ArticleEuropean radiology2022
Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 3D fractal dimension analysis of CT imaging for microvascular invasion prediction in hepatocellular carcinoma.European radiology · 2026Article
- Fractal Dimension of High-Risk Neuroblastoma Vascularity in MRI Is Associated with Chemotherapy Response and Event-Free Survival.Radiology. Imaging cancer · 2026Article
- Fractal Analysis of Intramuscular Adipose Tissue on CT Serves as a Novel Imaging Biomarker for Metabolic Syndrome.International journal of medical sciences · 2026Article
- Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors.Journal of bone oncology · 2025Article
- Fractal analysis of peripheral blood neutrophil chromatin in patients infected with Trypanosoma cruzi and/or HIV correlates with left ventricle ejection fraction, T CD4+, and T CD8+ lymphocytes levels.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2025Article
- Prostate Cancer Microvascular Routes: Exploration and Measurement Strategies.Life (Basel, Switzerland) · 2023Review
- Article
- Can the Gleason score be predicted in patients with prostate cancer? A dynamic contrast-enhanced MRI,Diagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- The Fractal Viewpoint of Tumors and Nanoparticles.Current medicinal chemistry · 2023Review
- Differentiation of hepatocellular adenoma by subtype and hepatocellular carcinoma in non-cirrhotic liver by fractal analysis of perfusion MRI.Insights into imaging · 2022Article
- Article
- Predicting Model of Biochemical Recurrence of Prostate Carcinoma (PCa-BCR) Using MR Perfusion-Weighted Imaging-Based Radiomics.Technology in cancer research & treatmentArticle
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6 authors.
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Abstract
objectivesMultiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade.
methodsWe retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements.
resultsFractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2-5) cancer with a sensitivity of 91% (confidence interval [CI]: 83-96%) and a specificity of 86% (CI: 73-94%). FD correlated linearly with ISUP groups (r
conclusionFractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors. KEY POINTS: • In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1-4). • Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83-96%) and a specificity of 86% (73-94%). • Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading.
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