A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study.

DOI
Publisher DOI
PubMed ID
39303279
Abstract
Background
Salvage radiation therapy (sRT) is often the sole curative option in patients with biochemical recurrence after radical prostatectomy. After sRT, we developed and validated a nomogram to predict freedom from biochemical failure.Objective
This study aims to evaluate prostate-specific membrane antigen-positron emission tomography (PSMA-PET)-based sRT efficacy for postprostatectomy prostate-specific antigen (PSA) persistence or recurrence. Objectives include developing a random survival forest (RSF) model for predicting biochemical failure, comparing it with a Cox model, and assessing predictive accuracy over time. Multinational cohort data will validate the model's performance, aiming to improve clinical management of recurrent prostate cancer.Methods
This multicenter retrospective study collected data from 13 medical facilities across 5 countries: Germany, Cyprus, Australia, Italy, and Switzerland. A total of 1029 patients who underwent sRT following PSMA-PET-based assessment for PSA persistence or recurrence were included. Patients were treated between July 2013 and June 2020, with clinical decisions guided by PSMA-PET results and contemporary standards. The primary end point was freedom from biochemical failure, defined as 2 consecutive PSA rises >0.2 ng/mL after treatment. Data were divided into training (708 patients), testing (271 patients), and external validation (50 patients) sets for machine learning algorithm development and validation. RSF models were used, with 1000 trees per model, optimizing predictive performance using the Harrell concordance index and Brier score. Statistical analysis used R Statistical Software (R Foundation for Statistical Computing), and ethical approval was obtained from participating institutions.Results
Baseline characteristics of 1029 patients undergoing sRT PSMA-PET-based assessment were analyzed. The median age at sRT was 70 (IQR 64-74) years. PSMA-PET scans revealed local recurrences in 43.9% (430/979) and nodal recurrences in 27.2% (266/979) of patients. Treatment included dose-escalated sRT to pelvic lymphatics in 35.6% (349/979) of cases. The external outlier validation set showed distinct features, including higher rates of positive lymph nodes (47/50, 94% vs 266/979, 27.2% in the learning cohort) and lower delivered sRT doses (<66 Gy in 57/979, 5.8% vs 46/50, 92% of patients; P<.001). The RSF model, validated internally and externally, demonstrated robust predictive performance (Harrell C-index range: 0.54-0.91) across training and validation datasets, outperforming a previously published nomogram.Conclusions
The developed RSF model demonstrates enhanced predictive accuracy, potentially improving patient outcomes and assisting clinicians in making treatment decisions.
Date Issued
2024-09-20
Publication Type
Article
Subjects
AI
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ML
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PET
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PSMA-PET
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algorithm
•
algorithms
•
artificial intelligence
•
cancer
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deep learning
•
machine learning
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metastases
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oncologist
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positron emission tomography
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practical model
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practical models
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predictive analytics
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predictive model
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predictive models
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predictive system
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prostate
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prostate cancer
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prostate-specific membrane antigen
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prostate-specific membrane antigen–positron emission tomography
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prostatectomy
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radiography
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radiology
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radiotherapy
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salvage radiotherapy
Language(s)
en
Author(s)
Janbain, Ali
Farolfi, Andrea
Guenegou-Arnoux, Armelle
Romengas, Louis
Scharl, Sophia
Fanti, Stefano
Serani, Francesca
Peeken, Jan C
Katsahian, Sandrine
Strouthos, Iosif
Ferentinos, Konstantinos
Koerber, Stefan A
Vogel, Marco E
Combs, Stephanie E
Vrachimis, Alexis
Morganti, Alessio Giuseppe
Spohn, Simon Kb
Grosu, Anca-Ligia
Ceci, Francesco
Henkenberens, Christoph
Kroeze, Stephanie Gc
Guckenberger, Matthias
Belka, Claus
Bartenstein, Peter
Hruby, George
Emmett, Louise
Schmidt-Hegemann, Nina-Sophie
Zamboglou, Constantinos
Wiegel, Thomas
Additional Credits
Journal
JMIR Cancer
Publisher
JMIR Publications
ISSN
2369-1999
Access(Rights)
restricted
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