Feng Z, Li Y. Web-based nomograms for predicting overall survival and cancer-specific survival in retroperitoneal leiomyosarcoma: a population-based analysis.
J Cancer Res Clin Oncol 2023;
149:11735-11748. [PMID:
37405479 DOI:
10.1007/s00432-023-05052-y]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 06/28/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND
Retroperitoneal leiomyosarcoma is a type of carcinoma with low incidence and poor prognosis, and prognostic factors are currently unknown. Therefore, our study aimed to investigate the predictive factors of RPLMS and establish prognostic nomograms.
METHODS
Patients diagnosed with RPLMS between 2004 and 2017 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified by univariate and multivariate COX regression analyses and used to generate nomograms to predict overall survival (OS) and cancer-specific survival (CSS).
RESULTS
646 eligible patients were randomly divided into training set (n = 323) and validation set (n = 323). Multivariate COX regression analysis indicated that the independent risk factors for OS and CSS were age, tumor size, grade, SEER stage, and surgery. In the nomogram of OS, the concordance indices (C-index) of the training and validation sets were 0.72 and 0.691, and in the nomogram of CSS, the C-indices of the training and validation sets were 0.737 and 0.737. Furthermore, calibration plots showed that the predicted results of the nomograms in the training and validation sets agree well with the actual observations.
CONCLUSION
Age, tumor size, grade, SEER stage, and surgery were independent prognostic factors for RPLMS. The nomograms developed and validated in this study can accurately predict the OS and CSS of patients, which could help clinicians make individualized survival predictions. Finally, we make the two nomograms into two web calculators for the convenience of clinicians.
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