journal article·2025-12-09Open access

Development of a Nomogram for Predicting Lymphovascular Invasion at Initial Transurethral Resection of Bladder Tumors

Takatoshi Somoto, Takanobu Utsumi, Rino Ikeda, Naoki Ishitsuka, Takahide Noro, Yuta Suzuki, Shota Iijima, Yuka Sugizaki, Ryo Oka, Takumi Endo, Naoto Kamiya, Nobuyuki Hiruta, Hiroyoshi Suzuki

Applied Sciences· Volume 15· Issue 24

Abstract

Lymphovascular invasion (LVI) is a potent yet underutilized prognostic marker in bladder cancer, particularly in non–muscle-invasive disease (NMIBC). We aimed to develop and internally validate a predictive nomogram to estimate the probability of LVI at initial transurethral resection of bladder tumors (TURBT), utilizing preoperative clinical parameters. In this retrospective cohort study, 413 patients with histologically confirmed urothelial carcinoma who underwent initial TURBT were included. LVI was identified histologically in 9.2% of cases. Univariate and multivariate logistic regression, in conjunction with the least absolute shrinkage and selection operator modeling, revealed eight significant predictors: papillary architecture, Box–Cox–transformed tumor size, urinary cytology classification, age ≥ 75 years, pedunculated morphology, gender, hydronephrosis, and tumor multiplicity. The resulting nomogram demonstrated excellent discriminative performance, with an AUC of 0.888 in the training cohort and 0.827 in the validation cohort, and exhibited good calibration based on weighted plots. This model facilitates individualized prediction of LVI using routinely available clinical data. Early detection of LVI may inform risk-adapted management strategies, including repeat resection, or intensified surveillance in patients with bladder cancer. The model complements existing predictive frameworks and can contribute to more personalized and effective bladder cancer care.
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Research context

OpenAlex topics: Bladder and Urothelial Cancer Treatments · Prostate Cancer Diagnosis and Treatment · Esophageal Cancer Research and Treatment

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Urology, Toho University Sakura Medical Center, Sakura 285-8741, Japan

Department of Surgical Pathology, Toho University Sakura Medical Center, Sakura 285-8741, Japan

Funding

Japan Society for the Promotion of Science

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