99mTC-Sestamibi SPECT/CT Imaging for the Risk Stratification of Renal Masses
Bibliographic record
Abstract
1586 Introduction: Imaging characteristics of renal cell carcinoma (RCC) and oncocytoma overlap significantly, resulting in diagnostic uncertainty. 99mTc-sestamibi (MIBI) has emerged as a potential imaging tool to further characterize renal masses. This study evaluated the utility of MIBI SPECT/CT imaging in the assessment and risk stratification of indeterminate renal masses. Methods: 25 patients with indeterminate renal masses on cross-sectional imaging underwent MIBI SPECT/CT imaging. MIBI SPECT/CT imaging characteristics, including lesion density and visual and quantitative MIBI uptake, were correlated with histopathology from either percutaneous biopsy or surgical resection. Lesions with MIBI uptake visually were defined as positive and lesions with no uptake as negative (figure 1). Results: 25 lesions with a median size of 3 cm (1.6 - 6 cm) and density ranging from 22 to 56 Hounsfield Units were analyzed. 20 of the lesions were solid enhancing masses and 5 were Bosniak 4 cysts with measurable solid components. Histopathology demonstrated 6 oncocytomas, 1 hybrid oncocytic/chromophobe tumor (HOCT) and 18 RCCs including 13 clear cell, 3 papillary, 1 mixed papillary and clear cell, and 1 chromophobe subtype. Visually, all patients with oncocytoma and HOCT (100%) had positive MIBI scan, and all RCC patients were negative. In terms of quantitative evaluation, the median of mean and maximum relative tumor uptake (relative to ipsilateral renal parenchymal uptake) in MIBI positive tumors (oncocytomas and HOCT) was 0.75 and 0.60, compared to 0.33 and 0.26 in RCC cases, respectively. The mean relative tumor uptake of 0.44 was the cut off to classify tumors (Oncocytoma vs. RCC).Conclusion: This study demonstrates that the combination of presence or absence of MIBI uptake and lesions’ density on SPECT-CT represents a novel imaging approach to risk stratifying incompletely characterized renal masses. Further validation of this technique may reduce the need for further imaging and unnecessary biopsy or surgical resection of indeterminate renal masses.
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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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".