Re-classification of archival Ovarian Carcinoma diagnostics using immunohistologic digital quantification and algorithmic prognosis
Bibliographic record
Abstract
Abstract Twenty years of research improved the classification of ovarian carcinoma, making the diagnostic relevant from a scientific and clinical perspective. Our research question was to find out if old studies are still pertinent under new diagnostic criteria and how we can use machine learning techniques for re-classification purposes. The same main investigator re-classified 60 cases of ovarian carcinoma after 15 years, using 2014 WHO diagnostic criteria. Selected pathology data only (macro, micro information and immunohistochemistry images coming from a seven-stain panel) were provided for digital analysis. Biomarker images were digitalized and quantified using open source software and a validated methodology. 1080 attributes were classified using a random forest (open source) algorithm, using a supervised learning technique (the training dataset used 180 attributes). Human results were considered “ground truth” for the digital analysis. The human analysis maintained the initial histopathologic diagnostic in 61.5% of cases. The digital prediction shows 80% accuracy and 73% precision when compared with human reclassified data. Based on results, we concluded that “recycling” of old studies is possible. Limitation of the study are the low number of cases analyzed, the total absence of clinical, treatment and prognostic data and a possible human criteria selection bias. Even if technical difficulties related to biomarker selection and histological analysis exist, digital investigation of existing, large archival registries is feasible, reliable and it can be done at a low cost.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".