scholarly journals Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhijun Geng ◽  
Yunfei Zhang ◽  
Shaohan Yin ◽  
Shanshan Lian ◽  
Haoqiang He ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Diagnostic Performance ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Characteristic Analysis ◽  
Microstructural Heterogeneity ◽  
Diffusion Kurtosis ◽  
And Diffusion

Purpose. To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer. Methods. A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance. Results. For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer ( p < 0.05 ). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and Kapp (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001 ). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer. Conclusion. The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

2020 ◽  
Vol 61 (9) ◽  
pp. 1228-1239
Author(s):  
Xiaodan Chen ◽  
Lin Lin ◽  
Jie Wu ◽  
Guang Yang ◽  
Tianjin Zhong ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Mean Diffusivity ◽  
Labeling Index ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
High Grade ◽  
Ki 67 ◽  
Diffusion Kurtosis ◽  
Median Maximum

Background Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. Purpose To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. Material and Methods A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann–Whitney U test, Kruskal–Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. Results Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( P < 0.05). Conclusion The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.

scholarly journals IMG-14. DEVELOPING A PREDICTIVE GRADING MODEL FOR CHILDREN WITH GLIOMAS BASED ON DIFFUSION KURTOSIS IMAGING METRICS: ACCURACY AND CLINICAL CORRELATIONS WITH SURVIVAL

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii357-iii358
Author(s):  
Ioan Paul Voicu ◽  
Antonio Napolitano ◽  
Alessia Carboni ◽  
Lorenzo Lattavo ◽  
Andrea Carai ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
High Grade Glioma ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Free Survival ◽  
Kaplan Meier ◽  
Model Predictions ◽  
Probability Prediction ◽  
Grading Model ◽  
Diffusion Kurtosis

Abstract PURPOSE To develop a predictive grading model based on diffusion kurtosis imaging (DKI) metrics in children affected by gliomas, and to investigate the clinical impact of the model via correlations with overall survival and progression-free survival. MATERIALS AND METHODS We retrospectively studied 59 children (33M, 26F, median age 7.2 years) affected by gliomas on a 3T magnet. Patients with tumor locations other than infratentorial midline were included. Conventional and DKI sequences were obtained. Mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps were obtained. Whole tumor volumes (VOIs) were segmented semiautomatically. Mean DKI values were calculated for each metric. The quantitative values from DKI-derived metrics were used to develop a predictive grading model with penalized logistic regression (glmnet package, R). Elasticnet regularization was used to avoid model overfitting. Fitted model coefficients from each metric were used to develop a probability prediction of a high-grade glioma (HGG). Grading accuracy of the resulting probabilities was tested with ROC analysis. Finally, model predictions were correlated to progression-free survival (PFS) with a Kaplan-Meier analysis. RESULTS The cohort included 46 patients with low-grade gliomas (LGG) and 13 patients with HGG. The developed model predictions yielded an AUC of 0.946 (95%CI: 0.890–1). Model predictions were significantly correlated with PFS (23.1 months for HGG vs 34.7 months for LGG, p&lt;0.004). CONCLUSION In our cohort, a DKI-based predictive model was highly accurate for pediatric glioma grading. DKI-based model predictions were significantly correlated with progression-free survival.

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