Eighth Edition Staging of Thoracic Malignancies: Implications for the Reporting Pathologist
Bibliographic record
Abstract
Context The Staging and Prognostic Factors Committee of the International Association for the Study of Lung Cancer, in conjunction with the International Mesothelioma Interest Group, the International Thymic Malignancy Interest Group, and the Worldwide Esophageal Cancer Collaboration, developed proposals for the 8th edition of their respective tumor, node, metastasis (TNM) staging classification systems. Objective To review these changes and discuss issues for the reporting pathologist. Data Sources Proposals were based on international databases of lung (N = 94 708), with an external validation using the US National Cancer Database; mesothelioma (N = 3519); thymic epithelial tumors (10 808); and epithelial cancers of the esophagus and esophagogastric junction (N = 22 654). Conclusions These proposals have been mostly accepted by the Union for International Cancer Control and the American Joint Committee on Cancer and incorporated into their respective staging manuals (2017). The Union for International Cancer Control recommended implementation beginning in January 2017; however, the American Joint Committee on Cancer has deferred deployment of the eighth TNM until January 1, 2018, to ensure appropriate infrastructure for data collection. This manuscript summarizes the updated staging of thoracic malignancies, specifically highlighting changes from the 7th edition that are relevant to pathologic staging. Histopathologists should become familiar with, and start to incorporate, the 8th edition staging in their daily reporting of thoracic cancers henceforth.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".