Diffusion Kurtosis Imaging Combined With DWI at 3-T MRI for Detection and Assessment of Aggressiveness of Prostate Cancer

Wang, Xiangyu; Tu, Ning; Qin, Tao; Xing, Fu-qi; Wang, Panying; Wu, Guangyao

doi:10.2214/ajr.17.19249

Cited by 25 publications

(11 citation statements)

References 38 publications

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“…Compared to the monoexponential signal model widely used for conventional ADC mapping, the multicompartmental RSI models examined in this study showed substantially better fits (ie, lower fitting residuals) to DWI data from the prostate. This aligns with the results of previous studies that suggest the standard monoexponential model does not adequately describe the diffusion characteristics of the prostate 15,18,32,34,35 . While PI‐RADS specifies careful application of this model only over the range of b‐values where the DWI signal can be well‐approximated by a monoexponential decay (~100–1000 sec/mm 2 ), 3 synthetic DWI methods regularly use the monoexponential model to extrapolate DWI signal far beyond this range 36,37 .…”

Section: Discussionsupporting

confidence: 83%

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

Conlin

Feng

Rodríguez‐Soto

et al. 2020

Magnetic Resonance Imaging

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BackgroundMulticompartmental modeling outperforms conventional diffusion‐weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study TypeRetrospective.SubjectsForty‐six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field Strength/Sequence3T multishell diffusion‐weighted sequence.AssessmentMulticompartmental models with 2–5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model‐fitting residuals were calculated to quantify model complexity and goodness‐of‐fit. Tumor contrast‐to‐noise ratio (CNR) and tumor‐to‐background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal‐contribution maps.Statistical TestsAnalysis of variance (ANOVA) and two‐sample t‐tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T‐tests (α = 0.05) were also used to assess differences in compartmental signal‐fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.ResultsThe lowest BIC was observed from the 4‐compartment model, with optimal ADCs of 5.2e‐4, 1.9e‐3, 3.0e‐3, and >3.0e‐2 mm2/sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment‐1 signal maps than conventional DWI (P < 0.05) and increased with model order.Data ConclusionThe 4‐compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer.Level of Evidence 3Technical Efficacy Stage 3J. MAGN. RESON. IMAGING 2021;53:628–639.

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Section: Discussionsupporting

confidence: 83%

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

Conlin

Feng

Rodríguez‐Soto

et al. 2020

Magnetic Resonance Imaging

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“…The results have proved that IVIM ( 14 – 19 ) and DKI ( 20 – 24 ) may contribute to the detection and aggressiveness assessment of PCa. However, the results of IVIM and DKI for assessing the aggressiveness of PCa were various; several studies showed that IVIM ( 16 – 18 ) and DKI ( 22 – 24 ) were feasible to stratify the pathological grade of PCa, but a few studies found negative results of IVIM ( 15 ) and DKI ( 20 ) parameters in predicting the GS.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Monoexponential, Intravoxel Incoherent Motion, Kurtosis, and IVIM-Kurtosis Models for the Diagnosis and Aggressiveness Assessment of Prostate Cancer

et al. 2020

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Objective: This study aimed to compare the potential of monoexponential model (MEM), intravoxel incoherent motion (IVIM) model, kurtosis model, and IVIM–kurtosis model in the diagnosis and aggressiveness assessment of prostate cancer (PCa). Materials and Methods: Thirty-six patients were recruited. Diffusion-weighted images were acquired on a 3.0-T magnetic resonance imaging (MRI) system using 0 b values up to 2,000 s/mm 2 and analyzed using four models: MEM (ADC MEM ), IVIM ( D IVIM , D * IVIM , f IVIM ), kurtosis ( D kurtosis , K kurtosis ), and IVIM–kurtosis ( D IVIM−kurtosis , D * IVIM−kurtosis , f IVIM−kurtosis , D IVIM−kurtosis ) models. The values of these parameters were calculated and compared between PCa, benign prostatic hyperplasia (BPH), and prostatitis. Correlations between these parameters and the Gleason score (GS) of PCa were evaluated using the Pearson test. Results: Forty-five lesions were studied, including 18 PCa, 12 prostatitis, and 15 BPH lesions. The ADC MEM , D IVIM , f IVIM , D kurtosis , and D IVIM−kurtosis values were significantly lower and K kurtosis and K IVIM−kurtosis values were significantly higher in PCa compared with prostatitis and BPH. The area under the curve (AUC) of ADC MEM showed significantly higher values than that of f IVIM and K IVIM−kurtosis , but no statistical differences were found between the other parameters. The D * IVIM−kurtosis value correlated negatively and f IVIM−kurtosis and K IVIM−kurtosis values correlated positively with the GS. Conclusion: The MEM, IVIM, kurtosis, and IVIM–kurtosis models were all useful for the diagnosis of PCa, and the diagnostic efficacy seemed to be similar. The IVIM–kurtosis model may be superior to the MEM, IVIM, and kurtosis models in the grading of PCa.

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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: 99%

“…A recent study [20] suggested that to analyze a larger number of pixels may result in more reproducible results for the measured parameters. Furthermore, We chose consecutive three slices of ROIs (the slice with the prominent lesion section and the adjacent up and down slices), thus could decrease the deviation of sampling between cases and increase the reliability of results [16].…”

Section: Discussionmentioning

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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Diffusion Kurtosis Imaging Combined With DWI at 3-T MRI for Detection and Assessment of Aggressiveness of Prostate Cancer

Abstract: Both ADC and K can be used as quantitative parameters in detection and assessment of aggressiveness of PCa. The combination of DKI and DWI showed no significant superiority to DWI alone in detection and assessment of the aggressiveness of PCa.

Cited by 25 publications

References 38 publications

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

Comparative Study of Monoexponential, Intravoxel Incoherent Motion, Kurtosis, and IVIM-Kurtosis Models for the Diagnosis and Aggressiveness Assessment of Prostate Cancer

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

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