Comparison of mono-exponential, bi-exponential, kurtosis, and fractional-order calculus models of diffusion-weighted imaging in characterizing prostate lesions in transition zone

Liu, Guiqin; Yang, Lü; Dai, Yongming; Xue, Ke; Yi, Yangyang; Xu, Jianrong; Wu, Dongmei; Wu, Guangyu

doi:10.1007/s00261-020-02903-x

Cited by 14 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Severe artifacts were observed on sDWI in the pelvic tissue outside of the prostate, in particular for sDWI 500 , which makes the detection of metastasis outside of the prostate region difficult, Figure 5 . There are many ways to potentially improve the calculation of synthesized images including bi-exponential or multi-exponential modeling 23 . For example, RSIrs is based on a multi-exponential model and may synthesize images without introducing artifacts.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have yielded contradicting results on whether synthesized b-values are clinically interchangeable with acquired DWI (aDWI) images. Liu et al 23 compared various models, including the standard mono-exponential, for the detection of csPCa and concluded that nonlinear fitting with various b-values is superior to simpler models. In contrast, other studies reported better image quality for synthetic DWI (sDWI) with a similar tumor detection rate in comparison to acquired DWI 5,[24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of synthesized and acquired highb-value diffusion-weighted MRI for detection of prostate cancer

Kallis

Conlin

Zhong

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). To decrease scan time and improve signal-to-noise ratio, high b-value (>1000 s/mm2) images are often synthesized instead of acquired. Purpose: Qualitatively and quantitatively compare synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. Study Type: Retrospective Subjects: 151 consecutive patients who underwent prostate MRI and biopsy. Sequence: Axial DWI with b=0, 500, 1000, and 2000 s/mm2using a 3T clinical scanner using a 32-channel phased-array body coil Assessment: We synthesized DWI for b=2000 s/mm2via extrapolation based on monoexponential decay, using b=0 and b=500 s/mm2(sDWI500) and b=0, b=500, and b=1000 s/mm2(sDWI1000). Differences between sDWI and aDWI were evaluated within regions of interest (ROIs). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was also compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Statistical Tests: Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). Statistical significance was assessed using bootstrap difference (two-sided α=0.05). Results: Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were 46±35% for sDWI1000 and -67±24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. When considering the whole field of view, classification accuracy and qualitative image quality decreased notably for sDWI compared to aDWI and RSIrs. Data Conclusion: sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of synthesized and acquired highb-value diffusion-weighted MRI for detection of prostate cancer

Kallis

Conlin

Zhong

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, developing non-invasive imaging biomarkers to assist tumor grading on the basis of MR images holds clinical significance. Previous studies reported that the FROC parameters could effectively distinguish tumor grades in glioma ( 30 ), pediatric brain tumor ( 28 ), and prostate cancer ( 16 ). In this study, the high-grade lesions exhibited significantly lower ADC, β, D , and μ than those in the low-grade ones and D derived from the FROC model provided the best individual parameter performance in grading the tumors compared with the other parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Collectively, these parameters offer a multi-faceted characterization of cancerous tissues and can be used as a new class of biomarker in tumor diagnosis ( 11 – 14 ). Prior research proved that the FROC model could better reflect the complexity and heterogeneity of tissue microstructure than conventional diffusion model in gastric adenocarcinoma ( 15 ) and prostate lesions ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

Shao

Liu

et al. 2022

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

ObjectiveThe objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma.MethodsSixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons.ResultsOnly the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829).ConclusionThe parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.

show abstract

“…(2) Fractional-order calculus model: The parameter maps of FROC-DWI were calculated by the following fitting formula ( 21 , 24 ):…”

Section: Methodsmentioning

confidence: 99%

A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types

et al. 2022

View full text Add to dashboard Cite

ObjectiveTo evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression.MethodsA total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax.ResultsThe ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively).ConclusionThe parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.

show abstract

Comparison of mono-exponential, bi-exponential, kurtosis, and fractional-order calculus models of diffusion-weighted imaging in characterizing prostate lesions in transition zone

Cited by 14 publications

References 42 publications

Comparison of synthesized and acquired highb-value diffusion-weighted MRI for detection of prostate cancer

Comparison of synthesized and acquired highb-value diffusion-weighted MRI for detection of prostate cancer

Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types

Contact Info

Product

Resources

About