ArticleDiagnostics (Basel, Switzerland)2023
Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of Intraoperative
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.Nuclear medicine and molecular imaging · 2026Review
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics.Journal of clinical medicine · 2026Review
- Computed Tomography Diagnostic Reference Levels Worldwide and in Jordan: Implications for Patient Dose Reduction.Drug, healthcare and patient safety · 2026Review
- Intraoperative technologies to assess margin status during radical prostatectomy - a narrative review.Prostate cancer and prostatic diseases · 2025Review
- Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer-State of the Art.Diagnostics (Basel, Switzerland) · 2024Article
- Comparative Performance ofDiagnostics (Basel, Switzerland) · 2024Article
- Comparison of Digital versus AnalogDiagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative
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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.