ArticleEuropean journal of nuclear medicine and molecular imaging2023
Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06828263 (Prospective Evaluation of Imaging Response Biomarkers During [177Lu]Lu-PSMA in Metastatic Castration-resistant Prostate Cancer), which is not on this map. Cited by 11 papers.
What it found
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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.
Prospective Evaluation of Imaging Response Biomarkers During [177Lu]Lu-PSMA in Metastatic Castration-resistant Prostate Cancer
Who cites it
11 citing papers in PubMed, 22 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
- Automated lesion tracking and treatment response classification on [European journal of nuclear medicine and molecular imaging · 2026Article
- Automatic lesion segmentation in ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT: added value of PET-guided SPECT in a bicentric study.EJNMMI research · 2026Article
- Standardizing PSMA PET/CT reporting in prostate cancer: from structured frameworks to AI-assisted interpretation.Abdominal radiology (New York) · 2026Review
- RECIP 1.0 more predictive of overall survival than PSMA PET progression criteria in biochemically recurrent prostate cancer.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
- Risk-adapted intensification therapy in high-risk prostate cancer: how relevant is the role of radiation dose.Radiation oncology (London, England) · 2025Article
- Deep learning (DL)-based advancements in prostate cancer imaging: Artificial intelligence (AI)-based segmentation ofPrecision radiation oncology · 2025Review
- 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
- PSMA-PET follow-up to assess response in patients not receiving PSMA therapy: Is there value beyond localization of disease?Theranostics · 2024Article
- How to design a theranostic trial?Endocrine oncology (Bristol, England) · 2024Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThis study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model.
methodsOne hundred ninety-nine patients were imaged with [
resultsTwenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96-7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23-11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen's k = 0.31).
conclusionRECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This 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.