An international unified approach to reporting and grading invasive breast cancer. An overview of the International Collaboration on Cancer Reporting (<scp>ICCR</scp>) initiative
Bibliographic record
Abstract
Standardised reporting of breast cancer key pathology data has become the norm in some parts of the world, but are based on national or regional guidelines that differ in certain aspects, resulting in divergent reporting practices and a lack of comparability of data internationally. The International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter-)national pathology and cancer organizations, have recently produced a new international dataset for the pathology reporting of breast cancer, including resection specimens with invasive cancer and ductal carcinoma in situ (DCIS) of the breast. This initiative aims at providing an international unified approach to reporting cancer. The guidance was prepared by an international expert panel consisting of experienced breast pathologists, a surgeon, and an oncologist. The dataset includes core (essential) and noncore (optional) data items based on a critical review and discussion of current evidence. Commentary is provided for each data item to explain the rationale for selection, its clinical relevance, and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. The process concludes with international public consultation, before ratification and publication on the free open access ICCR website, with a synoptic reporting guide. The key aim is to promote high-quality, standardised pathology reporting that can be used worldwide. Histological grade, tumour size, and oestrogen receptor status are used in this article to illustrate this process and the detail provided to support its inclusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".