AIM:To explore the effectiveness of predictive models based on quantitative characteristics of multimodal fundus images in assessing the risk of progression of non-proliferative diabetic retinopathy(NPDR).METHODS:A single-center retrospective cohort study was conducted. Patients diagnosed with moderate to severe NPDR between January 2021 and December 2023 in Wuhan Ninth Hospital were selected. Baseline color fundus photography(CFP), optical coherence tomography(OCT), and fundus autofluorescence(FAF)images were collected. Microaneurysm count, hard exudate area, central retinal thickness, retinal nerve fiber layer thickness, and total area of hyperautofluorescent spots were extracted and quantified. All patients were followed up regularly. The primary endpoint was the progression to treatment-requiring proliferative diabetic retinopathy(PDR)or clinically significant diabetic macular edema(DME)during the follow-up period. According to the occurrence of endpoint events during follow-up, the patients were divided into the progression group and the non-progression group. Combined with clinical significance and inter-group comparison results, candidate quantitative imaging parameters were included in the multivariate logistic regression analysis to construct a prediction model. The model was visualized using a Nomogram. The discriminative ability and calibration degree of the model were evaluated via receiver operating characteristic(ROC)curves and calibration curves.RESULTS:A total of 300 patients(300 eyes)completed follow-up, with 93 in the progression group, including 47 males and 46 females, with an average age of 59.02±8.21 y. There were 207 cases in the non-progression group, including 105 males and 102 females, with an average age of 58.01±8.10 y. Multivariate logistic regression showed that the total area of FAF hyperautofluorescent spots(OR=1.580, 95%CI: 1.061-1.718, P=0.002),OCT retinal nerve fiber layer thickness(OR=1.520, 95%CI: 1.307-6.212, P=0.013), and CFP microaneurysm count(OR=1.459, 95%CI: 1.089-2.095, P=0.015)were risk factors for DR progression. The AUC of the Nomogram for predicting progression risk during the follow-up period was 0.889(95%CI: 0.826-0.952), and the calibration curve showed good consistency between predicted probability and actual risk.CONCLUSION: The prediction model integrating quantitative imaging features of CFP, OCT and FAF can effectively identify NPDR patients with high progression risk and exhibits good predictive efficacy, which can serve as a quantitative tool for individualized clinical risk management.