ArticleBiomedical engineering online2024
Early diagnostic model of pyonephrosis with calculi based on radiomic features combined with clinical variables.
Article in Biomedical engineering online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Time to Decompression in Obstructive Urosepsis from Ureteral Calculi: Thresholds, Initial Diversion, and Early Biomarkers: A Systematic Review.Journal of clinical medicine · 2025Review
- Development and validation of a deep learning-based automated computed tomography image segmentation and diagnostic model for infectious hydronephrosis: a retrospective multicentre cohort study.EClinicalMedicine · 2025Article
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6 authors.
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Abstract
objectiveThis retrospective aims to develop a comprehensive predictive model based on CT radiomic features and clinical parameters, facilitating early preoperative diagnosis of pyonephrosis.
methodsClinical and radiological data from 311 patients treated for upper urinary tract stones with obstructive pyelohydronephrosis, between January 2018 and May 2023, were retrospectively collected. Univariate and multivariate logistic regression analyses were conducted on clinical data to identify independent risk factors for pyonephrosis. A clinical model was developed using logistic regression. The 3D Slicer software was employed to manually delineate the region of interest (ROI) in the preoperative CT images, corresponding to the area of pyelohydronephrosis, for feature extraction. The optimal radiomic features were selected to construct radiomic models and calculate the radiomic score (Radscore). Subsequently, a combined clinical-radiomic model-the nomogram-was established by integrating the Radscore with independent risk factors.
resultsUnivariate and multivariate logistic regression analyses identified cystatin C, Hounsfield Unit (HU) of pyonephrosis, history of ipsilateral urological surgery, and positive urine culture as independent risk factors for pyonephrosis (P < 0.05). Fourteen optimal radiomic features were selected from CT images to construct four radiomic models, with the Naive Bayes model demonstrating the best predictive performance in both training and validation sets. In the training set, the AUCs for the clinical model, radiomic model, and nomogram were 0.902, 0.939, and 0.991, respectively; in the validation set, they were 0.843, 0.874, and 0.959. Both calibration and decision curves showed good agreement between the predicted probabilities of the nomogram and the actual occurrences.
conclusionThe nomogram, constructed from CT radiomic features and clinical variables, provides an effective non-invasive predictive tool for pyonephrosis, surpassing both clinical and radiomic models.
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