Validation of the standardization framework SSTR-RADS 1.0 for neuroendocrine tumors using the novel SSTR‑targeting peptide [18F]SiTATE
Bibliographic record
Abstract
Abstract Objectives Somatostatin receptor positron emission tomography/computed tomography (SSTR-PET/CT) using [ 68 Ga]-labeled tracers is a widely used imaging modality for neuroendocrine tumors (NET). Recently, [ 18 F]SiTATE, a SiFAlin tagged [Tyr3]-octreotate (TATE) PET tracer, has shown great potential due to favorable clinical characteristics. We aimed to evaluate the reproducibility of Somatostatin Receptor-Reporting and Data System 1.0 (SSTR-RADS 1.0) for structured interpretation and treatment planning of NET using [ 18 F]SiTATE. Methods Four readers assessed [ 18 F]SiTATE-PET/CT of 95 patients according to the SSTR-RADS 1.0 criteria at two different time points. Each reader evaluated up to five target lesions per scan. The overall scan score and the decision on peptide receptor radionuclide therapy (PRRT) were considered. Inter- and intra-reader agreement was determined using the intraclass correlation coefficient (ICC). Results The ICC analysis on the inter-reader agreement using SSTR-RADS 1.0 for identical target lesions (ICC ≥ 85%), overall scan score (ICC ≥ 90%), and the decision to recommend PRRT (ICC ≥ 85%) showed excellent agreement. However, significant differences were observed in recommending PRRT among experienced readers (ER) ( p = 0.020) and inexperienced readers (IR) ( p = 0.004). Compartment-based analysis demonstrated good to excellent inter-reader agreement for most organs (ICC ≥ 74%), except for lymph nodes (ICC ≥ 53%). Conclusion SSTR-RADS 1.0 represents a highly reproducible and consistent framework system for stratifying SSTR-targeted PET/CT scans, even using the novel SSTR-ligand [ 18 F]SiTATE. Some inter-reader variability was observed regarding the evaluation of uptake intensity prior to PRRT as well as compartment scoring of lymph nodes, indicating that those categories require special attention during further clinical validation and might be refined in a future SSTR-RADS version 1.1. Clinical relevance statement SSTR-RADS 1.0 is a consistent framework for categorizing somatostatin receptor-targeted PET/CT scans when using [ 18 F]SiTATE. The framework serves as a valuable tool for facilitating and improving the management of patients with NET. Key Points SSTR-RADS 1.0 is a valuable tool for managing patients with NET . SSTR-RADS 1.0 categorizes patients with showing strong agreement across diverse reader expertise . As an alternative to [ 68 Ga]-labeled PET/CT in neuroendocrine tumor imaging, SSTR-RADS 1.0 reliably classifies [ 18 F]SiTATE-PET/CT .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".