ArticleEuropean journal of nuclear medicine and molecular imaging2021
Uncovering the invisible-prevalence, characteristics, and radiomics feature-based detection of visually undetectable intraprostatic tumor lesions in
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Radiomics vs radiologist in prostate cancer. Results from a systematic review.World journal of urology · 2023Pooled it
- Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.European radiology experimental · 2022Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Transrectal Ultrasound-Guided Prostate Biopsy with and without PSMA-PET-Targeted Prostate Biopsy for Prostate Cancer Diagnosis.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Trial
- [18F]F-DCFPyL PET/MRI radiomics for intraprostatic prostate cancer detection and metastases prediction using whole-gland segmentation.The British journal of radiology · 2025Trial
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Construction and external validation of radiomics models to detect primary prostate cancer with machine learning: a multicenter study based onJournal of the National Cancer Center · 2026Article
- A prescription-free, radiobiology-based framework for automated VMAT planning: A feasibility study in primary prostate cancer radiotherapy.Medical physics · 2026Article
- Application value of radiomics features based on PSMA PET/CT in diagnosis of clinically significant prostate cancer: a comparison between manual and automatic segmentation.BMC medical imaging · 2025Article
- Precision medicine in prostate cancer: individualized treatment through radiomics, genomics, and biomarkers.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Article
- Biomarkers in Localized Prostate Cancer: From Diagnosis to Treatment.International journal of molecular sciences · 2025Review
- Comparative Analysis ofAdvances in radiation oncology · 2025Article
- Significance of multi-task deep learning neural networks for diagnosing clinically significant prostate cancer in plain abdominal CT.Frontiers in oncology · 2025Article
- Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma.Cancers · 2024Review
- [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Review
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
- A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods inDiagnostics (Basel, Switzerland) · 2023Article
- Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPrimary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. METHODOLOGY: This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent
resultsIn the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1-6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8.
conclusionVisual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.
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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.