ArticleCancer medicine2023
A novel clinical tool to predict cancer-specific survival in patients with primary pelvic sarcomas: A large population-based retrospective cohort study.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.Scientific reports · 2024Article
- Development and validation of a machine learning-based model to predict postoperative overall survival in patients with soft tissue sarcoma: a retrospective cohort study.American journal of cancer research · 2024Article
- A novel clinical tool to predict cancer-specific survival in patients with primary pelvic sarcomas: A large population-based retrospective cohort study.Cancer medicine · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrimary osseous sarcoma of the pelvis is rare and has a particularly sinister outcome. This study aims to identify independent prognostic factors of cancer-specific survival (CSS) in patients with primary pelvic sarcoma (PS) and develop a nomogram to predict 3-, 5-, and 10-year probability of CSS in these patients.
methodsThe Surveillance, Epidemiology, and End Results (SEER) database was used to identify 416 patients with primary PS, who were divided into two groups: a training cohort and a validation cohort. Univariate and multivariate Cox analyses were used to screen independent prognostic factors in patients with primary PS. Based on these independent prognostic factors, a prognostic nomogram was developed to predict 3-, 5-, and 10-year probability of CSS. The nomogram's predictive performance and clinical value were evaluated using the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was developed.
resultsTumor size, tumor stage, histological type, surgery, and chemotherapy were identified as independent prognostic factors for the CSS of primary PS patients. Based on these factors, a nomogram was created to predict the 3-, 5-, and 10-year probability of CSS in these patients. The calibration curve, ROC, and DCA indicated that the nomogram performed well and was appropriate for clinical use, with 3-, 5-, and 10-year areas under ROC curve all higher than 0.800. Furthermore, the nomogram-based mortality risk stratification system could effectively divide these patients into three risk subgroups.
conclusionsThe nomogram constructed in this study could accurately predict 3-, 5-, and 10-year probability of CSS in patients with primary PS. Clinicians can use the nomogram to categorize these patients into risk subgroups and provide personalized treatment plans.
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