Inflammatory breast cancer defined: proposed common diagnostic criteria to guide treatment and research
Bibliographic record
Abstract
PURPOSE: Inflammatory breast cancer is a deadly and aggressive type of breast cancer. A key challenge relates to the need for a more detailed, formal, objective definition of IBC, the lack of which compromises clinical care, hampers the conduct of clinical trials, and hinders the search for IBC-specific biomarkers and treatments because of the heterogeneity of patients considered to have IBC. METHODS: Susan G. Komen, the Inflammatory Breast Cancer Research Foundation, and the Milburn Foundation convened patient advocates, clinicians, and researchers to review the state of IBC and to propose initiatives to advance the field. After literature review of the defining clinical, pathologic, and imaging characteristics of IBC, the experts developed a novel quantitative scoring system for diagnosis. RESULTS: The experts identified through consensus several "defining characteristics" of IBC, including factors related to timing of onset and specific symptoms. These reflect common pathophysiologic changes, sometimes detectable on biopsy in the form of dermal lymphovascular tumor emboli and often reflected in imaging findings. Based on the importance and extent of these characteristics, the experts developed a scoring scale that yields a continuous score from 0 to 48 and proposed cut-points for categorization that can be tested in subsequent validation studies. CONCLUSION: To move beyond subjective 'clinical diagnosis' of IBC, we propose a quantitative scoring system to define IBC, based on clinical, pathologic, and imaging features. This system is intended to predict outcome and biology, guide treatment decisions and inclusion in clinical trials, and increase diagnostic accuracy to aid basic research; future validation studies are necessary to evaluate its performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".