A review of 99mTc-sestamibi SPECT/CT for renal oncocytomas: A modified diagnostic algorithm
Bibliographic record
Abstract
99mTc-sestamibi SPECT/CT is a promising nuclear medicine imaging investigation for benign renal lesions such as renal oncocytomas. The purpose of this article is to i) review the current literature on 99mTc-sestamibi SPECT/CT, ii) to review to current application of 99mTc-sestamibi SPECT/CT for indeterminate renal lesion imaging, and iii) to discuss present limitations and areas for future research. The literature has been reviewed up to April 2022 for articles relating to the application of 99mTc-sestamibi SPECT/CT for benign renal lesions including a recently published systematic review and meta-analysis performed by the authors. One study evaluating 99mTc-sestamibi SPECT alone and five studies evaluating 99mTc-sestamibi SPECT/CT have been performed to date. 99mTc-sestamibi SPECT/CT demonstrates high sensitivity and specificity for detecting benign renal lesions, particularly renal oncocytomas. 99mTc-sestamibi SPECT/CT demonstrates near-perfect specificity for benign and low-grade renal lesions. The optimal quantified threshold ratio for tumor-to-background renal parenchyma radiotracer uptake for a positive result is > 0.6. In this article, we propose a modified diagnostic algorithm for small enhancing renal masses measuring 1-4 cm in which suspected benign lesions after conventional imaging are considered for 99mTc-sestamibi SPECT-CT. In this algorithm, positive studies can be monitored with active surveillance rather than requiring invasive biopsy and/or targeted therapy.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".