ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2023
Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Molecular Imaging in Photon Radiotherapy.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Deep learning models built from PSMA PET of the primary tumor can predict synchronous and metachronous prostate cancer metastases.PloS one · 2026Article
- Artificial intelligence in prostate cancer.Chinese medical journal · 2025Review
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Update on PSMA-based Prostate Cancer Imaging.Seminars in nuclear medicine · 2024Review
- The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks onRadiation oncology (London, England) · 2024Article
- Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
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Authors and funding
21 authors at 7 institutions in 4 countries.
Funding
Abstract
purposeWith the increased use of focal radiation dose escalation for primary prostate cancer (PCa), accurate delineation of gross tumor volume (GTV) in prostate-specific membrane antigen PET (PSMA-PET) becomes crucial. Manual approaches are time-consuming and observer dependent. The purpose of this study was to create a deep learning model for the accurate delineation of the intraprostatic GTV in PSMA-PET.
methodsA 3D U-Net was trained on 128 different
resultsMedian DSCs were Freiburg: 0.82 (IQR: 0.73-0.88), Dresden: 0.71 (IQR: 0.53-0.75), MGH: 0.80 (IQR: 0.64-0.83) and DFCI: 0.80 (IQR: 0.67-0.84), respectively. Median sensitivity for CNN and expert contours were 0.88 (IQR: 0.68-0.97) and 0.85 (IQR: 0.75-0.88) (p = 0.40), respectively. GTV volumes did not differ significantly (p > 0.1 for all comparisons). Median specificity of 0.83 (IQR: 0.57-0.97) and 0.88 (IQR: 0.69-0.98) were observed for CNN and expert contours (p = 0.014), respectively. CNN prediction took 3.81 seconds on average per patient.
conclusionThe CNN was trained and tested on internal and external datasets as well as histopathology reference, achieving a fast GTV segmentation for three PSMA-PET tracers with high diagnostic accuracy comparable to manual experts.
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