Development and validation of a prognostic nomogram for patients with malignant peritoneal mesothelioma
Frontiers in Oncology 2025 February 28 [Link]
Xiaohan Wang, RuiTing Liu, Chunli Wang, Jingjing Sun, Dongliang Yang
Abstract
Background: Malignant peritoneal mesothelioma(MPM) is a highly aggressive malignant tumor that originates from peritoneal mesothelial cells. Due to the rarity of MPM, there are few survival prediction models specifically for visualization of malignant peritoneal mesothelioma.
Objective: This study aimed to develop a nomogram for the overall survival of MPM based on the Surveillance, Epidemiology, and End Results (SEER) database and the data of Cangzhou People’s Hospital were used for external verification.
Methods: Patients screened from the SEER database were divided into a training group and an internal verification group in a 7:3 ratio, with data from Cangzhou People’s Hospital used as the external verification group. Cox proportional hazard regression was utilized to identify significant factors, and nomograms for 6-month, 12-month, and 18-month overall survival were developed. The performance of the nomogram was assessed using consistency index, calibration curve, and K-M curve.
Results: Age, sex, histology, surgery, tumor size, chemotherapy, differentiated and the number of organ metastases were significant risk factors (p<0.05) and were included in the nomogram.The area under the subject worker curve at 6,12,18 months overall survival (AUC) was 0.782,0.784,0.766 for the training group, 0.804,0.791,0.796 for the internal verification group, 0.767,0.749,0.783 for the external verification group. The predicted correction curve was in good agreement with the observed results. The Kaplan-Meier curves of different risk groups showed significant differences.
Conclusion: This study represents the first visual prognostic model of MPM and the initial incorporation of organ metastasis into MPM prognostic factors. The nomograph serves as a reliable tool for clinicians to personalize overall survival prediction and maximize patient benefits by identifying the most effective treatment.
