Data set for the reporting of lung cancer: recommendations from the International Collaboration on Cancer Reporting (<scp>ICCR</scp>)
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer related deaths worldwide, although some patients with early-stage disease can be cured with surgical resection. Standardised reporting of all clinically relevant pathological parameters is essential for best patient care and is also important for ongoing data collection and refinement of important pathological features that impact patient prognosis, staging and clinical care. Using the established International Collaboration on Cancer Reporting (ICCR) procedure, a representative international expert panel of nine lung pathologists as well as an oncologist was convened. Essential core elements and suggested non-core elements were identified for inclusion in the resected lung cancer pathology data set based on predetermined levels of evidence as well as consensus expert opinion. A lung cancer histopathology reporting guide was developed that includes relevant clinical, macroscopic, microscopic and ancillary testing. Critical review and discussion of current evidence was incorporated into the new data set including changes from the 2021 World Health Organisation (WHO) Classification of Thoracic Tumours, fifth edition, new requirements for grading invasive non-mucinous adenocarcinomas, assessment of response to neoadjuvant therapy and requirements for molecular testing in early-stage resected lung carcinomas. This ICCR data set represents incorporation of all relevant parameters for histology reporting of lung cancer resection specimens. Routine use of this data set is recommended for all pathology reporting of resected lung cancer and it is freely available worldwide on the ICCR website (https://www.iccr-cancer.org/datasets/published-datasets/). Widespread implementation will help to ensure consistent and comprehensive pathology reporting and data collection essential for lung cancer patient care, clinical trials and other research.
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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 | 0.002 | 0.005 |
| 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".