A predictive diagnostic model for refractory diffuse large B-cell lymphoma: a single-center retrospective cohort study

Abstract

To develop a baseline predictive model for refractory diffuse large B-cell lymphoma (DLBCL) utilizing imaging data including ultrasound findings and PET-CT in conjunction with clinical parameters. We retrospectively analyzed data from 140 patients with newly diagnosed DLBCL treated at Peking University Third Hospital between January 2018 and January 2023. All patients underwent ultrasound, histopathological examinations and PET-CT examinations. After completing 6–8 cycles of standardized chemotherapy, patients were categorized into refractory and non-refractory groups according to the Lugano International Response Assessment Criteria. Univariate analyses were performed using T-tests and Chi-Squared Tests, and independent risk factors for refractory DLBCL were identified through logistic regression. A nomogram predictive model was constructed using the R package “rms,” and its predictive performance was subsequently validated. Univariate analysis and logistic regression identified that blurred margins of the affected lymph nodes in ultrasound images (P < 0.001, OR = 18.238) and IPI score(P = 0.051, OR = 3.131) were significant risk factors for disease progression. The predictive nomogram established for refractory diffuse large B-cell lymphoma demonstrated an area under the receiver operating characteristic curve (AUC) of 0.835, with a sensitivity of 85.5% and specificity of 79.5%. Following internal validation, the predictive model exhibited a high degree of alignment between the estimated risk of refractory diffuse large B-cell lymphoma and the actual observed progression events. The prediction model of the R-DLBCL prediction model, amalgamating ultrasonic characterizations and clinical indicators, proves instrumental in identifying high-risk DLBCL groups.