ArticleWorld journal of urology2025
Nomogram predicts risk score and likehood of orchiectomy in patients with testicular torsion: a multicenter retrospective study.
Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Is It Too Late? Machine Learning for Predicting Orchiectomy Versus Testicular Preservation in Acute Torsion.Diagnostics (Basel, Switzerland) · 2026Article
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9 authors.
Funding
Abstract
purposeTesticular torsion is a prevalent scrotal emergency associated with a significant risk of testicular loss, and there is no reliable scoring system available to assess the likelihood of orchiectomy following testicular torsion.
methodsClinical characteristics and surgical data of patients with testicular torsion from January 2015 to May 2024 were gathered from National Center for Children's Health (NCCH) and the Children's Hospital of Capital Institute of Pediatrics (CIPCH). Independent risk variables for testicular resection were evaluated by logistic regression analyses, leading to the establishment of a nomogram. The performance of the nomogram was assessed and validated using AUC, C-index, calibration curves, and DCA. Patients were classified based on their risk score obtained from the nomogram for clinical application.
resultsA total of 387 patients were included. Risk factors were identified by multifactorial analysis: symptoms duration, testicular blood flow, degree of torsion, and cryptorchidism. A nomogram exhibits an area under the ROC curve of 0.9305 and a C-index of 0.9310. A scoring system was developed accordingly, categorising patients into four groups; 88.7% of patients in the super-low and low-risk categories preserved their testes, while 78.6% in the super-high and high-risk categories underwent testicular resection.
conclusionThe strong concordance between predicted and actual outcomes suggests the model's utility in clinical decision-making. The nomogram demonstrates robust calibration and differentiation capabilities.
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