ReviewJournal of personalized medicine2024
The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.
Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exosomal Liquid Biopsy in Prostate Cancer: A Systematic Review of Biomarkers for Diagnosis, Prognosis, and Treatment Response.International journal of molecular sciences · 2025Pooled it
- Feasibility study on the implementation of a mobile high-resolution PET/CT scanner for surgical specimens: exploring clinical applications and practical considerations.European journal of nuclear medicine and molecular imaging · 2025Trial
- Article
- Article
- Integrative analysis of urinary microRNAs for prostate cancer detection: A proof-of-concept study.Translational oncology · 2026Article
- Evolving Treatments and Resistance Mechanisms in Prostate Cancer Therapeutics.ACS pharmacology & translational science · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- PSMA PET/CT improves precision diagnosis, treatment, and clinical decision-making in prostate cancer.American journal of translational research · 2026Review
- Review
- Future opportunities and nuances with the use of PSMA PET in prostate cancer (MD PET 1).Theranostics · 2026Review
- Predicting recurrence of prostate cancer after radical treatment using AI models based on PET/CT radiomics: a dual-center study.Frontiers in oncology · 2026Article
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- The molecular aspects in prostatic cancer, unraveling the role of CDKs in prostate cancer progression.Discover oncology · 2025Review
- A multimodal automated deep learning-based model for predicting biochemical recurrence of prostate cancer following prostatectomy from baseline MRI, Presurgical clinical covariates.Clinical imaging · 2025Article
- Synergizing Liquid Biopsy and Hybrid PET Imaging for Prognostic Assessment in Prostate Cancer: A Focus Review.Biomolecules · 2025Review
- IntegratingCancers · 2025Article
- Transforming Prostate Cancer Care: Innovations in Diagnosis, Treatment, and Future Directions.International journal of molecular sciences · 2025Review
- Reversing Epigenetic Dysregulation in Neurodegenerative Diseases: Mechanistic and Therapeutic Considerations.International journal of molecular sciences · 2025Review
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular imaging is a key tool in the diagnosis and treatment of prostate cancer (PCa). Magnetic Resonance (MR) plays a major role in this respect with nuclear medicine imaging, particularly, Prostate-Specific Membrane Antigen-based, (PSMA-based) positron emission tomography with computed tomography (PET/CT) also playing a major role of rapidly increasing importance. Another key technology finding growing application across medicine and specifically in molecular imaging is the use of machine learning (ML) and artificial intelligence (AI). Several authoritative reviews are available of the role of MR-based molecular imaging with a sparsity of reviews of the role of PET/CT. This review will focus on the use of AI for molecular imaging for PCa. It will aim to achieve two goals: firstly, to give the reader an introduction to the AI technologies available, and secondly, to provide an overview of AI applied to PET/CT in PCa. The clinical applications include diagnosis, staging, target volume definition for treatment planning, outcome prediction and outcome monitoring. ML and AL techniques discussed include radiomics, convolutional neural networks (CNN), generative adversarial networks (GAN) and training methods: supervised, unsupervised and semi-supervised learning.
Indexed as
Identifiers
What OpenQuestion holds
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.