Abstract
Background: Up to 40% of patients with prostate cancer may develop biochemical recurrence after surgery, with salvage radiation therapy (SRT) being the only curative option. In 2016, Tendulkar et al. (Contemporary update of a multi-institutional predictive nomogram for salvage radiotherapy after radical prostatectomy. J Clin Oncol 2016;34:3648–54) published a nomogram to predict distant metastasis in a cohort of patients treated with SRT with pre-SRT prostate-specific antigen (PSA) of 0.5 ng/ml after radical prostatectomy. In modern practice, SRT is delivered at lower PSA values. Objective: To train and externally validate a machine learning model to predict the risk of distant metastasis at 5 yr in a contemporary cohort of patients receiving SRT. Design, setting, and participants: We trained a machine learning model on data from 2418 patients treated with SRT at one institution, with a median PSA value of 0.27 ng/ml. External validation was done in 475 patients treated at two different institutions. Patients with cM1, pN1, or pT4 disease were excluded, as were patients with PSA >2 ng/ml or PSA 0, and patients with radiation dose <60 or ≥80 Gy. Outcome measurements and statistical analysis: Model performance was assessed using calibration and time-dependent area under the receiver operating curve (tAUC). Results and limitations: Our model had better calibration and showed improved discrimination (tAUC = 0.72) compared with the Tendulkar model (tAUC = 0.60, p < 0.001). The main limitations of this study are its retrospective design and lack of validation on patients who received hormone therapy. Conclusions: The updated model can be used to provide more individualized risk assessments to patients treated with SRT at low PSA values, improving decision-making. Patient summary: Up to 40% of patients with prostate cancer may develop biochemical recurrence after surgery, with salvage radiation therapy as the only potentially curative option. We trained and validated a machine learning model using clinical and surgical data to predict a patient's risk of distant metastasis at 5 yr after treatment. Our model outperformed the reference tool and can improve clinical decision-making by providing more personalized risk assessment.
Original language | English (US) |
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Pages (from-to) | 66-74 |
Number of pages | 9 |
Journal | European Urology Focus |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2024 |
Externally published | Yes |
Keywords
- Biochemical recurrence
- Distant metastasis
- Machine leaning
- Prostate cancer
- Radical prostatectomy
- Salvage radiotherapy
ASJC Scopus subject areas
- Urology