Complementary value of molecular analysis to expert review in refining classification of uncommon soft tissue tumors
Bibliographic record
Abstract
The classification of many soft tissue tumors remains subjective due their rarity, significant overlap in microscopic features and often a non-specific immunohistochemical (IHC) profile. The application of molecular genetic tools, which leverage the underlying molecular pathogenesis of these neoplasms, have considerably improved the diagnostic abilities of pathologists and refined classification based on objective molecular markers. In this study, we describe the results of an international collaboration conducted over a 3-year period, assessing the added diagnostic value of applying molecular genetics to sarcoma expert pathologic review in a selected series of 84 uncommon, mostly unclassifiable mesenchymal tumors, 74 of which originated in soft tissues and 10 in bone. The case mix (71% historical, 29% contemporary) included mostly unusual and challenging soft tissue tumors, which remained unclassified even with the benefit of expert review and routine ancillary methods, including broad IHC panels and a limited number of commercially available fluorescence in situ hybridization (FISH) probes. All cases were further tested by FISH using a wide range of custom bacterial artificial chromosome probes covering most of known fusions in sarcomas, whereas targeted RNA sequencing was performed in 13 cases negative by FISH, for potential discovery of novel fusion genes. Tumor-defining molecular alterations were found in 48/84 tumors (57%). In 27 (32%) cases the tumor diagnosis was refined or revised by the additional molecular work-up, including five cases (6%), in which the updated diagnosis had clinical implications. Sarcoma classification is rapidly evolving due to an increased molecular characterization of these neoplasms, so unsurprisingly 17% of the tumors in this series harbored abnormalities only very recently described as defining novel molecularly defined soft tissue tumor subsets.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".