Radiologic-Pathologic Correlation of Tumor Thickness and Its Prognostic Importance in Squamous Cell Carcinoma of the Oral Cavity: Implications for the Eighth Edition Tumor, Node, Metastasis Classification
Bibliographic record
Abstract
<h3>BACKGROUND AND PURPOSE:</h3> Addressing the performance of an imaging-based parameter compared to a “gold standard” pathologic measurement is essential to achieve accurate clinical T-classification. Our aim was to determine the radiologic-pathologic tumor thickness correlation and its prognostic value in oral squamous cell carcinoma. <h3>MATERIALS AND METHODS:</h3> All pathologic T1–T3 (seventh edition of the <i>Cancer Staging Manual</i> of the American Joint Committee on Cancer) oral squamous cell carcinomas diagnosed between 2010 and 2015 were reviewed. Radiologic tumor thickness was measured on preoperative CT or MR imaging blinded to pathology. The radiologic-pathologic tumor thickness correlation was calculated. The impact of the imaging-to-surgery time interval and imaging technique on the correlation was explored. Intra-/interrater reliability on radiologic tumor thickness was calculated. The correlation of radiologic-versus-pathologic tumor thickness and its performance as the seventh edition T-category modifier was evaluated. Multivariable analysis assessed the prognostic value of the radiologic tumor thickness for overall survival adjusted for age, seventh edition T-category, and performance status. <h3>RESULTS:</h3> For 354 consecutive patients, the radiologic-pathologic tumor thickness correlation was similar for the image-to-surgery interval of ≤4.0 weeks (ρ = 0.76) versus 4–8 weeks (ρ = 0.80) but lower in those with more than an 8-week interval (ρ = 0.62). CT and MR imaging had similar correlations (0.76 and 0.80). Intrarater and interrater reliability was excellent (0.88 and 0.84). Excluding 19 cases with an imaging-to-surgery interval of >8 weeks, 335 patients were eligible for further analysis. The radiologic-pathologic tumor thickness correlation was 0.78. The accuracy for upstaging the T-classification based on radiologic tumor thickness was 83% for pathologic T1 and 74% for pathologic T2 tumors. Multivariable analysis confirmed the prognostic value of radiologic tumor thickness (hazard ratio = 1.5, <i>P</i> = .02) for overall survival. <h3>CONCLUSIONS:</h3> This study demonstrates a good radiologic-pathologic tumor thickness correlation. Intrarater and interrater reliability for radiologic tumor thickness was excellent. Radiologically thicker tumor was predictive of inferior survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".