AUTHOR=Jin Shuai , Xie Lang , You Yanwei , He Chengli , Li Xianghai TITLE=Development and validation of a nomogram to predict B-cell primary thyroid malignant lymphoma-specific survival: A population-based analysis JOURNAL=Frontiers in Endocrinology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.965448 DOI=10.3389/fendo.2022.965448 ISSN=1664-2392 ABSTRACT=

B cell primary thyroid malignant lymphoma (BC-PTML) accounts for 95% of all cases of PTML. However, development of effective treatment and management strategies for BC-PTML is challenging owing to the rarity of this disease. This study assessed data from 1,152 patients in the Surveillance, Epidemiology, and End Results (SEER) database who were diagnosed with BC-PTML during 2000–2015. Patients were randomly divided into a training group (n=806) and a test group (n=346) at a ratio of 7:3 using the hold-out method. Kaplan-Meier analysis and log-rank tests were used to calculate the survival rate of patients. Subsequently, a stepwise Cox regression model was established to screen the prognostic factors of patients with BC-PTML, and these variables were used to construct a nomogram to predict 5-, 10-, and 15-year BC-PTML cancer-specific survival (CSS). The discrimination and calibration of the new model were evaluated using the concordance index (C-index) and calibration curves, and the accuracy and benefits of the model were assessed through comparison with the traditional Ann Arbor staging system using decision curve analysis (DCA). After stepwise regression, the optimal model included radiotherapy, primary site surgery, Ann Arbor Stage, chemotherapy, histological subtype, and age at diagnosis. The C-index, area under the receiver operating characteristic curve, and DCA suggested that the nomogram had improved discriminatory ability and clinical benefit compared with the Ann Arbor staging system. In summary, this study established an effective nomogram to predict CSS in patients with BC-PTML and assist clinicians in developing effective individualized treatment strategies.