Towards a WHO classification of genetic tumour syndromes
Bibliographic record
Abstract
Almost 70 years ago, the World Health Organization (WHO) decided to propose a "Classification of Tumours". Since then, a systematic and extensive classification system for tumours has been continuously developed in successive editions and nowadays closely interlinks with coding systems for cancer registries like the International Classification of Diseases for Oncology (ICD-O). Whereas past editions had their focus on histopathological aspects of tumour classification in different organ systems and topologies, to which (somatic) genetic alterations increasingly contributed, the current fifth edition of the WHO Classification for the first time includes a separate "Blue Book" volume on "Genetic Tumour Syndromes". Along with chapters dedicated to tumour predisposition inferred by constitutional (germline) genetic pathogenic variants in the different organ-specific volumes of the classification, this new addition to the WHO classification highlights the increasing importance of constitutional genetic alterations for the diagnosis and clinical management of patients with such tumours. The WHO classification of Genetic Tumour Syndromes applies a hierarchical system based on four levels: the major (cellular) mechanism affected, the molecular pathway involved, the (clinical) syndrome, and the specific gene(s) affected. It provides - in part novel or modified - names to the genetic tumour syndromes as well as definitions and descriptions of clinical, epidemiologic, etiologic, pathogenetic and pathological aspects. Essential and desirable diagnostic criteria are given as well as rules for reporting, thus paving the way to international standardization. While the final version of the WHO Classification of Genetic Tumour Syndromes is in proof-stage, the present article, which is based on its beta-version, aims to provide an overview of the concepts underpinning the classification.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".