Multi-institutional Development and External Validation of a Machine Learning Model for the Prediction of Distant Metastasis in Patients Treated by Salvage Radiotherapy for Biochemical Failure After Radical Prostatectomy.

Sabbagh, Ali; Tilki, Derya; Feng, Jean; Huland, Hartwig; Graefen, Markus; Wiegel, Thomas; Böhmer, Dirk; Hong, Julian C; Valdes, Gilmer; Cowan, Janet E; Cooperberg, Matthew; Feng, Felix Y; Mohammad, Tarek; Shelan, Mohamed; D'Amico, Anthony V; Carroll, Peter R; Mohamad, Osama (2024). Multi-institutional Development and External Validation of a Machine Learning Model for the Prediction of Distant Metastasis in Patients Treated by Salvage Radiotherapy for Biochemical Failure After Radical Prostatectomy. European urology focus, 10(1), pp. 66-74. Elsevier 10.1016/j.euf.2023.07.004

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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.

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6 Citations in Scopus

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