The prognostic impact of bone metastasis in urothelial carcinoma treated with first-line platinum-based chemotherapy.
Bibliographic record
Abstract
415 Background: Metastatic urothelial carcinoma (mUC) is an aggressive disease with a median overall survival (OS) of ≈ 15 months. In the first-line setting, key prognostic factors include ECOG performance status, white blood cell count, and response to treatment per the Galsky nomogram. Bone metastases (BM) in mUC are associated with morbidity and mortality but are grouped with visceral disease; hence, their impact on prognosis is not well established. We aimed to assess the survival impact of BM in mUC patients treated with first-line platinum-based chemotherapy (PBC). Methods: A retrospective collection of patient and tumor characteristics, with clinical response to treatment (complete response [CR], partial response [PR]; stable disease [SD] or progressive disease [PD]) for patients treated at Princess Margaret Cancer Centre, Tom Baker Cancer Centre, and Cross Cancer Institute from 2005-2018 was performed. Progression-free survival (PFS) and OS were estimated using the Kaplan-Meier method. Univariate (UVA) followed by multivariate analysis (MVA) of patient variables [Cox] using PFS and OS was performed. Results: Overall 376 mUC patients were included; 222 (59%) had soft-tissue metastases (STM) only, 70 (19%) had bone-only metastases, and 84 (22%) had both STM and BM. Overall, 35% had PR or CR, 19% had SD, and 39% had PD (7%: unknown response). The median PFS and OS for the whole cohort were 5.6 months (95%CI: 4.8-6.4) and 9.7 months (95% CI: 8.8-10.8) respectively. Select UVA by metastatic site showed inferior PFS for bone-only (p=0.03) and combination STM and BM (p=0.017). Only combination STM and BM were significant on UVA for OS (p=0.002). MVA showed that bone-only metastases (p=0.03) and ECOG 3-4 (p<0.0001) were associated with worse PFS (Table). Predictors of worse OS were the combination of STM and BM (p=0.02), ECOG 3-4 (p=0.001), and WBCs ≥ULN (p=0.02), (Table). Conclusions: BM are a significant predictor of worse outcomes for mUC patients treated with first-line PBC. Consideration as a treatment stratification factor for future studies is suggested. Strategies for the treatment of mUC patients with BM (ie: bone targeted agents) in the first-line setting should be addressed in future trials. [Table: see text]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".