ArticleJournal of the National Cancer Institute2025
Development of a multi-institutional dataset to validate a novel inflammatory breast cancer diagnostic score.
Article in Journal of the National Cancer Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Survival and Treatment Patterns in Stage III Inflammatory and Noninflammatory Breast Cancer.JAMA network open · 2026Article
- Molecular and socioeconomic characteristics of inflammatory breast cancer in the Carolina Breast Cancer Study.Breast cancer research and treatment · 2026Article
- Assessing the accuracy of inflammatory breast cancer self-reported diagnoses through the metastatic breast cancer project from the count me in initiative database.Breast cancer research and treatment · 2025Article
- Inflammatory breast cancer, best practice in the community setting.NPJ breast cancer · 2025Review
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Authors and funding
21 authors.
Funding
Abstract
backgroundSusan G. Komen, the Inflammatory Breast Cancer (IBC) Research Foundation, and the Milburn Foundation convened patient advocates, clinicians, and researchers to propose novel quantitative scoring rubrics for IBC diagnosis. In this study, we developed a multi-institutional clinical dataset to test and validate the proposed scoring system.
methodsIBC (N = 988) and non-IBC (N = 332) cases were identified at 2 institutions with dedicated multidisciplinary IBC programs. The non-IBC cohort included consecutive cT4b and cT4c patients. Standard operating procedures (SOPs) were developed for all ambiguous findings and languages. Three different methods were used for the imputation of missing data, resulting in 3 separate datasets. The sensitivity, specificity, and area under the receiver operator characteristic curve (AUC-ROC) were used to assess the discrimination of the proposed scoring rubric.
resultsThe distribution of "true IBC" cases was 19.7% very likely IBC, 49.1% strong possibility of IBC, 0.4% weak possibility of IBC, 0.1% very unlikely IBC, and 30.7% unknown; corresponding groupings for true non-IBC cases were 0.6% very likely IBC, 51.8% strong possibility of IBC, 9.9% weak possibility of IBC, 2.1% very unlikely IBC, and 35.5% unknown. The AUC-ROC values for missing data imputation methods were similar (0.83-0.84); exploratory score refinement improved the AUC-ROC to 0.88-0.89.
conclusionUsing the largest multi-institutional IBC clinical database to date, the score has been validated and is available for clinical use at https://www.komen.org/ibc-calc to assist health-care providers and their patients in IBC diagnosis. Exploratory score refinement demonstrates the potential to increase specificity; however, any change requires separate validation.
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