Abstract
Objectives: Performing immediate radical cystectomy in all patients with the highest-risk non-muscle invasive bladder cancer results in overtreatment. We confirm whether the substratification of highest-risk patients can more effectively select suitable patients for radical cystectomy. Methods: Patients with primary T1 high grade bladder cancer from two centers were included and roughly stratified into high-risk or highest-risk. The highest-risk patients were further substratified according to the number of risk factors. Endpoints were tumor recurrence and progression. The predictive accuracy was assessed with internal validation that consists of time-dependent receiver operating characteristic curve and calibration curves. Results: A total of 262 patients were included. Although highest-risk patient had a poor prognosis, after further substratification, we found that those with only one factor showed the same prognosis with high-risk patients (recurrence: hazard ratio 1.79, P = 0.105; progression: hazard ratio 1.38, P = 0.532), while those with ≥2 factors had worst prognosis than high-risk patients. The 3-year area under the curve showed that the predictive accuracy of substratification in terms of recurrence and progression were superior to that of non-substratification (0.685 vs 0.622 and 0.666 vs 0.599, respectively). Additionally, calibration curves showed perfect agreement between the predicted and the actual recurrence and progression. Conclusions: Substratification of highest-risk enables us to further optimize the surgical decisions-making. Highest-risk patients with one factor show the similar outcomes as high-risk patients and deserve to try bladder-sparing treatment, whereas those with ≥2 risk factors were strongly recommended to undergo radical cystectomy.
| Original language | English |
|---|---|
| Pages (from-to) | 930-936 |
| Number of pages | 7 |
| Journal | International Journal of Urology |
| Volume | 29 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2022 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- factor number
- highest-risk
- predictive accuracy
- surgical decision-making
- tumor outcomes
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