Integration of Optical Genome Mapping in the Cytogenomic and Molecular Work‐Up of Hematological Malignancies: Expert Recommendations From the International Consortium for Optical Genome Mapping
Bibliographic record
Abstract
The latest updates to the classification of hematolymphoid malignancies using the World Health Organization (WHO, 5th ed.) and ICC (International Consensus Classification) criteria highlight the critical need for comprehensive and precise cytogenomic data for diagnosis, prognostication, and treatment. This presents significant challenges for clinical laboratories, requiring a complex workflow using multiple assays to detect different types of structural chromosomal variants (copy number changes, fusions, inversions) across the entire genome. Optical genome mapping (OGM) is an advanced cytogenomic tool for genome-wide detection of structural chromosomal alterations at the gene/exon level. Studies demonstrate that OGM facilitates the identification of novel cytogenomic biomarkers, improves risk stratification, and expands therapeutic targets and personalized treatment strategies. OGM is easy to implement and highly accurate in detecting structural variants (SVs) across various diagnostic entities. Consequently, many centers are integrating OGM into the clinical cytogenetic workflow for hematological malignancies. However, systemic clinical adoption has remained limited due to the lack of expert recommendations on clinical indications, testing algorithms, and result interpretation. To address this, experts from the International Consortium for OGM and relevant multidisciplinary fields developed recommendations for the integration of OGM as a standard-of-care cytogenetic assay for the diagnostic workflow in various clinical settings. These recommendations standardize the use of OGM across laboratories, ensure high-quality cytogenetic data, guide clinical trial design and development, and provide a basis for updates to diagnostic and classification models.
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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 | 0.071 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".