The Histogram Analysis of Intravoxel Incoherent Motion-Kurtosis Model in the Diagnosis and Grading of Prostate Cancer—A Preliminary Study

Abstract

ObjectivesThis study was conducted in order to explore the value of histogram analysis of the intravoxel incoherent motion-kurtosis (IVIM-kurtosis) model in the diagnosis and grading of prostate cancer (PCa), compared with monoexponential model (MEM).Materials and MethodsThirty patients were included in this study. Single-shot echo-planar imaging (SS-EPI) diffusion-weighted images (b-values of 0, 20, 50, 100, 200, 500, 1,000, 1,500, 2,000 s/mm2) were acquired. The pathologies were confirmed by in-bore MR-guided biopsy. The postprocessing and measurements were processed using the software tool Matlab R2015b for the IVIM-kurtosis model and MEM. Regions of interest (ROIs) were drawn manually. Mean values of D, D*, f, K, ADC, and their histogram parameters were acquired. The values of these parameters in PCa and benign prostatic hyperplasia (BPH)/prostatitis were compared. Receiver operating characteristic (ROC) curves were used to investigate the diagnostic efficiency. The Spearman test was used to evaluate the correlation of these parameters and Gleason scores (GS) of PCa.ResultsFor the IVIM-kurtosis model, D (mean, 10th, 25th, 50th, 75th, 90th), D* (90th), and f (10th) were significantly lower in PCa than in BPH/prostatitis, while D (skewness), D* (kurtosis), and K (mean, 75th, 90th) were significantly higher in PCa than in BPH/prostatitis. For MEM, ADC (mean, 10th, 25th, 50th, 75th, 90th) was significantly lower in PCa than in BPH/prostatitis. The area under the ROC curve (AUC) of the IVIM-kurtosis model was higher than MEM, without significant differences (z = 1.761, P = 0.0783). D (mean, 50th, 75th, 90th), D* (mean, 10th, 25th, 50th, 75th), and f (skewness, kurtosis) correlated negatively with GS, while D (kurtosis), D* (skewness, kurtosis), f (mean, 75th, 90th), and K (mean, 75th, 90th) correlated positively with GS. The histogram parameters of ADC did not show correlations with GS.ConclusionThe IVIM-kurtosis model has potential value in the differential diagnosis of PCa and BPH/prostatitis. IVIM-kurtosis histogram analysis may provide more information in the grading of PCa than MEM.

Microstructural parameters from DW‐MRI for tumour characterization and local recurrence prediction in particle therapy of skull‐base chordoma

“…In addition, unlike what was obtained from boxplots in which no information on time to recurrence is considered, a significant separation of patients according to recurrence-free survival probability was obtained from survival analyses, confirming that the information on time to recurrence is critical for identifying a SBC high-risk microstructure and that changes in microstructural parameters investigated in follow-ups will probably be more representative of treatment outcome.It should be noted that,among the various investigated metrics, only entropy values of estimated microstructural parameters were able to correctly predict the treatment outcome. This is in agreement with recent studies recognizing histogram-based heterogeneity parameters, such as entropy, skewness, and kurtosis, to be very promising imaging biomarkers [45][46][47][48][49][50] in discriminating different microenvironments that may be masked by more conventional metrics of central tendency (e.g., mean, median). Therefore, the possibility of extending these analyses by exploring additional histogram-based metrics (e.g., skewness or kurtosis) or methods (e.g., advanced texture analysis 47,51 ) that can capture the greater complexity of tissue heterogeneity could bring significant improvements on tumor microstructure estimation.…”

“…It should be noted that, among the various investigated metrics, only entropy values of estimated microstructural parameters were able to correctly predict the treatment outcome. This is in agreement with recent studies recognizing histogram‐based heterogeneity parameters, such as entropy, skewness, and kurtosis, to be very promising imaging biomarkers 45–50 in discriminating different microenvironments that may be masked by more conventional metrics of central tendency (e.g., mean, median). Therefore, the possibility of extending these analyses by exploring additional histogram‐based metrics (e.g., skewness or kurtosis) or methods (e.g., advanced texture analysis 47,51 ) that can capture the greater complexity of tissue heterogeneity could bring significant improvements on tumor microstructure estimation.…”

Histograms of computed tomography values in differential diagnosis of benign and malignant osteogenic lesions

“…Thus, we can quantitatively analyze lesion features at the pixel level and obtain more objective and accurate information on subtle differences. As an effective method of image data analysis, histogram analysis is widely used in the differentiation, classification, and qualitative diagnosis of benign and malignant lesions (8–15). The aim of the present study was to evaluate the usefulness of CT histogram analysis for differentiating benign osteoblastic lesions (BOLs) and malignant osteoblastic lesions (MOLs).…”