Radiology–Pathology Concordance and Prognostication of Nodal Features in <scp>pN</scp>+ Oral Cavity Cancer
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aims of our study are to evaluate the diagnostic performance and prognostic value of radiological lymph node (LN) characteristics in pN+ oral cavity squamous carcinoma (OSCC). MATERIALS AND METHODS: pN+ OSCC treated between 2012 and 2020 were included. Preoperative imaging was reviewed by a single radiologist blinded to pathologic findings for the following nodal features: imaging-positive LN (iN+), laterality and total number, and image-identified extranodal extension (iENE). The sensitivity of iN+ for pN+ was calculated. The diagnostic performance of other nodal features was evaluated in the iN+ subgroup. The association of radiologic nodal features with overall survival (OS) was evaluated. Inter-rater kappa for radiologic nodal features was assessed in 100 randomly selected cases. RESULTS: Of 406 pN+ OSCC, 288 were iN+. The sensitivity of iN+ for pN+ was 71% overall, and improved to 89% for pN+ LN >1.5 cm. Within iN+, sensitivity/specificity for LN size (>3 cm), total LN number (>4), and ENE were 0.44/0.95, 0.57/0.84, and 0.27/0.96, respectively. Sensitivity of iENE was higher in the subset, with major (>2 mm) versus minor (≤2 mm) pENE (43% vs. 13%, p = 0.001). Reduced OS was observed in iN+ versus iN- (p = 0.006), iENE+ versus iENE- (p = 0.004), LN size >3 versus ≤3 cm (p < 0.001), and higher LN number (p < 0.001). Inter-rater kappa for iN+, laterality, total LN number, and presence of iENE were 0.71, 0.57, 0.78, and 0.69, respectively. CONCLUSION: Our study shows that despite modest sensitivity of most radiological nodal features, the specificity of image-identified nodal features is high and their prognostic values are retained in pN+ OSCC. LEVEL OF EVIDENCE: 3 (retrospective review comparing cases and controls) Laryngoscope, 134:4947-4955, 2024.
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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) | 0.000 | 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".