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Clinical features and prognostic factors of pulmonary carcinosarcoma: A nomogram development and validation based on
Ming-Yi Zhang1,2, Lian-Sha Tang2, Zhao-Juan Qin1
1Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Background:
Pulmonary carcinosarcoma (PCS) is a rare but aggressive malignant disease in the lung. It is characterized by coexisting histologic elements of carcinomatous and sarcomatous components. This study aimed to comprehensively understand the clinical features of PCS and develop a nomogram for prognostic prediction of PCS patients.
Methods:
Data were collected from the Surveillance Epidemiology and End Results (SEER) database between 1975 and 2018. Propensity-score matching (PSM) was used to match the demographic characteristic of the PCS vs. pulmonary sarcoma (PS). Cancer-specific survival (CSS) and overall survival (OS) were the main endpoints of the survival of patients and were evaluated using the Kaplan Meier curves and Cox proportional hazards regression. We further randomly split enrolled PCS patients from SEER into the training and validation sets. All independent predictors for OS of the training set were integrated to create a predictive nomogram. The performance of the nomogram was determined by discrimination, calibration ability, clinical usefulness, and risk stratification ability both in the training and validation cohorts. In addition, the clinical data of PCS patients from the West China Hospital were also retrospectively analyzed by this model.
Results:
A total of 428 PCS patients and 249 PS patients were enrolled from SEER. Compared to pure PS, PCS was associated with significantly better survival in the unmatched cohorts, whereas non-significantly better survival after PSM. In subgroup analysis, PCS showed significantly worse survival than pure PS in subgroups among the race, marital status, and radiation treatment. A nomogram was constructed for PCS patients' survival prediction by combining the independent risk factors, including gender, stage, surgery, radiation, and chemotherapy. The nomogram showed good discrimination, calibration, and predictive power in the training and validation sets. Risk stratification analysis indicated that the nomogram scores efficiently divided PCS patients into low and high-risk groups.
Conclusion:
PCS is a rare malignant disease of the lung with distinct clinical features. It had a comparable survival compared with pure PS in the matched cohorts. In addition, a nomogram was developed and validated for predicting the OS in PCS patients.
