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Updated: May 31, 2025

Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment
Published on: April 22, 2019
Incidence trends and prognostic factors in head and neck MALT lymphoma
Yaobin Lin1, Shan Liu2, Jianzhen Shen3
1Department of Radiation Oncology, Fujian Medical University Union Hospital, Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematological and Breast Malignancies), Fuzhou, Fujian, China.
Abstract:
Primary head and neck mucosa-associated lymphoid tissue lymphoma (HN-MALT) is a rare lymphoma with unknown incidence and prognosis. We allocated HN-MALT data from the Self-Surveillance, Epidemiology, and End Results database (2000-2021) into training and validation cohorts at a 7:3 ratio. A joinpoint regression analysis was used to examine sex-specific and age-group morbidities, and independent prognostic factors were identified through multivariate Cox analysis to construct a nomogram prediction model and verify the accuracy of prediction. A total of 2,517 patients with HN-MALT were randomly divided into training (1,762) and validation (755) groups. The incidence of HN-MALT in men and women decreased from 2007 to 2021, with an annual percent change (APC) of -3.04% (95%: -4.4 - -1.6, P < 0.05), while in the ≥ 60 years old group, the incidence decreased significantly from 2016 to 2021, with an APC of -8.20 (95%: -13.9 - -2.1, P < 0.05). The multivariate Cox analysis revealed that male, ≥ 60 years old, white, and divorced/separated/widowed were independent risk factors of the overall survival and lymphoma-specific survival. A nomogram prediction model was constructed based on the Cox regression results for multiple factors, the areas under the curve, the calibration curve, and decision curve analysis results of which indicated that the nomogram prediction model performed very well. HN-MALT cells exhibit distinctive clinical and pathological characteristics. Furthermore, we developed a nomogram model for the prognostic assessment to offer valuable guidance for clinical decision-making.
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