Abstract A9: Phenotype matters in the prediction of cancer risk of oral premalignant lesions (OPL)
Bibliographic record
Abstract
Abstract Histology remains the most reliable way for predicting cancer risk of premalignant (preinvasive) lesions if the OPLs show high-grade changes (i.e. severe dysplasia or carcinoma in situ, CIS): however, it is a poor predictor of the cancer risk of OPLs with no or low-grade (mild/moderate) dysplasia (termed LGOPL). It is possible that there are subtle histological differences between progressing LGOPLs and nonprogressing LGOPLs. In a recent retrospective study (Cancer Research 2008, 68:3099–107), we have shown that nuclear phenotypic score (NPS) as measured by a computer-driven microscope imaging system could serve as an adjunct tool to assist pathologists in judging the progression risk of LGOPLs Objective: to assess the potential of this new tool in identifying high-risk LGOPLs from an ongoing prospective study and to give an interim report of our results. Methods: 284 primary LGOPLs from 284 patients were studied: 47 hyperplasias, 116 mild and 121 moderate dysplasias. Thoinin-Feulgen stained sections were imaged and analyzed to generate a NPS for each sample. The NPS was correlated with histopathology, clinical variables and outcome (progression to severe dysplasia, CIS or invasive cancer). Results: Elevated NPS was significantly associated with progression: high NPS (≥ 4.5) was associated with a 4.7-fold increase in risk of progression as compared to low NPS (< 4.5). Of the 199 LGOPLs with low NPS, 13 (7%) progressed as compared to 26/85 (31%) of LGOPLs with high NPS (P < 0.0001). In the multivariate Cox model, high NPS was a significant risk predictor for cancer progression (P < 0.0001). Conclusions: These data support the potential utility of automated quantitative microscopy technology to assist the pathologist in assessing progressing potential of low-grade OPLs (Supported by grant R01DE13124, NIDCR). Citation Information: Cancer Prev Res 2011;4(10 Suppl):A9.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".