Diffusion-Weighted MRI in Patients with Testicular Tumors—Intra- and Interobserver Variability
Bibliographic record
Abstract
In general, magnetic resonance (MR) diffusion-weighted imaging (DWI) has shown potential in clinical settings. In testicles parenchyma, the DW imaging helps differentiate and characterize benign from malignant lesions. Placement and size of the region of interest (ROI) may affect the ADC value. Therefore, the aim of this study was to investigate the intra- and interobserver variability in testicular tumors when measuring ADC using various types of regions of interest (ROI). Two observers performed the ADC measurements in testicular lesions based on three ROI methods: (1) whole volume, (2) round, and (3) small sample groups. Intra- and interobserver variability was analyzed for all ROI methods using intraclass correlation coefficients (ICC) and bland-altman plots. The two observers performed the measurements twice, three months apart. A total of 26 malignant testicle tumors were included. Interobserver agreement was excellent in tumor length (ICC = 0.98) and tumor width (ICC = 0.98). In addition, intraobserver agreement was excellent in tumor length (ICC = 0.98) and tumor width (ICC = 0.99). The whole volume interobserver agreement in the first reading was excellent (ICC = 0.93). Round ADC had an excellent (ICC = 0.93) and fair (ICC = 0.58) interobserver agreement, in the first and second reading, respectively. Interobserver agreement in ADC small ROIs was good (ICC = 0.87), and good (ICC = 0.78), in the first and second reading, respectively. Intraobserver agreement varied from fair, good to excellent agreement. The ROI method showed varying inter- and intraobserver agreement in ADC measurement. Using multiple small ROI conceded the highest interobserver variability, and, thus, the whole volume or round seem to be the preferable methods.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".