC values, the difference was highly significant (p<0.0001). Conclusion: AI models outperformed conventional LR and non-AI diagnostic features in improving FIT-based CRC screening. This is the first study to show that combining clinical data with FIT results in AI frameworks can significantly improve diagnostic accuracy in CRC screening.
Source: Anticancer Research
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funder: Pohjois-Savon Rahasto
OpenAlex ID: W4410775533