Diffusion-Weighted Imaging Using a Readout-Segmented, Multishot EPI Sequence at 3 T Distinguishes between Morphologically Differentiated and Undifferentiated Subtypes of Thyroid Carcinoma—A Preliminary Study
Bibliographic record
Abstract
BACKGROUND: Thyroid carcinomas represent the most frequent endocrine malignancies. Recent studies were able to distinguish malignant from benign nodules of the thyroid gland with diffusion-weighted imaging (DWI). Although this differentiation is undoubtedly helpful, presurgical discrimination between well-differentiated and undifferentiated carcinomas would be crucial to define the optimal treatment algorithm. Therefore, the aim of this study was to investigate if readout-segmented multishot echo planar DWI is able to differentiate between differentiated and undifferentiated subtypes of thyroid carcinomas. PATIENTS AND METHODS: Fourteen patients with different types of thyroid carcinomas who received preoperative DWI were included in our study. In all lesions, apparent diffusion coefficient (ADC)min, ADCmean, ADCmax, and D were estimated on the basis of region of interest measurements after coregistration with T1-weighted, postcontrast images. All tumors were resected and analyzed histopathologically. Ki-67 index, p53 synthesis, cellularity, and total and average nucleic areas were estimated using ImageJ version 1.48. RESULTS: Analysis of variance revealed a statistically significant difference in ADCmean values between differentiated and undifferentiated thyroid carcinomas (P=.022). Spearman Rho calculation identified significant correlations between ADCmax and cell count (r=0.541, P=.046) as well as between ADCmax and total nuclei area (r=0.605, P=.022). CONCLUSION: DWI can distinguish between differentiated and undifferentiated thyroid carcinomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".