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.

Biochemical recurrence Distant metastasis Machine leaning Prostate cancer Radical prostatectomy Salvage radiotherapy

Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
26 Jul 2023
Historique:
received: 16 03 2023
revised: 30 05 2023
accepted: 13 07 2023
medline: 29 7 2023
pubmed: 29 7 2023
entrez: 28 7 2023
Statut: aheadofprint

Résumé

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.

Sections du résumé

BACKGROUND 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 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 METHODS
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 METHODS
Model performance was assessed using calibration and time-dependent area under the receiver operating curve (tAUC).
RESULTS AND LIMITATIONS CONCLUSIONS
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 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 RESULTS
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.

Identifiants

DOI: 10.1016/j.euf.2023.07.004 PMID: 37507248
pubmed: 37507248
pii: S2405-4569(23)00176-1
doi: 10.1016/j.euf.2023.07.004
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Ali Sabbagh (A)

Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.

Derya Tilki (D)

Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Martini-Klinik Prostate Cancer Center, University Hospital-Hamburg-Eppendorf, Hamburg, Germany.

Jean Feng (J)

Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.

Hartwig Huland (H)

Martini-Klinik Prostate Cancer Center, University Hospital-Hamburg-Eppendorf, Hamburg, Germany.

Markus Graefen (M)

Martini-Klinik Prostate Cancer Center, University Hospital-Hamburg-Eppendorf, Hamburg, Germany.

Thomas Wiegel (T)

Department of Radio Oncology, University Hospital Ulm, Ulm, Germany.

Dirk Böhmer (D)

Department of Radiation Oncology, Charité University Hospital, Berlin, Germany.

Julian C Hong (JC)

Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.

Gilmer Valdes (G)

Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.

Janet E Cowan (JE)

Department of Urology, University of California San Francisco, San Francisco, CA, USA.

Matthew Cooperberg (M)

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.

Felix Y Feng (FY)

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.

Tarek Mohammad (T)

University of California Berkeley, Berkeley, CA, USA.

Mohamed Shelan (M)

Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Anthony V D'Amico (AV)

Department of Radiation Oncology, Brigham and Women's Hospital and Dana Farber Cancer Institute, Boston, MA, USA.

Peter R Carroll (PR)

Department of Urology, University of California San Francisco, San Francisco, CA, USA.

Osama Mohamad (O)

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: osama.mohamad@ucsf.edu.

Classifications MeSH