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Record W4408348184 · doi:10.1055/s-0045-1804292

Tumour dosimetry across 6 cycles of [177Lu]Lu-PSMA-617 in patients with metastatic castration-resistant prostate cancer: results from the VISION sub-study

2025· article· en· W4408348184 on OpenAlexaff
Ken Herrmann, Bernd‐Joachim Krause, K.N. Chi, Oliver Sartor, Karim Fizazi, Michael J. Morris, Johann S. de Bono, Scott T. Tagawa, Jens Kurth, M. Eiber, Michael Laßmann, Walter Jentzen, Rachel Sparks, Quang Ngoc Nguyen, Lars Blumenstein, C. Wilke, Kambiz Rahbar

Bibliographic record

VenueNuklearmedizin - NuclearMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsProstate cancerMedicineDosimetryCastrationProstateOncologyCancerUrologyNuclear medicineInternal medicineHormone

Abstract

fetched live from OpenAlex

Ziel/Aim: In VISION, [ 177 Lu]Lu-PSMA-617 ( 177 Lu-PSMA-617) plus standard of care (SoC) significantly improved overall survival and radiographic progression-free survival in patients (pts) with PSMA-positive metastatic castration-resistant prostate cancer. In the VISION dosimetry sub-study, 177 Lu-PSMA-617 had a good safety profile with low radiotoxicity. Here, we present the tumor dosimetry for 177 Lu-PSMA-617 across cycles 1-6. Methodik/Methods: Dosimetry was assessed in a separate cohort of 29 non-randomized pts. Pts received 177 Lu-PSMA-617 (7.4 GBq/6 weeks,≤6 cycles) plus SoC. Pts underwent SPECT/CT scans during cycle 1 at around 2, 26, 48 and 168 h after 177 Lu-PSMA-617 injection, and in subsequent cycles at 36-48 h post-injection. Region of interest/volume of interest construction was performed on PET/CT images (cycle 1) and SPECT/CT (kinetic data) images (cycles 2-6) to determine tumor volumes and morphology. Pts with≥1 evaluable tumor in cycles 2-6 after selection of≥5 tumors per pt in cycle 1 were included. The normalized disintegration numbers were calculated assuming consistent uptake and retention half-times across cycles, with only variations in activity uptake magnitude. The ratio of individual tumor activities in each cycle to cycle 1 was used to scale the normalized disintegrations from cycle 1 to following cycles. Tumor dosimetry parameters were estimated using the standard MIRD/RADAR method. Ergebnisse/Results: In total, 60 unique, delineated prostate cancer tumors were analyzed across cycles 1-6, in 18 patients who had evaluable tumors in cycles 2-6. The mean radiation-absorbed dose for all tumors declined from 7.9 Gy/GBq (SD 10; range 0.17-55) in cycle 1 (60 tumors) to 1.6 Gy/GBq (SD 1.7; range 0.11-7.5) in cycle 6 (20 tumors). For tumors in bone, the mean absorbed dose was 6.4 Gy/GBq (SD 7.3; range 0.17-45) in cycle 1 (46 tumors) and 1.3 Gy/GBq (SD 0.95; range 0.11-3.9) in cycle 6 (17 tumors). For tumors in lymphatic tissue, the mean absorbed dose was 11 Gy/GBq (SD 15; range 0.99-55) in cycle 1 (12 tumors) and 3.2 Gy/GBq (SD 3.8; range 0.14-7.5) in cycle 6 (3 tumors). In total pts received a median 6-cycle cumulative absorbed dose of ~ 100 Gy. Schlussfolgerungen/Conclusion: Declining radiation-absorbed doses across cycles 1-6 in this sub-study were consistent with the anti-tumor efficacy of 177 Lu-PSMA-617 observed in VISION. Publication History Article published online: 12 March 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.343
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractno

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