Trial reportFrontiers in endocrinology2022
A web-based predictive model for overall survival of patients with cutaneous Merkel cell carcinoma: A population-based study.
Trial report in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Merkel cell carcinoma (MCC) is an aggressive neuroendocrine carcinoma with a high mortality rate, so it is necessary to create models to predict overall survival of MCC. We developed an easy-to-use web-based calculator to predict the OS of MCC patients based on the nomogram. Methods: MCC patients between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to training and validation cohorts. Patients between 2016-2017 serve as the external validation cohort. Relevant risk factors were identified by univariate and multivariate COX hazards regression methods and combined to produce nomograms. The concordance index (C-index), area under the receiver operating characteristic (AUC) curve, and calibration plots have demonstrated the predictive power of the nomograms. Decision curve analysis (DCA) was used to measure nomograms in clinical practice. Patients were divided into three groups according to the scores of the nomogram. Results: A total of 3480 patients were randomly assigned to the training group and validation group in this study. Meaningful prognostic factors were applied to the establishment of nomograms. The C-index for OS was 0.725 (95% CI: 0.706-0.741) in the training cohort and 0.710 (95% CI: 0.683-0.737) in the validation cohort. In the external validation cohort, C-index was 0.763 (95% CI: 0.734-0.792). The C-index of training cohort, validation cohort and external validation cohort for CSS were 0.743 (95% CI:0.725-0.761), 0.739(95%CI:0.712-0.766) and 0.774 (95%CI:0.735-0.813), respectively. The AUC and calibration plots of 1-, 3-, and 5-year OS rates showed that the nomogram had good predictive power. DCA demonstrated that the nomogram constructed in this study could provide a clinical net benefit. Our calculator demonstrated excellent predictive capabilities for better risk grouping of MCC patients. Conclusion: We created novel nomograms of prognostic factors for MCC, which more accurately and comprehensively predicted 1-, 3-, and 5-year OS/CSS in MCC patients. We established a calculator which can easily and quickly calculate the risk grouping of MCC patients by inputting clinically relevant characteristics. This can help clinicians identify high-risk patients as early as possible, carry out personalized treatment, follow-up, and monitoring, and improve the survival rate of MCC patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.