ArticleBreast cancer research and treatment2025
Assessing the accuracy of inflammatory breast cancer self-reported diagnoses through the metastatic breast cancer project from the count me in initiative database.
Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis study assessed the accuracy of self-reported inflammatory breast cancer (IBC) diagnoses within the Count Me In (CMI) Metastatic Breast Cancer Project. Given IBC's aggressive nature and diagnostic complexity, we aimed to evaluate the reliability of patient-reported data by quantifying concordance rates between self-reported and clinically confirmed diagnoses.
methodsMedical records from 79 patients who self-identified as having IBC were reviewed to confirm the diagnosis through provider documentation. When explicit confirmation was absent, a recently validated quantitative IBC scoring system was applied. Each patient's diagnosis was adjudicated by an expert physician, with cases classified as concordant or discordant based on predefined criteria. A concordance threshold of 90% was established to consider patient-reported diagnoses as sufficiently reliable.
resultsAmong the 79 patients, 57/79 (72.2%) had concordant diagnoses based on either explicit documentation or scoring system verification. Specifically, 51/79 (64.6%) had explicit documentation, while an additional 6/79 (7.6%) met scoring system criteria. However, 22/79 (27.8%) were discordant, either lacking evidence or unable to be confirmed due to incomplete medical records, falling below the 90% concordance threshold required for reliability.
conclusionAlthough patient self-reporting via the CMI initiative allows rapid data collection, reliance solely on self-identification for diagnosing IBC may lead to misclassification. Future strategies should incorporate refined symptom-specific screening and prioritize enrolling patients with stage III IBC. Advanced technologies such as AI-assisted medical record analysis could further enhance diagnostic accuracy and facilitate high-quality data collection for improved diagnosis and outcomes.
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