Nodal disease burden of oral cancer in British Columbia and a novel approach for risk assessment
Bibliographic record
Abstract
For patients of oral squamous cell carcinoma (OSCC), tumour spread to regional lymph nodes reduces survival by half. On the account of this widely demonstrated fact, prophylactic neck treatment has been advocated for clinically node negative (cN0) necks of which the risk of nodal disease is considerably high. However, there is a lack of sensitive and specific marker to determine such risk and benefits of prophylactic treatment await confirmation. The first part of this thesis presents a population-based retrospective review on OSCC in British Columbia. The incidence of regional failure (RF) in early-stage, cN0 patients was 28%, with median time of only 10 months after local excision. This group of patients needed to be identified and treated at earliest time possible. Tumour depth of invasion (DOI) was significantly associated with RF (P=0.01). However, it has low accuracy in predicting nodal disease with AUC of 63%. Moreover, assessment of performance for 4mm cut-off of DOI showed 55% sensitivity and 68% specificity. Furthermore, we demonstrated that using DOI as an indicator of neck treatment resulted in 25% under-treated occult metastasis and 55% over-treated necks. Thus, we concluded that, at least for BC population, conventional histological attributes of tumour cannot predict RF and we need a new marker for risk assessment. The second part presents a pilot study exploring a novel approach of risk assessment by utilizing Quantitative Tissue Pathology (QTP) on tumour nests. We were able to quantitate and evaluate 120 features describing nuclear phenotypes of tumour cell nuclei and tissue architectures of tumour nests. Compared to node-negative (N0) group, cell nuclei of the node positive (N+) group had higher fractions of heterochromatin regions. Also, the combination of two features, which describe chromatin condensation, from the outermost two layers of tumour nests had performance of AUC 94%, sensitivity of 100% and specificity of 75% in discriminating N0 and N+ group. QTP may be a potential proxy for predicting the metastatic risk of OSCC. Further investigation on potential biomarkers in risk assessment for nodal disease of early-stage OSCC patients is warranted to provide precision management to improve mortality and reduce morbidity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".