Diffusion-weighted magnetic resonance imaging in cystic renal masses
Bibliographic record
Abstract
Introduction: We aimed to introduce the diagnostic value of diffusion-weighted (DWI) magnetic resonance imaging (MRI) for distinguishing benign and malignant renal cystic masses.Methods: Abdominal DWI-MRIs of patients with Bosniak categories 2F, 3, and 4 cystic renal masses were evaluated retrospectively. Cystic masses were assigned as benign or malignant according to histopathological or followup MRI findings and compared with apparent diffusion coefficient (ADC) values.Results: There were 30 patients (18 males and 12 females, mean age was 59.23 ± 12.08 years [range 38‒83 years]) with cystic renal masses (eight Bosniak category 2F, 12 Bosniak category 3, 10 Bosniak category 4). Among them, 14 cysts were diagnosed as benign and 16 as malignant by followup imaging or histopathological findings. For the malignant lesions, the mean ADC values were lower than for benign lesions (p=0.001). An ADC value of ≤2.28 x10-6 mm2/s or less had a sensitivity of 75% and a specificity of 92.86% for detecting malignancy.Conclusions: ADC can improve the diagnostic performance of MRI in the evaluation of complex renal cysts when used together with conventional MRI sequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".