ArticleEuropean journal of nuclear medicine and molecular imaging2022
Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 37 citations in OpenAlex.
- Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test-retest trial.La Radiologia medica · 2026Trial
- Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.Japanese journal of radiology · 2026Article
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- A computer-aided diagnosis tool in prostate cancer patients with biochemical recurrence using 18F-PSMA PET/CT imaging.Medical physics · 2026Article
- Development and validation of an Automated computed tomography segmentation model for thyroid nodules using a channel attention high-resolution network.Gland surgery · 2026Article
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Deep-learning-based software versus experienced nuclear medicine physician to read [European journal of nuclear medicine and molecular imaging · 2025Article
- Artificial intelligence in prostate cancer.Chinese medical journal · 2025Review
- Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [EJNMMI physics · 2025Article
- Evaluating the prognostic value of radiomics and clinical features in metastatic prostate cancer using [Physical and engineering sciences in medicine · 2025Article
- An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.Journal of imaging informatics in medicine · 2024Article
- The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks onRadiation oncology (London, England) · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- A convolutional neural network-based system for fully automatic segmentation of whole-body [European journal of nuclear medicine and molecular imaging · 2024Article
- Clinical Evaluation of Deep Learning for Tumor Delineation onJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans.Cancers · 2024Review
- Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [European journal of nuclear medicine and molecular imaging · 2023Article
- Quantitative [Scientific reports · 2023Article
- Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index.European journal of nuclear medicine and molecular imaging · 2023Article
- A review of artificial intelligence in prostate cancer detection on imaging.Therapeutic advances in urologyReview
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [
methodsThree hundred thirty-seven [
resultsAt the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLV
conclusionThe fully automated assessment of whole-body [
trial registrationThis study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
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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.