Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging?

Wan, Qing; Deng, Yingshi; Lei, Qiang; Bao, Yingying; Wang, Yuze; Zhou, Jiaxuan; Zou, Qiao; Li, Xinchun

doi:10.1007/s00330-018-5714-6

Cited by 46 publications

(58 citation statements)

References 27 publications

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“…In the study, five b values (200, 500, 1000, 1500, and 2000 mm 2 /s) were applied to acquire DKI imaging. Previous studies [11,16] have demonstrated that perfusion has prominent influence for low b values (usually < 200 mm 2 /s); nonetheless, too much high b values increase the chance of image distortion and susceptibility artifacts, especially for bowels. Therefore, considering acquisition time, we finally selected above five b values in our institution.…”

Section: Discussionmentioning

confidence: 97%

“…DKI and DWI data were analyzed using an in-house program written in MATLAB (version R2013b, MathWorks, MA, USA). The DKI parameter was fitted according to the following equation [10][11][12][13]:…”

Section: Imaging Analysismentioning

confidence: 99%

“…The ADC is the mean value obtained using all b values that were fitted with a conventional mono-exponential model according to the following equation [11,12]:…”

Section: Imaging Analysismentioning

confidence: 99%

“…Diffusion kurtosis imaging is an advanced DWI model that quantifies non-Gaussian behavior of diffusion, yielding additional parameters, apparent kurtosis coefficient (K app ), reflecting the deviation degree from the ideal Gaussian curve, and the corrected ADC value, which quantifies the non-Gaussian behavior of water molecular diffusion [10]. Recent years, DKI has been widely applied to the preoperative diagnosis, grading and postoperative surveillance of cancer [11][12][13]. Nevertheless, few studies applied DKI to evaluate the activity of autoimmune inflammation diseases, especially in the inflammatory bowel disease.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the inflammatory activity in Crohn’s disease using magnetic resonance diffusion kurtosis imaging

et al. 2019

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Objectives To explore the feasibility of diffusion kurtosis imaging (DKI) for evaluating inflammatory activity in Crohn's disease (CD). Materials and methods In all, 51 CD patients were included, who were performed with consecutive enteroscopy, MR and DKI (b values = 0-2000 mm 2 /s). The lesions of bowel segments were graded as inactive (0-2), mild (3-6), and moderatesevere group (> 6) based on simplified endoscopic activity score for Crohn's disease (SES-CD), The abilities of the parameters of DKI and DWI in grading different activity lesions were compared. Results One hundred and twenty-seven bowel segments including inactive (15), mild (45) and moderate-severe (67) were analyzed. ADC (r = − 0.627, p < 0.001), D app (r = − 0.381, p < 0.001) and K app (r = 0.641, p < 0.001) were correlated with SES-CD. These parameters were significantly different among the three groups (all p < 0.001). ROC analysis found ADC had the highest accuracy (AUC = 0.884, p < 0.001) to differentiate inactive from active group with the threshold at 0.865 × 10 −3 mm 2 /s, which was slightly higher than K app (AUC = 0.867, p < 0.001) with the threshold at 0.645, and was obviously higher than D app (AUC = 0.726, p = 0.005). Similarly, ADC also had the highest accuracy (AUC = 0.846, p < 0.001) to differentiate inactive-mild from moderate-severe group with the threshold at 0.825 × 10 −3 mm 2 /s, and minimally higher than K app (AUC = 0.843, p < 0.001) with the threshold at 0.695, and obviously higher than D app (AUC = 0.690, p < 0.001). Conclusion DKI is feasible and comparable to conventional DWI for the evaluation of inflammatory activity in CD.

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Section: Discussionmentioning

confidence: 97%

Section: Imaging Analysismentioning

confidence: 99%

“…The ADC is the mean value obtained using all b values that were fitted with a conventional mono-exponential model according to the following equation [11,12]:…”

Section: Imaging Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the inflammatory activity in Crohn’s disease using magnetic resonance diffusion kurtosis imaging

et al. 2019

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“…As such, these parameters are important to be analyzed. Several studies had applied IVIM-DWI to discriminate lung cancer from benign lesions and demonstrated better or comparable diagnostic performance compared with traditional ADC value [ 7 – 9 ]. However, the diagnostic performances of IVIM-DWI derived parameters in the differentiation of lung tumors were not consistent and the application still remained debatable in the lung.…”

Section: Introductionmentioning

confidence: 99%

Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis

Liang

et al. 2020

BMC Cancer

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Background and objectives The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method. Materials and methods The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) . Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities. Results Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66). Conclusion IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC .

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Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors

Song

et al. 2020

Magnetic Resonance Imaging

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BackgroundThe accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear.PurposeTo compare the whole solid‐tumor volume histogram analysis of diffusion‐weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast‐enhanced MRI (DCE‐MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE‐MRI.Study TypeRetrospective.PopulationTwenty patients with BEOTs and 42 patients with MEOTs.Field Strength/Sequence1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE‐MRI.AssessmentHistogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (Ktrans), rate constant (kep), and extravascular extracellular volume fraction (ve).Statistical TestsThe Mann–Whitney U‐test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE‐MRI parameters.ResultsADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P < 0.05). K, Ktrans, kep, and ve were significantly lower in BEOTs than in MEOTs (P < 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with Ktrans yielded the highest AUC of 0.950. A weak inverse correlation was found between D and Ktrans (r = −0.320, P = 0.025) and between D and kep (r = −0.267, P = 0.037).Data ConclusionThe 10th percentile of Dk was the most valuable metric and Dk combined with Ktrans had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion‐related parameters derived from IVIM and DCE‐MRI.Level of Evidence: 4Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;52:257–268.

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Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging?

Cited by 46 publications

References 27 publications

Evaluating the inflammatory activity in Crohn’s disease using magnetic resonance diffusion kurtosis imaging

Evaluating the inflammatory activity in Crohn’s disease using magnetic resonance diffusion kurtosis imaging

Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis

Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors

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