ArticleCanadian Urological Association journal = Journal de l'Association des urologues du Canada2025
A novel tool to predict lymph node metastasis in patients with prostate cancer based on clinical and 68Ga-PSMA PET/CT parameters.
Article in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- 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
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWe sought to develop a model that predicts lymph node invasion (LNI) in patients with intermediate- and high-risk prostate cancer incorporating preoperative clinical and
methodsA cohort of 413 consecutive patients diagnosed with prostate cancer who underwent
resultsOne-hundred sixty-three men (39%) were categorized as high-risk, 168 (41%) as unfavorable-intermediate-risk, and 82 (20%) as favorable-intermediate-risk. Thirty-one patients (7.5%) had LNI on final pathology. All underwent extended lymph node dissection. Clinical stage, the presence of PET-positive lymph nodes, and diameter of the largest PET-positive node were included in the final predictive model. Four different categories were defined for estimating the risk for LNI. Internal validation was completed after applying the four-tire classification on both the learning and validation groups and achieving similar results. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 97%, 54%, 15%, and 99%, respectively, and area under the ROC curve was 0.906 (95% confidence interval 0.83-0.95, p<0.001). Using a 5% cutoff as a threshold for performing lymph node dissection, only one patient with LNI on final pathology would have been classified erroneously as node negative, while 206 (50%) men would have been spared an unwarranted lymph node dissection.
conclusionsWe present a novel prediction model for LNI that incorporates clinical staging and molecular imaging data. Pending further validation, this model may improve the risk stratification and patient selection for lymph node dissection at time of radical prostatectomy.
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