ArticleEuropean journal of nuclear medicine and molecular imaging2023
Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- M2 macrophage related genes predict prognosis and drug response in prostate cancer.Discover oncology · 2026Article
- Article
- Molecular Imaging in Photon Radiotherapy.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Oncologic outcomes of salvage radiotherapy and lymphadenectomy for positron emission tomography-positive lymph nodes in biochemical recurrence: A systematic review to inform treatment decisions.Prostate international · 2025Review
- Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.Neuro-oncology · 2024Article
- Review
- Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.Scientific reports · 2024Article
- Biomarker-driven molecular imaging probes in radiotherapy.Theranostics · 2024Review
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
Corrections and comments
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Authors and funding
31 authors.
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Abstract
purposeTo develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND
methodsConsecutive patients, who underwent
resultsAmong 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature.
conclusionThis is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.
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