Rechallenge and Extended [ <sup>177</sup> Lu]Lu-PSMA Therapy in Metastatic Prostate Cancer
Bibliographic record
Abstract
Continuation of effective and well-tolerated systemic treatment is often performed in care for metastatic castration-resistant prostate cancer. Likewise, continued administration of [177Lu]Lu-PSMA radiopharmaceutical therapy beyond the approved number of cycles holds promising potential to enhance therapeutic efficacy. Rechallenge therapy involves readministration of [177Lu]Lu-PSMA cycles after a break, whereas extended therapy continues treatment beyond the standard 6 cycles without interruption. Both approaches aim to improve disease control and prolong survival in patients with metastatic castration-resistant prostate cancer. However, practices vary: some clinicians continue treatment in patients with early favorable responses, whereas others recommend pausing therapy after significant prostate-specific antigen declines, even after a few cycles. In this narrative review, we show that safety profiles for continued [177Lu]Lu-PSMA radiopharmaceutical therapy are generally favorable, and most adverse events are mild to moderate in severity. Hematotoxicity, particularly anemia and thrombocytopenia, is the most significant concern, with few patients experiencing high-grade adverse events. In addition, cumulative irradiation, particularly during extended therapy, necessitates careful monitoring of hematologic and renal function. Biochemical responses to rechallenge and extended [177Lu]Lu-PSMA therapy are promising, with at least 50% reductions in prostate-specific antigen levels observed in a significant proportion of highly selected patients. Moreover, survival outcomes are encouraging, showing the extension of overall and progression-free survival beyond the known data for standard therapy. Despite these advances, challenges remain in optimizing patient selection, managing cumulative toxicities, and harmonizing treatment protocols. In addition, variability in trial designs, influenced by international regulatory differences, limits the current evidence and necessitates consideration of each treatment approach within its regulatory context. Prospective studies are needed to refine therapeutic strategies, implement consistent clinical and imaging response criteria, and identify predictive biomarkers to improve both efficacy and safety.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".