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Purpose <p>This 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.</p> Methods <p>Medical 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.</p> Results <p>Among 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.</p> Conclusion <p>Although 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.</p>

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Assessing the accuracy of inflammatory breast cancer self-reported diagnoses through the metastatic breast cancer project from the count me in initiative database

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Pietro De Placido,
  • Elizabeth Troll,
  • Samuel M. Niman,
  • Sean Ryan,
  • Ginny Mason,
  • Mariesa Powell,
  • Aditi Hazra,
  • Nikhil Wagle,
  • Mary McGillicuddy,
  • Nancy U. Lin,
  • Sara M. Tolaney,
  • Faina Nakhlis,
  • Meredith M. Regan,
  • Filipa Lynce

摘要

Purpose

This 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.

Methods

Medical 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.

Results

Among 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.

Conclusion

Although 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.