Predictive Value of Chemical-Shift MRI in Distinguishing Clear Cell Renal Cell Carcinoma From Non–Clear Cell Renal Cell Carcinoma and Minimal-Fat Angiomyolipoma
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the diagnostic performance of chemical-shift MRI in the differentiation of clear cell renal cell carcinoma (RCC) from minimal-fat angiomyolipoma (AML) and non-clear cell RCC. MATERIALS AND METHODS: In this retrospective study, 97 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of clear cell RCC (n = 40), non-clear cell RCC (n = 31), or minimal-fat AML (n = 26) who had undergone renal chemical-shift MRI were included. Size, location, morphology, and signal intensity (SI) of the tumors and the contralateral normal kidneys on T2-weighted and in-phase and opposed-phase images were recorded by readers blinded to the pathology. Percentage tumor-to-renal parenchymal SI drop (percentage SI drop) was calculated and correlated to tumor histology. The statistical analysis was done using Kruskal-Wallis, one-way ANOVA, chi-square, and Fisher exact tests. RESULTS: The percentage SI drop was significantly higher in clear cell RCC compared with non-clear cell RCC and minimal-fat AML (p < 0.001). Percentage SI drop of greater than 20% had 57.5% sensitivity, 96.5% specificity, and 92% positive predictive value (PPV); and percentage SI drop greater than 29% had 40% sensitivity and 100% specificity for diagnosis of clear cell RCC within the cohort of clear cell RCC, minimal-fat AML, and non-clear cell RCC. A significant proportion of minimal-fat AML (46.2%) displayed homogeneous low T2-weighted SI as opposed to clear cell RCC (5%) and non-clear cell RCC (29%) (p < 0.001). CONCLUSION: The percentage SI drop on chemical-shift MRI had high specificity and moderate sensitivity in predicting clear cell RCC over non-clear cell RCC and minimal-fat AML. A percentage SI drop greater than 20% in a renal mass without macroscopically visible fat has high PPV for clear cell RCC over minimal-fat AML and non-clear cell RCC. Among morphologic features, homogeneous low T2 SI favors minimal-fat AML over RCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".