Evidence map›Paper›PMID 42014903›Full record

ArticleScientific reports2026

A novel nomogram incorporating lymphovascular invasion to predict lymph node invasion in high-risk prostate cancer.

Yige Yang, Yi Tang, Shuiping Yin

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yige YangDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Yi TangDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Shuiping YinDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China. yfy1101330@fy.ahmu.edu.cn.

Funding

National Natural Science Foundation of China 82102788Outstanding Scientific Research and Innovation Team for Male Genitourinary Diseases in Anhui Provincial Universities 2022AH010071
6 · The paper itself

Abstract

Accurate preoperative prediction of lymph node invasion (LNI) is essential for selecting candidates for extended pelvic lymph node dissection (ePLND) in high-risk prostate cancer. Although ePLND provides the most reliable nodal staging, it is associated with increased operative time and postoperative complications. Existing predictive models have limited validation in Asian populations and rarely incorporate lymphovascular invasion (LVI). We retrospectively analyzed 325 patients with high-risk prostate cancer who underwent radical prostatectomy with ePLND between 2019 and 2023. Multivariable logistic regression was used to identify independent predictors of LNI and to develop a novel nomogram integrating LVI with established clinical parameters. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, clinical impact curves, and decision curve analysis, and compared with the Briganti 2012, Briganti 2017, and MSKCC models. LVI, seminal vesicle invasion, biopsy Gleason grade group, clinical stage, and preoperative prostate-specific antigen were independent predictors of LNI. The proposed model demonstrated discrimination (AUC = 0.89; 95% CI 0.85–0.93) and good calibration. At a 7% risk threshold, 31.3% of ePLND procedures could be avoided while missing only 1.7% of LNI cases. Incorporation of LVI significantly improves preoperative prediction of nodal metastasis and may support more individualized surgical decision-making.

Indexed as

Lymphatic MetastasisLymph NodesNomogramsProstatic NeoplasmsAgedHumansLogistic ModelsLymph Node ExcisionMaleMiddle AgedNeoplasm GradingNeoplasm InvasivenessNeoplasm StagingProstatectomyRetrospective StudiesROC CurveePLNDlymph node invasionlymphovascular invasionnomogramProstate cancerradical prostatectomy

Identifiers

PMID42014903
PMCPMC13265955

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.