Examination of Malignant Findings of Thyroid Nodules Using Thyroid Ultrasonography
Bibliographic record
Abstract
BACKGROUND: It is important to distinguish benign thyroid nodules from malignant thyroid nodules. Hence, this study aimed to determine the characteristics of patients with thyroid cancer using thyroid ultrasonography. METHODS: We retrospectively examined the ultrasonographic findings of 327 patients with 457 thyroid nodules (age: 59.9 ± 14.3 years; sex, n (%): female 242 (74.0%)) at a single center from 2014 to 2016. Ultrasonography was used to determine the nodule size, shape, border, internal echogenicity, presence of coarse calcifications and microcalcifications within the nodule, internal blood flow and whether the nodule was solid or contained cystic structures. Thyroid fine needle aspiration cytology (FNAC) was performed in all patients. The ultrasonographic findings were compared between patients with benign nodules and those with papillary thyroid carcinoma (PTC). Furthermore, in the analysis of anti-thyroglobulin (Tg) antibody-negative patients with single nodules, values of serum Tg/nodule volume were calculated and compared between patients with benign nodules and those with PTC. RESULTS: There were 298 (65.2%) benign nodules, 33 (7.2%) PTCs and 126 (27.6%) others (104 follicular neoplasms, 19 masses of undetermined significance and three other malignant tumors). The nodules diagnosed as PTC had significantly lower internal echogenicity (P < 0.01), more microcalcifications (P < 0.01) and comprised more nodules rich in blood flow (P < 0.05) than benign nodules. Solid nodules were found significantly more in the PTC group (P < 0.01). The serum Tg/nodule volume ratio was significantly higher in the PTC group (P < 0.05). CONCLUSIONS: Findings suggestive of PTC were found from images obtained using thyroid ultrasonography. In the diagnosis of PTC, the frequency of FNAC examinations should be reduced as this method is costly and invasive.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".