ObjectiveTo systematically review the models for predicting coronary artery disease (CAD) and demonstrate their predictive efficacy. MethodsPubMed, EMbase and China National Knowledge Internet were searched comprehensively by computer. We included studies which were designed to develop and validate predictive models of CAD. The studies published from inception to September 30, 2020 were searched. Two reviewers independently evaluated the studies according to the inclusion and exclusion criteria and extracted the baseline characteristics and metrics of model performance.ResultsA total of 30 studies were identified, and 19 diagnostic predictive models were for CAD. Seventeen models had external validation group with area under curve (AUC)>0.7. The AUC for the external validation of the traditional models, including Diamond-Forrester model, updated Diamond-Forrester model, Duke Clinical Score, CAD consortium clinical score, ranged from 0.49 to 0.87.ConclusionMost models have modest discriminative ability. The predictive efficacy of traditional models varies greatly among different populations.
Objective To validate the performance of a CT imaging feature-based prediction model for identifying high-grade histological patterns (HGP), specifically micropapillary and solid subtypes, in stage ⅠA invasive lung adenocarcinoma. Methods A previously developed prediction model was applied to a cohort of 650 patients with stage ⅠA lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model’s ability to discriminate HGP (assessed by area under the receiver operating characteristic curve), calibration, and clinical utility were evaluated based on extracted imaging parameters including tumor size, density, and lobulation. Results Validation revealed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). Calibration of the original model improved its calibration performance. Decision curve analysis (DCA) indicated that the model’s predicted HGP patient population closely approximated the actual population when using a threshold probability>0.6. Conclusion This study confirms the effectiveness of a CT imaging feature-based prediction model for identifying HGP in stage ⅠA lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patient prognosis. Despite certain limitations, these findings provide new directions for future research.