ArticleEuropean journal of nuclear medicine and molecular imaging2024
Deep learning-based whole-body characterization of prostate cancer lesions on [
Article in European journal of nuclear medicine and molecular imaging, 2024. 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, 13 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
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- Convolutional neural networks for prostate cancer detection, classification, and segmentation: A systematic review and bibliometric analysis.European journal of radiology open · 2026Review
- Repeatability of Semi-Quantitative and Volumetric Features from Artificial-Intelligence-Guided Lesion Segmentation onTomography (Ann Arbor, Mich.) · 2026Article
- AI-powered radiogenomics: imaging-driven diagnosis and discovery of cancer's molecular and cellular landscape.Molecular genetics and genomics : MGG · 2026Review
- Artificial intelligence in the early diagnosis of prostate cancer: from multimodal imaging to liquid biopsy.Frontiers in oncology · 2026Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions.Medical review (2021) · 2025Review
- The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks onRadiation oncology (London, England) · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe automatic segmentation and detection of prostate cancer (PC) lesions throughout the body are extremely challenging due to the lesions' complexity and variability in appearance, shape, and location. In this study, we investigated the performance of a three-dimensional (3D) convolutional neural network (CNN) to automatically characterize metastatic lesions throughout the body in a dataset of PC patients with recurrence after radical prostatectomy.
methodsWe retrospectively collected [
resultsOn the internal test set, the DSC, precision, recall, and F1-score values were 0.631, 0.961, 0.721, and 0.824, respectively. On the external test set, the corresponding values were 0.596, 0.888, 0.792, and 0.837, respectively. Our approach outperformed previous studies in segmenting and detecting metastatic lesions throughout the body. Tumor burden indicators derived from deep learning and ground truth showed strong correlation (R
conclusionOur 3D CNN accurately characterizes whole-body tumors in relapsed PC patients; its results are highly consistent with those of manual contouring. This automatic method is expected to improve work efficiency and to aid in the assessment of tumor burden.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.