ArticleDiscover oncology2024
A network dynamic nomogram for predicting overall survival and cancer-specific survival in patients with breast cancer liver metastases: an analysis based on the SEER database.
Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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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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.
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Who cites it
2 citing papers in PubMed.
- Article
- Development and validation of a machine learning model to evaluate survival in patients with newly diagnosed breast cancer with liver metastasis.Frontiers in oncology · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The liver stands out as one of the most frequent sites for distant metastasis in breast cancer cases. However, effective risk stratification tools for patients with breast cancer liver metastases (BCLM) are still lacking. We identified BCLM patients from the SEER database spanning from 2010 to 2016. After meticulously filtering out cases with incomplete data, a total of 3179 patients were enrolled and randomly divided into training and validation cohorts at a ratio of 2:1. Leveraging comprehensive patient data, we constructed a nomogram through rigorous evaluation of a Cox regression model. Validation of the nomogram was conducted using a range of statistical measures, including the concordance index (C-index), calibration curves, time-dependent receiver operating characteristic curves, and decision curve analysis (DCA). Both univariable and multivariable analyses revealed significant associations between OS and CSS in BCLM patients and 14 variables, including age, race, and tumor stage, among others. Utilizing these pertinent variables, we formulated nomograms for OS and CSS prediction. Subsequent validation involved rigorous assessment using time-dependent ROC curves, decision curve analysis, C-index evaluations, and calibration curves. Our web-based dynamic nomogram represents a valuable tool for efficiently analyzing the clinical profiles of BCLM patients, thereby aiding in informed clinical decision-making processes.
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