PET‐CT for Detecting Nodal Metastasis in cN0 Early‐Stage Oral Cavity Squamous Cell Carcinoma
Bibliographic record
Abstract
Objectives: To determine if positron emission tomography (PET)‐computed tomography (CT) offers any diagnostic advantage over traditional CT neck in assessing the clinically N0 neck in patients with T1 and T2 squamous cell carcinoma (SCC) of the oral cavity. Methods: We performed a retrospective review of patients in the Alberta Cancer Registry who were diagnosed with cT1 or T2N0M0 disease who underwent elective unilateral or bilateral neck dissections. Results of preoperative PET‐CT and CT necks were reviewed for number of “suspicious” lymph nodes. Surgical pathology reports were reviewed to obtain the total number of nodes sampled and number of malignant nodes. Results: Between 2009 and 2011, 148 patients were diagnosed with cT1 or T2N0M0 SCC of the oral cavity. Of these, 62 patients underwent elective neck dissections. Fourteen patients underwent preoperative PET‐CT while 48 patients underwent CT neck alone. Based on final surgical pathology, 6 nodes out of 499 nodes sampled were falsely fludeoxyglucose‐avid in the PET‐CT group while 3 nodes out of 1800 were falsely identified as suspicious on CT neck alone. The overall false positive rate of PET‐CT was significantly higher than CT alone (1.2% vs 0.2%, P <. 001). Both modalities had excellent specificity of >98% for benign nodes in these patients. Conclusions: In patients with cT1 and T2 of the oral cavity and no palpable lymphadenopathy, PET‐CT is no better than CT alone for ruling out nodal metastasis and may have a higher false positive rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".