Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms
Bibliographic record
Abstract
PURPOSE: A major challenge for the early diagnosis of oral cancer is the ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive squamous cell carcinoma (SCC) from those at low risk. Our group has previously used high-resolution image analysis algorithms to quantify the nuclear phenotypic changes occurring in OPLs. This approach, however, requires a manual selection of nuclei images. Here, we investigated a new, semi-automated algorithm to identify OPLs at high risk of progressing into invasive SCC from those at low risk using Random Forests, a tree-based ensemble classifier. METHODS: We trained a sequence of classifiers using morphometric data calculated on nuclei from 29 normal, 5 carcinoma in situ (CIS) and 28 SCC specimens. After automated discrimination of nuclei from other objects (i.e., debris, clusters, etc.), a nuclei classifier was trained to discriminate abnormal nuclei (8,841) from normal nuclei (5,762). We extracted voting scores from this trained classifier and created an automated nuclear phenotypic score (aNPS) to identify OPLs at high risk of progression. RESULTS: The new algorithm showed a correct classification rate of 80% (80.6% sensitivity, 79.3% specificity) at the cellular level for the test set, and a correct classification rate of 75% (77.8% sensitivity, 71.4% specificity) at the tissue level with a negative predictive value of 76% and a positive predictive value of 74% for predicting progression among 71 OPLs, performed on par with the manual method in our previous study. CONCLUSIONS: We conclude that the newly developed aNPS algorithm serves as a crucial asset in the implementation of high-resolution image analysis in routine clinical pathology practice to identify lesions that require molecular evaluation or more frequent follow-up.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".