The value of 99mTc-sestamibi single-photon emission computed tomography-computed tomography in the evaluation and risk stratification of renal masses
Bibliographic record
Abstract
INTRODUCTION: Tc-sestamibi (MIBI) imaging has the potential to characterize indeterminate renal masses. This comparative study evaluated the utility of MIBI single-photon emission computed tomography-computed tomography (SPECT-CT) in the assessment and risk stratification of renal masses. METHODS: A total of 29 patients with 31 renal masses who had cross-sectional imaging and MIBI SPECT-CT were included. Lesions were categorized as either MIBI-positive or -negative on SPECT-CT. Individual lesion density ranged from 22-56 Hounsfield units (HU) on the non-contrast CT part of SPECT-CT. Quantitative relative MIBI uptake was calculated by measuring tumor to ipsilateral renal parenchymal uptake. The imaging results were correlated with histopathology. RESULTS: All oncocytic lesions, including seven oncocytomas and one hybrid oncocytic chromophobe tumor (100%), were positive on MIBI. One chromophobe RCC showed low-grade MIBI uptake. The remaining RCC subtypes, including 15 clear-cell, four papillary, two mixed clear-cell and papillary, and one chromophobe, were MIBI-negative. The quantitative relative tumor uptake showed statistically significant higher uptake in the low-risk/oncocytic lesions compared to RCCs. CONCLUSIONS: This study demonstrates that MIBI SPECT-CT is valuable in the characterization of indeterminate renal masses. The combination of MIBI uptake on SPECT and lesion density on non-contrast CT can be used for risk stratification of renal masses. This technique may reduce the need for further imaging (multiphasic CT or magnetic resonance imaging), renal mass biopsy, or surgical resection of low-risk renal masses. Subsequently, more patients could be followed with active surveillance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".