Dataset for the reporting of renal biopsy for tumour: recommendations from the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
The International Collaboration on Cancer Reporting (ICCR) has developed a suite of detailed datasets for international implementation. These datasets are based on the reporting protocols developed by the Royal College of Pathologists (UK), The Royal College of Pathologists of Australasia and the College of American Pathologists, with modifications undertaken by international expert groups appointed according to ICCR protocols. The dataset for the reporting of renal biopsy for tumour is designed to provide a structured reporting template containing minimum data recording key elements suitable for international use. In formulating the dataset, the ICCR panel incorporated recommendations from the 2012 Vancouver Consensus Conference of the International Society of Urological Pathology (ISUP) and the 2016 edition of the WHO Bluebook on tumours of the urinary and male genital systems. Reporting elements were divided into Required (Core) and Recommended (Non-core) components of the report. Required elements are as follows: specimen laterality, histological tumour type, WHO/ISUP histological tumour grade, sarcomatoid morphology, rhabdoid morphology, necrosis, lymphovascular invasion and coexisting pathology in non-neoplastic kidney. Recommended reporting elements are as follows: operative procedure, tumour site(s), histological tumour subtype and details of ancillary studies. In particular, it is noted that fluorescence in situ hybridisation studies may assist in diagnosing translocation renal cell carcinoma (RCC) and in distinguishing oncocytoma and eosinophilic chromophobe RCC. It is anticipated that the implementation of this dataset into routine clinical practice will facilitate uniformity of pathology reporting worldwide. This, in turn, should have a positive impact on patient treatment and the quality of demographic information held by cancer registries.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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".