A new prognostic nomogram in patients with mucosa-associated lymphoid tissue lymphoma: a multicenter retrospective study - Wen, Qiuyue; Li, Xiaoqian; Zhao, Kewei; Li, Qiuhui; Zhu, Fang; Wu, Gang; Lin, Tongyu; Zhang, Liling (Frontiers in Oncology, 2023)
Background: The present study sought to understand how clinical factors and inflammatory biomarkers affected the prognosis of mucosa-associated lymphoid tissue (MALT) lymphoma and develop a predictive nomogram to assist in clinical practice. Methods: We conducted a retrospective study on 183 cases of newly diagnosed MALT lymphoma from January 2011 to October 2021, randomly divided into two groups: a training cohort (75%); and a validation cohort (25%). The least absolute shrinkage and selection operator (LASSO) regression analysis was combined with multivariate Cox regression analysis to construct a nomogram for predicting the progression-free survival (PFS) in patients with MALT lymphoma. To evaluate the accuracy of the nomogram model, the area under the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used. Results: The PFS was significantly associated with the Ann Arbor Stage, targeted therapy, radiotherapy, and platelet-to-lymphocyte ratio (PLR) in MALT lymphoma. These four variables were combined to establish a nomogram to predict the PFS rates at three and five years. Importantly, our nomogram yielded good predictive value with area under the ROC curve (AUC) values of 0.841 and 0.763 in the training cohort and 0.860 and 0.879 in the validation cohort for the 3-year and 5-year PFS, respectively. Furthermore, the 3-year and 5-year PFS calibration curves revealed a high degree of consistency between the prediction and the actual probability of relapse. Additionally, DCA demonstrated the net clinical benefit of this nomogram and its ability to identify high-risk patients accurately. Conclusion: The new nomogram model could accurately predict the prognosis of MALT lymphoma patients and assist clinicians in designing individualized treatments.