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.
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.
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.
Model performance was assessed using calibration and time-dependent area under the receiver operating curve (tAUC).
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.
The updated model can be used to provide more individualized risk assessments to patients treated with SRT at low PSA values, improving decision-making.
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.
European urology focus. 2023 Jul 26 [Epub ahead of print]
Ali Sabbagh, Derya Tilki, Jean Feng, Hartwig Huland, Markus Graefen, Thomas Wiegel, Dirk Böhmer, Julian C Hong, Gilmer Valdes, Janet E Cowan, Matthew Cooperberg, Felix Y Feng, Tarek Mohammad, Mohamed Shelan, Anthony V D'Amico, Peter R Carroll, Osama Mohamad
Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA., Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Martini-Klinik Prostate Cancer Center, University Hospital-Hamburg-Eppendorf, Hamburg, Germany., Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA., Martini-Klinik Prostate Cancer Center, University Hospital-Hamburg-Eppendorf, Hamburg, Germany., Department of Radio Oncology, University Hospital Ulm, Ulm, Germany., Department of Radiation Oncology, Charité University Hospital, Berlin, Germany., Department of Urology, University of California San Francisco, San Francisco, CA, USA., Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA; Department of Urology, University of California San Francisco, San Francisco, CA, USA., Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA; Department of Urology, University of California San Francisco, San Francisco, CA, USA., University of California Berkeley, Berkeley, CA, USA., Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland., Department of Radiation Oncology, Brigham and Women's Hospital and Dana Farber Cancer Institute, Boston, MA, USA., Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA; Department of Urology, University of California San Francisco, San Francisco, CA, USA. Electronic address: .