Related Experiment Video
Updated: Jun 25, 2025
A Syngeneic Mouse B-Cell Lymphoma Model for Pre-Clinical Evaluation of CD19 CAR T Cells
Published on: October 16, 2018
Suicide among lymphoma patients.
Jie Zhou1, Mengjie Tian2, Xiangchen Zhang1
1Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China.
This study developed a nomogram to identify lymphoma patients at high risk of suicide. The model aids in personalized interventions to reduce suicide risk in this vulnerable population.
Area of Science:
- Oncology
- Psychiatry
- Biostatistics
Background:
- Lymphoma patients exhibit elevated suicide rates.
- Effective interventions are needed to mitigate suicide risk in this demographic.
Purpose of the Study:
- To construct a predictive nomogram for identifying high-risk lymphoma patients for suicide.
- To aid in the development of targeted interventions for suicide prevention in lymphoma patients.
Main Methods:
- Utilized the Surveillance, Epidemiology, and End Results (SEER) database (2000-2020) with 235,806 lymphoma patients.
- Employed Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression to identify suicide predictors.
- Validated the nomogram using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Main Results:
- Key predictors identified: age at diagnosis, gender, ethnicity, marital status, stage, surgery, radiotherapy, and annual income.
- The nomogram demonstrated good predictive accuracy with a c-index of 0.773 (training) and 0.777 (validation).
- Calibration curves and DCA confirmed the nomogram's clinical utility and reliability.
Conclusions:
- A reliable nomogram was developed to individualize and quantify suicide risk in lymphoma patients.
- This tool can support clinical decision-making for personalized suicide prevention strategies.
- Further research may explore the impact of unmeasured confounders in retrospective analyses.
