Molecular classification of soft tissue sarcomas for adequate diagnosis: A study on the northeast population of Morocco
Bibliographic record
Abstract
Background: Soft tissue sarcomas (STS) are a heterogeneous group of tumors. For adequate therapeutic management, an accurate diagnosis is necessary. In Morocco, the diagnosis is essentially based on the morphological and immunohistochemical study. Compared to other techniques, fluorescence in situ hybridization (FISH) is easier to develop and less expensive. This study aims to assess the feasibility and utility of implementing FISH technique to improve diagnostic accuracy and establish a good classification. Material and methods: This is a retrospective cohort study. 211 cases of mesenchymal tumors were included. Hematoxylin Eosin Safran (HES) staining was performed in all cases followed by immunohistochemistry (IHC). FISH was performed in all cases with suspected STS. The probes used were EWSR1, MDM2 and SS18. The performance of FISH and histopathological test were evaluated by the ROC curve method (receiver operating characteristic). We evaluated the concordance between FISH and real time PCR by Cohen test. Results: The real-time PCR technique showed good agreement with the FISH test by a Kappa coefficient of 60% (p = 0.035). FISH was able to confirm that it is more accurate (Youden's Index = 91%) than histological/immunohistochemical analysis (Youden's Index = 51%), as well as the positive predictive value was higher (100%) with an ROC curve finding a larger area under the curve of 0.953 (95% CI: 0.918-0.988), p = 0.000 which supports that FISH shows high performance to present an accurate final diagnosis. Conclusion: This is the first and the largest Moroccan series for the molecular diagnosis of STS by FISH. Our study shows that paraffin FISH is a sensitive and specific ancillary tool in the diagnosis of STS when used in the appropriate clinicopathological context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".