ArticleAbdominal radiology (New York)2022
An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 11 citations in OpenAlex.
- Von Hippel-Lindau disease: pathophysiological and clinical advances.Familial cancer · 2026Review
- Impact of tumor size and sex on diagnostic discrepancies in renal mass imaging.Scientific reports · 2025Article
- RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning.BMC medical informatics and decision making · 2024Article
- Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.Journal of magnetic resonance imaging : JMRI · 2024Article
- Article
- CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome.Clinical radiology · 2024Article
- Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma.Current issues in molecular biology · 2024Review
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI.Abdominal radiology (New York) · 2024Article
- Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses.European urology open science · 2023Article
- Review
Corrections and comments
- Commented on byImaging.2023
Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
purposeUpfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors. MATERIALS AND
methodsA total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT < = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).
resultsThe average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).
conclusionHere we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.
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