Role of Diffusion-Weighted MRI ( DWI) in Differentiating Between Benign and Malignant Nodules of Thyroid Taking Histopathology as Gold Standard
Bibliographic record
Abstract
Background: Thyroid gland is a principal endocrine organ, first to develop in embryo, endodermal in origin and producing hormones essential for life. It is a vascular organ inclusive of right and left lobes, surrounded by a capsule and isthmus in the centre. Objective: To determine the role of Diffusion-Weighted MRI ( DWI) in differentiating between benign and malignant thyroid nodules keeping histopathology as the gold standard. Methodology: One hundred and three patients (103) were enrolled. DWI sequence was performed on 1.5 Tesla GE machine at b-values of 0, 50 and 1000 s/mm2 with correlative ADC map and quantitative values were calculated. FNAC of the thyroid nodules was carried out and results were tallied with ADC values. Out of these, five patients lost to follow up and an inadequate sample was obtained in six patients. Results: Mean age of patients (n=92) was 39 years. Out of 92 patients, 26 patients (28.3%) showed restricted diffusion on DWI in malignant thyroid nodules. Mean ADC ( Apparent Diffusion Coefficient) value of benign thyroid nodules (1.43 × 10-3mm2s-1) was significantly greater than malignant thyroid nodules (0.91×10-3mm s-1). For discriminating two types of nodules, cut off ADC value was determined at 1.1×10-3mm2s-1 and its sensitivity, specificity, PPV, NPV and diagnostic accuracy was 84.6, 95.4, 88.8, 91.5 and 92.3% respectively. Conclusion: Diffusion Weighted MRI is a non-invasive imaging investigation without ionizing radiation hazard. Its greater soft tissue differentiation and multiplanar images help in the diagnosis of malignant thyroid nodules with high sensitivity and accuracy. The overload of unnecessary surgeries is thus lowered when pre-operative FNAC is indecisive and aids in making a precise diagnosis.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".