⚡ Quick Summary
This study developed a random survival forest (RSF) model to predict biochemical failure after PSMA-PET-guided salvage radiotherapy (sRT) in recurrent prostate cancer patients. The model demonstrated robust predictive performance, potentially enhancing clinical decision-making and patient outcomes.
🔍 Key Details
- 📊 Dataset: 1029 patients from 13 medical facilities across 5 countries
- 🧩 Features used: PSMA-PET results, clinical characteristics
- ⚙️ Technology: Random survival forest (RSF) model
- 🏆 Performance: Harrell C-index range: 0.54-0.91
🔑 Key Takeaways
- 🔬 Salvage radiation therapy (sRT) is often the only curative option for biochemical recurrence after radical prostatectomy.
- 📈 The RSF model outperformed traditional Cox models in predicting biochemical failure.
- 🌍 Multinational cohort data was utilized for model validation, enhancing its reliability.
- 🧑⚕️ Clinical decisions were guided by PSMA-PET results, demonstrating the importance of imaging in treatment planning.
- 📉 The study found local recurrences in 43.9% and nodal recurrences in 27.2% of patients.
- 🔑 The model’s predictive accuracy could significantly improve patient management and outcomes.
- 🗺️ Study conducted across Germany, Cyprus, Australia, Italy, and Switzerland.
- 🆔 Ethical approval was obtained from all participating institutions.
📚 Background
Prostate cancer remains a significant health concern, particularly in patients who experience biochemical recurrence following radical prostatectomy. Salvage radiation therapy (sRT) is often the only curative option available, making accurate prediction of treatment outcomes crucial for effective patient management. The integration of advanced imaging techniques, such as PSMA-PET, has opened new avenues for enhancing treatment efficacy and tailoring patient-specific therapeutic strategies.
🗒️ Study
This multicenter retrospective study aimed to evaluate the efficacy of PSMA-PET-based sRT in predicting biochemical failure in recurrent prostate cancer patients. Data were collected from 1029 patients treated between July 2013 and June 2020 across 13 medical facilities in five countries. The study focused on developing and validating an RSF model to enhance predictive accuracy compared to traditional methods.
📈 Results
The analysis revealed that the RSF model demonstrated a Harrell C-index range of 0.54-0.91, indicating robust predictive performance across various datasets. Notably, the external validation set exhibited distinct features, such as a higher rate of positive lymph nodes and lower delivered sRT doses, underscoring the model’s adaptability and reliability in diverse clinical settings.
🌍 Impact and Implications
The findings from this study have significant implications for the management of recurrent prostate cancer. By utilizing machine learning techniques, the RSF model can provide clinicians with enhanced tools for predicting treatment outcomes, ultimately leading to improved patient care. This approach not only aids in clinical decision-making but also paves the way for personalized treatment strategies in oncology.
🔮 Conclusion
This study highlights the potential of machine learning in revolutionizing the prediction of biochemical outcomes in prostate cancer treatment. The development of the RSF model represents a significant advancement in the field, offering a promising avenue for improving patient outcomes and guiding clinical decisions. Continued research in this area is essential to further refine these predictive tools and enhance their applicability in clinical practice.
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A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study.
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.
Author: [‘Janbain A’, ‘Farolfi A’, ‘Guenegou-Arnoux A’, ‘Romengas L’, ‘Scharl S’, ‘Fanti S’, ‘Serani F’, ‘Peeken JC’, ‘Katsahian S’, ‘Strouthos I’, ‘Ferentinos K’, ‘Koerber SA’, ‘Vogel ME’, ‘Combs SE’, ‘Vrachimis A’, ‘Morganti AG’, ‘Spohn SK’, ‘Grosu AL’, ‘Ceci F’, ‘Henkenberens C’, ‘Kroeze SG’, ‘Guckenberger M’, ‘Belka C’, ‘Bartenstein P’, ‘Hruby G’, ‘Emmett L’, ‘Omerieh AA’, ‘Schmidt-Hegemann NS’, ‘Mose L’, ‘Aebersold DM’, ‘Zamboglou C’, ‘Wiegel T’, ‘Shelan M’]
Journal: JMIR Cancer
Citation: Janbain A, et al. A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study. A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study. 2024; 10:e60323. doi: 10.2196/60323