@ARTICLE{10.3389/fonc.2022.1055046, AUTHOR={Yang, Ziyue and Li, Zhenfen and Fu, Chunmeng and Zhu, Yuanyuan and Lin, Ying and Deng, Ying and Li, Ning and Peng, Fang}, TITLE={Development and validation of a nomogram to predict overall survival and cancer-specific survival in patients with primary intracranial malignant lymphoma: A Retrospective study based on the SEER database}, JOURNAL={Frontiers in Oncology}, VOLUME={12}, YEAR={2023}, URL={https://www.frontiersin.org/articles/10.3389/fonc.2022.1055046}, DOI={10.3389/fonc.2022.1055046}, ISSN={2234-943X}, ABSTRACT={IntroductionPrimary intracranial malignant lymphoma (PIML) is a rare form of lymphoma that most often occurs in the brain and has an extremely low 5-year survival rate. Although chemotherapy and radiotherapy are widely used in the clinical management of PIML, the choice of treatment regimen and the actual circumstances of patients remain challenges when assessing survival rates in different patients.MethodsConsidering this, we obtained clinical treatment and survival information from the Surveillance, Epidemiology, and End Results database (SEER) on patients with lymphoma, the primary site of which was the brain, and performed statistical analyses of the demographic characteristics. Survival analyses were performed using the Kaplan–Meier method, and univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent prognostic factors.ResultWe identified age, pathology, the Ann Arbor stage, and treatment as the risk factors affecting patient prognosis. The areas under the curve (AUCs) for overall survival at 1, 3, and 5 years were 0.8, 0.818, and 0.81, respectively. The AUCs for cancer-specific survival at 1, 3, and 5 years were 0.8, 0.79, and 0.79. The prediction ability in the development and verification cohorts was in good agreement with the actual values, while we plotted the clinical decision curves for the model, suggesting that the nomogram can provide benefits for clinical decision-making.ConclusionOur model provides a prognostic guide for patients with PIML and a reliable basis for clinicians.} }