Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast‐enhanced MRI

Abstract

Purpose: To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI. Methods: A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUC FSD ) and the maximum enhancement (ME FSD ) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K trans map and area under MR intensity enhancement curve AUC MR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d 1 and d 2 of K trans and AUC MR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day. Results: The tumor mean K trans and its heterogeneity measurements d 1 and d 2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUC FSD and ME FSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUC FSD and ME FSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d 1 and d 2 of K trans map were 87.5% and 100% at first and second post-treatment scan days, respectively. Conclusions: As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response. C

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

“…They describe the heterogeneity of the tumor margin or shape (Appendix S1). [27][28][29][30] Using these features, we found two distinct tumor growth patterns that had specific margin-related radiomic features that were predictive of tumor DT. Our major findings were: (i) for all ADCs, the combination of eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) had predictive ability for tumor DT; (ii) the majority of subjects were assigned to the GP I group (n = 41) because they had gradually growing tumors without a temporary decrease in tumor volume, and the finally selected radiomic features for predicting tumor DT in this group were similar to those found for total subjects, namely surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5).…”

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study

“…Previous studies have reported that d 1 and d 2 of K trans map might reflect the tumor heterogeneity differences between low and high-grade glioma (Rose et al , 2009). In one of our recent works, the d 1 and d 2 of K trans map were found to be useful in determining the bevacizumab therapeutic effect (Wang et al , 2016). …”

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications

“…Advantages of fractal dimension are that it is relatively stable, less susceptible to noise than other features, and can be used for longitudinal assessment in a single patient [32]. Another feature of interest is the recently developed fractal signature dissimilarity method, which has been suggested as a novel image texture analysis technique [33]. In that study, the fractal signature dissimilarity method was used to quantitatively assess contrast agent uptake heterogeneity dynamics, indicating a potential role in monitoring the early response to anti-angiogenesis treatment [33].…”