Incidence and Outcomes of Secondary Bladder Cancer Following Radiation Therapy for Prostate Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There is an established association between secondary bladder cancers (SBCs) and radiotherapy (RT) for prostate cancer (PC), which remains a significant concern. Our aim was to update the evidence on SBC incidence across different RT modalities and to compare oncological outcomes for patients diagnosed with SBC to those diagnosed with primary bladder cancer (PBC). METHODS: We searched MEDLINE, Scopus, and Web of Science for studies on SBC following PC. Pairwise meta-analyses were conducted to compare SBC incidence in terms of odds ratios (ORs) between RT modalities (external beam radiation therapy [EBRT], brachytherapy [BT], and BT + EBRT) and PBC incidence after radical prostatectomy (RP). SBC incidence data are presented as proportions, and pairwise meta-analyses were used to compare overall survival (OS) between SBC and PBC using hazard ratios (HRs). KEY FINDINGS AND LIMITATIONS: Thirty-one studies (n = 576 341) were included. All RT modalities significantly increased the risk of SBC in comparison to RP at all time points investigated. BT alone had similar long-term SBC risk in comparison to EBRT (OR 0.56, 95% confidence interval [CI] 0.25-1.23 at 10 yr; OR 0.51, 95% CI 0.24-1.06 at 15 yr). There was no significant difference in OS between SBC and PBC in the overall cohort. However, among patients requiring radical cystectomy (RC), SBC resulted in a significant decrease in OS in comparison to PBC (HR 1.55, 95% CI 1.06-2.26; p = 0.02). CONCLUSIONS AND CLINICAL IMPLICATIONS: All RT modalities increased the risk of SBC at each post-RT time point investigated. SBC patients requiring RC have worse survival than those with PBC. Our results highlight the need for ongoing surveillance and early detection. Despite the rarity of SBC, clinicians should monitor bladder symptoms in PC patients after RT. These data need to be included in the shared decision-making process with patients regarding therapeutic decisions to raise awareness of SBC in this setting.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.039 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".