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<p>Primary lymphoma of bone (PLB) is a rare extranodal lymphoma, and its epidemiology and prognosis remain controversial. We conducted a retrospective analysis of 1,222 patients with PLB in the Surveillance, Epidemiology, and End Results (SEER) database to investigate its epidemiology and prognostic factors. The incidence of PLB peaked in 1992 with an average annual percent change of 1.72 after a significant rise from 1975 to 1992, followed by a general decline. The risk of death from PLB involves both patient and treatment factors. Survival analysis revealed that age, stage, laterality, chemotherapy, and primary site significantly influence both overall survival and disease-specific survival. We integrated and compared 99 machine learning algorithms, and identified the Random Survival Forest (RSF) model as the most effective for predicting PLB outcomes. Patients were stratified into low- and high-risk groups according to the RSF model score. The incidence of PLB began to decrease after 1992, with variations by age, race, and gender. The factors influencing the prognosis of PLB are multifaceted. And the RSF model showed promising performance, aiding clinicians in early prognosis identification and improving clinical outcomes through revised management strategies and patient care.</p>

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Epidemiology and survival of patients with primary lymphoma of bone: a large retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月24日

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  • Xiaojie Liang,
  • Weixiang Lu,
  • Tong Li,
  • Baiwei Luo,
  • Yuzhe Wu,
  • Chaoran Lin,
  • Yang Liu,
  • Liang Wang

摘要

Primary lymphoma of bone (PLB) is a rare extranodal lymphoma, and its epidemiology and prognosis remain controversial. We conducted a retrospective analysis of 1,222 patients with PLB in the Surveillance, Epidemiology, and End Results (SEER) database to investigate its epidemiology and prognostic factors. The incidence of PLB peaked in 1992 with an average annual percent change of 1.72 after a significant rise from 1975 to 1992, followed by a general decline. The risk of death from PLB involves both patient and treatment factors. Survival analysis revealed that age, stage, laterality, chemotherapy, and primary site significantly influence both overall survival and disease-specific survival. We integrated and compared 99 machine learning algorithms, and identified the Random Survival Forest (RSF) model as the most effective for predicting PLB outcomes. Patients were stratified into low- and high-risk groups according to the RSF model score. The incidence of PLB began to decrease after 1992, with variations by age, race, and gender. The factors influencing the prognosis of PLB are multifaceted. And the RSF model showed promising performance, aiding clinicians in early prognosis identification and improving clinical outcomes through revised management strategies and patient care.