Assessment of Cervical Lymph Node Metastasis with Different Imaging Methods in Patients with Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To determine the predictive value of different imaging methods,-computed tomography (CT), magnetic resonance imaging (MRI), ultrasonography (US), and single-photon emission tomography (SPECT),-for cervical node metastasis. DESIGN: Prospective clinical trial. SETTING: An academic otolaryngology department. METHODS: Twenty-three consecutive patients with head and neck malignancy were prospectively evaluated for the presence of cervical lymphadenopathy. All patients underwent clinical, CT, MRI, US, and SPECT examinations. Neck dissection was performed for 31 neck sides, and the results of the preoperative evaluation were confirmed by the surgical and histopathologic findings. MAIN OUTCOME MEASURES: The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each method and a comparison of the methods was done. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CT, MRI, US, and SPECT were 77.7%, 85.7%, 91.3%, 66.6%, and 80.4%; 59.2%, 92.8%, 94.1%, 54.1%, and 70.7%; 81.4%, 64.2%, 81.4%, 64.2%, and 75.6%; 55.5%, 92.8%, 93.7%, 52.0%, and 68.2%, respectively. Both CT and US were found to be superior to clinical examination. There was no statistically significant difference between US and CT. US was found to be superior to MRI and SPECT in detecting cervical node metastasis. CT was also superior to SPECT. CONCLUSION: Our data show that, despite high specificity rates, especially with SPECT, none of the currently available imaging methods are reliable in evaluating the occult regional metastasis because the negative predictive values of all of these methods are rather low.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".