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 (r2 = 0.874, p < 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1–4 (p ≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUCFD = 0.97 versus AUCADC = 0.77, p < 0.001).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.
Highlights
Angiogenesis is a hallmark of cancer and is closely intertwined with tumor development and metabolism [1, 2]
Based on our mathematical assumptions, we found a fractal relationship between blood flow and perfused tissue area in the form of A−peFrDf ∝ Qperf, which represents a power law scaling between the proximal, regulating part of the vascular tree and the distal, regulated tissue portion, i.e., the perfusion territory
Examples of the three distinct simulated tumor grades are shown in Fig. 2a–d together with the corresponding perfusion simulation and fractal dimension (FD) map
Summary
Angiogenesis is a hallmark of cancer and is closely intertwined with tumor development and metabolism [1, 2]. The dedifferentiation of tumor tissue is related to an “angiogenic switch” and ensuing changes in vascular architecture [3]. Such phenotypes of tumor microvascularization have been visualized, e.g., using contrast-enhanced ultrasound microscopy [4]. Blood vessel trees follow physiology-determined branching rules over a multitude of scales. This so-called scale invariance is a central characteristic of fractals. Since fractal analysis is based on pathophysiological principles of perfusion, it can be expected to reveal information on the underlying biological correlate of perfusion abnormalities.
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