Cohort Study of Somatostatin-Based Radiopeptide Therapy With [<sup>90</sup>Y-DOTA]-TOC Versus [<sup>90</sup>Y-DOTA]-TOC Plus [<sup>177</sup>Lu-DOTA]-TOC in Neuroendocrine Cancers
Bibliographic record
Abstract
PURPOSE: Radiopeptide therapy is commonly performed with a single radioisotope. We aimed to compare the effectiveness of somatostatin-based radiopeptide therapy with a single versus a combination of radioisotopes. PATIENTS AND METHODS: In a cohort study, patients with metastasized neuroendocrine cancer were treated with repeated cycles of (90)yttrium-labeled tetraazacyclododecane-tetraacetic acid modified Tyr-octreotide ([(90)Y-DOTA]-TOC) or with cycles alternating between [(90)Y-DOTA]-TOC and (177)lutetium-labeled DOTA-TOC ([(177)Lu-DOTA]-TOC) until tumor progression or permanent toxicity. Multivariable Cox regression and competing risk regression were used to study predictors of survival and renal toxicity in patients completing three or more treatment cycles. RESULTS: A total of 486 patients completed three or more treatment cycles; 237 patients received [(90)Y-DOTA]-TOC and 249 patients received [(90)Y-DOTA]-TOC + [(177)Lu-DOTA]-TOC. Patients receiving [(90)Y-DOTA]-TOC + [(177)Lu-DOTA]-TOC had a significantly longer survival than patients receiving [(90)Y-DOTA]-TOC alone (5.51 v 3.96 years; hazard ratio, 0.64; 95% CI, 0.47 to 0.88; P = .006). The rates of severe hematologic toxicities (6.3% v 4.4%; P = .25) and severe renal toxicity (8.9% v 11.2%; P = .47) were comparable in both groups. CONCLUSION: [(90)Y-DOTA]-TOC + [(177)Lu-DOTA]-TOC was associated with improved overall survival compared with [(90)Y-DOTA]-TOC alone in patients completing three or more cycles of treatment. Contrary to the current practice in radiopeptide therapy, our results suggest an advantage of using a combination of radioisotopes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".