Development and validation of prognostic nomogram for malignant pleural mesothelioma.
10.3760/cma.j.cn12152-20211124-00871
- Author:
Xiao Jie XIE
1
;
Jian You CHEN
2
;
Jie JIANG
1
;
Hui DUAN
1
;
Yi WU
3
;
Xing Wen ZHANG
3
;
Shen Jie YANG
4
;
Wen ZHAO
1
;
Sha Sha SHEN
1
;
Li WU
1
;
Bo HE
1
;
Ying Ying DING
2
;
Heng LUO
5
;
Si Yun LIU
6
;
Dan HAN
1
Author Information
1. Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China.
2. Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming 650106, China.
3. Department of Radiology, Chuxiong People's Hospital, Chuxiong 675099, China.
4. Department of Thoracic Surgery, Chuxiong People's Hospital, Chuxiong 675099, China.
5. Deputy President's Office, Chuxiong People's Hospital, Chuxiong 675099, China.
6. GE Healthcare (China), Beijing 100176, China.
- Publication Type:Journal Article
- Keywords:
Malignant pleural mesothelioma;
Nomogram prediction model;
Prognosis;
Risk stratification
- MeSH:
Humans;
Mesothelioma, Malignant;
Prognosis;
Nomograms;
Retrospective Studies;
Proportional Hazards Models
- From:
Chinese Journal of Oncology
2023;45(5):415-423
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.
