Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis
Bibliographic record
Abstract
Background: Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed. Objective: This study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively. Methods: A retrospective cohort study was conducted at Peking University Third Hospital in Beijing, China, from 2012 to 2023. Patients with a postoperative pathological diagnosis of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included. FTNs were classified into small- and large-sized categories based on the cutoff value of the tumor diameter derived from spline regression, which indicated the turning point of malignancy risk. We identified the 5 most important predictors from 22 variables including demography, sonography, and hormones, using machine learning methods. We also calculated the odds ratios (OR) with 95% CI for these predictors in both small- and large-sized FTNs. Results: Altogether, we included 1494 FTNs, comprising 1266 FTAs and 228 FTCs. FTNs with a maximum diameter less than 3.0 cm were grouped as small-sized tumors (n=715), while those with larger diameters were categorized as large-sized tumors (n=779). In the small-sized group, tumors with macrocalcification (OR 2.90, 95% CI 1.50-5.60), those with peripheral calcification (OR 4.50, 95% CI 1.50-13.00), and those in younger patients (OR 1.33, 95% CI 1.05-1.69) showed a higher malignancy risk. In the large-sized group, tumors presenting with a nodule-in-nodule appearance (OR 3.30, 95% CI 1.30-7.90) exhibited a higher malignancy risk. In both groups, lower thyroid-stimulating hormone levels (OR 1.49, 95% CI 1.20-1.85 for small-sized FTNs; OR 1.61, 95% CI 1.37-1.96 for large-sized FTNs) and a larger mean diameter (OR 1.40, 95% CI 1.10-1.70 for small-sized FTNs; OR 1.50 95% CI 1.20-1.70 for large-sized FTNs) were associated with the malignancy risk of FTNs. Conclusions: This study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".