A prospective cohort study to assess the role of FDG-PET in differentiating benign and malignant follicular neoplasms
Bibliographic record
Abstract
BACKGROUND: -glucose (FDG) positron emission tomography/computed tomography (PET/CT) in predicting the risk of malignancy in follicular/Hürthle cell neoplasms. MATERIALS AND METHODS: Fifty thyroid nodules showing follicular/Hürthle cell neoplasm on prior ultrasonography guided fine needle aspiration cytology (FNAC) were recruited into this study. A FDG-PET/CT scan, performed for neck and superior mediastinum, was reported by a single observer, blinded to the surgical and pathology findings. Receiver operating characteristic (ROC) curve analysis of maximum standardized uptake value (SUVmax) and the area under the curve (AUROC) were used to assess discrimination between benign from malignant nodules. Youden index was used to identify the optimal cut-off SUVmax for diagnosing malignancy. Sensitivity, specificity, predictive values and overall accuracy were used as measures of performance. RESULTS: Our study group comprises of 31 benign and 19 malignant thyroid nodules. After excluding all Hürthle cell adenomas, the AUROC for discriminating benign and malignant non-Hürthle cell neoplasms was 0.79 (95% CI, 0.64-0.94; p = 0.001); with SUVmax of 3.25 as the best cut-off for the purpose. PET/CT had sensitivity of 79% (95% CI, 54-93%), specificity of 83% (95% CI, 60-94%), positive predictive value (PPV) of 79% (95% CI, 54-93%), and negative predictive value (NPV) of 83% (95% CI, 60-94%). The overall accuracy was 81%. CONCLUSIONS: FDG-PET/CT can help in differentiating benign and malignant non-Hürthle cell neoplasms. SUVmax of 3.25 was found to be the best for identifying malignant non-Hürthle cell follicular neoplasms.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".