Prevalence and Clinical Correlations of Somatostatin Receptor-2 (SSTR2) Expression in Neuroblastoma
Bibliographic record
Abstract
Alternative radiolabeled, targeted agents are being investigated for children with relapsed neuroblastoma (NB) who do not respond to I-metaiodobenzylguanidine (MIBG) therapy. (DOTA-Tyr)-octreotate targets somatostatin receptors (SSTRs), particularly SSTR2, which are expressed on NB cells. We investigated SSTR2 expression in NB tumors (36 high-risk [HR]; 33 non-HR patients) and correlated SSTR2 levels with clinical features, norepinephrine transporter (NET) expression, and MIBG avidity. SSTR2 and NET immunohistochemistry scores (0 to 3) were calculated on biopsies using digital image analysis based on staining intensity and distribution. Clinical data were correlated with SSTR2 expression. Median SSTR2 score for 69 patients was 1.31 (0.26 to 2.55). Non-HR NB was associated with a higher SSTR2 score (P=0.032). The SSTR2 expression did not correlate with age, International Neuroblastoma Staging System (INSS) stage, MYCN amplification and histology. Higher SSTR2 scores were observed in MIBG-avid versus MIBG-nonavid NB. SSTR2 score was not significantly associated with NET score (r=-0.062, P=0.62). Twenty-six patients who relapsed or progressed had a median SSTR2 score of 1.33 (0.26 to 2.55). Patients with NB including relapsed or progressive disease showed SSTR2 expression at diagnosis, suggesting they could be candidates for radiolabeled-DOTA-conjugated peptide imaging or 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 | 0.000 | 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 | 0.000 | 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".