Multivariate regression analysis to predict progression of oral premalignant lesions
Bibliographic record
Abstract
B138 Survival rates for oral cancer patients have remained unchanged in the past several decades largely because of the late identification of the disease and the high rate of local recurrence after treatment. We present research based upon an approach that uses automated quantitative microscopy technology in conjunction with molecular approaches to create an integrated plan for patient assessment and management that would be rapid and cost-effective. > Objective : The aim of this study was to assess the potential of Quantitative Tissue Phenotype (QTP) and its correlation with conventional histopathology, allelic losses for prediction of cancer development from OPLs. > Methods : A total of 138 oral mucosa lesions were analyzed. The distribution of the pathology grades was as follow: normal- 30, hyperplasia -21, mild dysplasia -13, moderate dysplasia-10, sever dysplasia/CIS- 35, and invasive Squamous Cell Cancer -29. Thoinin-Feulgen stained sections, adjacent to the HE n = 34, MR3 ). The time course data for these patients has been stratified into those which develop cancer (29 lesions) and those which have not developed cancer after at least 10 years of follow-up (15 lesions) > Results: We investigated the potential of Quantitative Tissue Phenotype (as measured by the NPS), to recognize severe dysplasia/carcinoma in situ ( CIS ) (known to have an increased risk of transformation into invasive cancer) and to predict progression of hyperplasia/mild/moderate dysplasia (termed HMD). Using the NPS in this pilot data we can correctly identify 94% of the high-grade OPLs while maintaining a specificity of 74%. The NPS can be used in this pilot study to identify patients which progressed to cancer 77% of the time while correctly identifying the patient which do not progress 78% of the time. There was significant correlation between lesions with High NPS and lesion with genetic damage. From a Cox model the most predictive factors for cancer risk are LOH (p =0.0008) and NPS (p = 0.0007). In the multivariate Cox model, LOH (p =0.01) and NPS (p = 0.07) are the strongest predictors for cancer development. All analyses showed that pathology was not a predictor for HMD progression to cancer. > Conclusion s: These data support the potential utility of Quantitative Tissue Phenotype as a marker associated with molecular damage and with cancer development. QTP could be used to identify lesions that require molecular evaluation.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".