ArticleEuropean journal of nuclear medicine and molecular imaging2022
Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians.
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 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- The diagnostic value of radiomics-based machine learning for lymph node metastasis in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Standardizing PSMA PET/CT reporting in prostate cancer: from structured frameworks to AI-assisted interpretation.Abdominal radiology (New York) · 2026Review
- coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026Article
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- Context-level machine learning to improve the identification of lymph node and bone metastases in prostate cancer patients using [EJNMMI physics · 2025Article
- Deep-learning-based software versus experienced nuclear medicine physician to read [European journal of nuclear medicine and molecular imaging · 2025Article
- Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis.World journal of clinical oncology · 2025Review
- A fully automated AI-based method for tumour detection and quantification on [EJNMMI physics · 2025Article
- Artificial intelligence in prostate cancer.Chinese medical journal · 2025Review
- PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.Journal of cancer research and clinical oncology · 2025Review
- PSMA PET/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms.European journal of nuclear medicine and molecular imaging · 2025Article
- Clinical value of [Theranostics · 2025Article
- A convolutional neural network-based system for fully automatic segmentation of whole-body [European journal of nuclear medicine and molecular imaging · 2024Article
- Patterns of metastatic spread and tumor burden in unselected cancer patients using PET imaging: Implications for the oligometastatic spectrum theory.Clinical and translational radiation oncology · 2024Article
- Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review.Journal of clinical medicine · 2023Review
- 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
- Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation.Frontiers in nuclear medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study was to develop and validate an artificial intelligence (AI)-based method using convolutional neural networks (CNNs) for the detection of pelvic lymph node metastases in scans obtained using [
methods[
resultsThe sensitivity of the AI method for detecting pelvic lymph node metastases was 82%, and the corresponding sensitivity for the human readers was 77% on average. The average number of false positives was 1.8 per patient. A total of 5-17 false negative lesions in the whole cohort were found, depending on which reader was used as a reference. The method is available for researchers at www.recomia.org .
conclusionThis study shows that AI can obtain a sensitivity on par with that of physicians with a reasonable number of false positives. The difficulty in achieving high inter-observer sensitivity emphasizes the need for automated methods. On the road to qualifying AI tools for clinical use, independent validation is critical and allows performance to be assessed in studies from different hospitals. Therefore, we have made our AI tool freely available to other researchers.
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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.