Peptide Receptor Radionuclide Therapy with Lu-177-DOTATATE and Monitoring with Somatostatin Receptor PET/CT in Patients with Advanced Differentiated Thyroid Carcinoma
Bibliographic record
Abstract
PURPOSE: Peptide receptor radionuclide therapy (PRRT) with Lu-177-DOTATATE is an established treatment option for neuroendocrine tumors (NETs) and has been extended to other somatostatin receptor (SSTR)-expressing tumors. We aimed to determine its efficacy and safety profile in patients with advanced radioiodine-refractory differentiated thyroid carcinoma (DTC). METHODS: Seven radioiodine-refractory DTC patients undergoing at least two cycles of PRRT were included. Patients were subdivided into continuous treatment (defined as sequential application of PRRT; 5/7 (71.4%)) vs. discontinuous treatment (with at least one-year PRRT-free interval; 2/7 (28.6%)). Baseline SSTR PET was analyzed to determine patients' eligibility for PRRT. Response was assessed by tumor control as defined by stable (± 30.0%) or decreasing (≥ 30.0%) total tumor volume (PET-derived TTV), thyroglobulin (Tg) and RECIST 1.1 criteria. RESULTS: SSTR PET showed discernible high uptake (maximum standardized uptake values, 10.4 ± 8.6) in metastases, in particular in the skeleton. Continuous PRRT showed variable tumor control (stable disease / response; TTV: 3/5 (60.0%); Tg: 2/5 (40.0%); RECIST 1.1: 3/5 (60.0%)). All patients undergoing discontinuous PRRT exhibited concordant stable disease upon first follow-up and renewed tumor control upon reinitiating PRRT (RECIST 1.1; decreasing TTV and Tg levels). No Common Terminology Criteria for Adverse Events (CTCAE) Grade 3-5 events occured in both groups. CONCLUSION: In advanced radioiodine-refractory DTC, PRRT may be beneficial even after treatment interruptions, without major side effects. Given the small cohort and retrospective design, further prospective studies are needed to optimize PRRT strategies in DTC, in particular in a rechallenge scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".