Abstract 5125: Predictive markers on risk of developing lymph node metastasis in early-staged oral cancer patients.
Bibliographic record
Abstract
Abstract Introduction: Cervical lymph nodal metastasis is the most important variable for unimproved survival of oral squamous cell carcinoma (OSCC) patients. Resulting from lack of predictive marker, unnecessary neck dissection (ND) has tremendous impact on costs and patient's quality of life. Current literature have shown contradictory results in predicting markers using clinico-pathological parameters. Objectives: 1) to collect the demographics, clinico-pathological information of primary OSCC patients with intent-to-cure surgery; 2) to categorize these patients according to their nodal status at and after surgery; 3) to determine the impact of nodal status to overall survival; and 4) to assess clinic-pathological variables predicting nodal disease of N0 early-stage OSCC patients. Methods: Between 2003 and 2007, 303 primary OSCC patients were identified from the BC Cancer Registry Database with complete clinico-pathological information and received primary curative surgical treatment with at least 5-year follow-up (FU). Patients were categorized into 4 groups: Gr.A were N0 at surgery or during FU (N=118); Gr.B were N0 at surgery but N+ during FU (N=57); Gr.C received concurrent ND and were N0 at the time of surgery (N=57); and Gr.D received concurrent ND and were N+ at the time of surgery (N=71). Data retrieved included demographics, clinico-pathological factors, treatment, and time to outcomes (survival or nodal disease). Results: Nodal disease at the time of surgery (Gr.D) or during the FU (Gr.B) has a pivotal impact on the 5-year survival rates (44% and 55%, respectively, P<0.0001). Cox proportional hazard models identified positive nodal disease, TNM staging, and adjuvant radiotherapy as significant predictors for survival (HR: 2.43, 95%CI, 1.18-5.02; P=0.02; 1.31, 95%CI, 1.0-1.7; P=0.03; 6.53, 95%CI, 2.86-14.91; P<0.0001, respectively). Among the 205 N0 at the time of surgery (Gr.A+Gr.B), strikingly, one-in-four developed nodal disease in average 13.2±14.1 months and with 76% in the first 18-months post surgery. Between Gr.A and Gr.B, there was no significant differences in tumor depth (4.2±0.4 mm vs. 5.0±0.5 mm, P=0.25). Using multivariate analysis, age (P=0.03) and tumor grade (P=0.001) were significant predictive markers. Tumor depth of 4 mm, the current standard factor on the necessity of prophylactic ND, did not predict nodal status (P=0.11). Conclusion: Nodal status is highly associated with patient survival. The data strongly suggest aggressiveness of neck metastasis either at the time of surgery or during FU. Effective markers to predict nodal disease pre-surgery can benefit high-risk patients to have early intervention and avoid unnecessary ND for the low-risk. (Supported by the Canadian Cancer Society Research Institute (CCSRI-20336), and Terry Fox Research Institute (TFRI-2009-24). CFP is supported by a Scholar Award from the Michael Smith Foundation for Health Research.) Citation Format: Kelly YP Liu, Scott Durham, Kenneth W. Berean, Catherine F. Poh. Predictive markers on risk of developing lymph node metastasis in early-staged oral cancer patients. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5125. doi:10.1158/1538-7445.AM2013-5125 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".