Dataset for pathology reporting of ductal carcinoma <i>in situ</i>, variants of lobular carcinoma <i>in situ</i> and low‐grade lesions: recommendations from the International Collaboration on Cancer Reporting (<scp>ICCR</scp>)
Bibliographic record
Abstract
AIMS: To describe a new international dataset for pathology reporting of ductal carcinoma in situ (DCIS), variants of lobular carcinoma in situ (LCIS) and low-grade lesions (encapsulated papillary carcinoma, solid papillary carcinoma in situ, Paget's disease) produced by the International Collaboration on Cancer Reporting (ICCR). METHODS AND RESULTS: The ICCR, a global alliance of pathology bodies, uses a rigorous and efficient process for the development of evidence-based, structured datasets for pathology reporting of common cancers. Their aim is to support quality pathology reporting and engender understanding between the breast surgeon, pathologist, and oncologist for optimal and uniform patient management globally. Here we describe the dataset for DCIS, some variants of LCIS (namely the pleomorphic and the florid variants), and low-grade lesions by a multidisciplinary panel of internationally recognized experts. The agreed dataset comprises 12 core (required) and five noncore (recommended) elements suitable for both developed and low-income jurisdictions, derived from a review of current evidence. Areas of contention were addressed using a pragmatic approach in the absence of evidence. Use of all core elements is the minimum reporting standard for any individual case. Commentary is provided, explaining each element's clinical relevance, definitions to be applied where appropriate for the agreed list of value options and the rationale for considering the element as core or noncore. CONCLUSION: This first internationally agreed dataset for DCIS, variants of LCIS, and low-grade lesions reporting will enable their standardization of pathology reporting and enhance clinicopathological communication leading to improved patient outcomes. Widespread adoption will also facilitate international comparisons, multinational clinical trials, and help to improve the management of breast disease globally.
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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 | 0.054 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".