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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".