Application of whole-lesion intravoxel incoherent motion analysis using iZOOM DWI to differentiate malignant from benign thyroid nodules

Song, Minghui; Jin, Yichao; Guo, Juan; Zuo, Lili; Xie, Hong; Shi, Kun; Yue, Yunlong

doi:10.1177/0284185118813599

Cited by 9 publications

(7 citation statements)

References 33 publications

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“…D* reflects the contribution of perfusion to signal attenuation of the diffusion image (20). It has been reported that the D* value may be unreliable in the IVIM model for differential diagnosis and has poor measurement reproducibility (21). DKI is a non-Gaussian diffusion weighted analysis method.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules

Jiang

Chen²,

Huang³

et al. 2022

Front. Oncol.

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ObjectiveThis study aimed to compare the diagnostic capacity between IVIM and DKI in differentiating malignant from benign thyroid nodules.Material and MethodsThis study is based on magnetic resonance imaging data of the thyroid with histopathology as the reference standard. Spearman analysis was used to assess the relationship of IVIM-derived parameters D, f, D* and the DKI-derived parameters Dapp and Kapp. The parameters of IVIM and DKI were compared between the malignant and benign groups. Binary logistic regression analysis was performed to establish the diagnostic model, and receiver operating characteristic (ROC) curve analysis was subsequently performed. The DeLong test was used to compare the diagnostic effectiveness of different prediction models. Spearman analysis was used to assess the relationship of Ki-67 expression and parameters of IVIM and DKI.ResultsAmong the 93 nodules, 46 nodules were malignant, and 47 nodules were benign. The Dapp of DKI-derived parameter was related to the D (P < 0.001, r = 0.863) of IVIM-derived parameter. The Kapp of DKI-derived parameter was related to the D (P < 0.001, r = -0.831) of IVIM-derived parameters. The malignant group had a significantly lower D value (P < 0.001) and f value (P = 0.013) than the benign group. The malignant group had significantly higher Kapp and lower Dapp values (all P < 0.001). The D+f had an area under the curve (AUC) of 0.951. The Dapp+Kapp had an AUC of 0.943. The D+f+Dapp+Kapp had an AUC of 0.954. The DeLong test showed no statistical significance among there prediction models. The D (P = 0.007) of IVIM-derived parameters and Dapp (P = 0.045) of DKI-derived parameter were correlated to the Ki-67 expression.ConclusionsIVIM and DKI were alternative for each other in in differentiating malignant from benign thyroid nodules.

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Section: Discussionmentioning

confidence: 99%

Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules

Jiang

Chen²,

Huang³

et al. 2022

Front. Oncol.

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“…But in clinical circumstances, ss-EPI DWI of optic nerve suffers from partial-volume effects, magnetic susceptibility artifacts, chemical shift artifacts, and low image quality on account of the small dimension of the nerves and low resolution [ 8 ]. Recent studies have indicated that reduced field-of-view (rFOV) DWI provides outstanding improvements in image quality of the spinal cord [ 9 , 10 ], pancreas [ 11 ], prostate [ 12 , 13 ], breast [ 14 ], thyroid nodules [ 15 ], and optic nerve [ 16 ]. Literature has demonstrated that rFOV DWI showed an improvement in subjective image quality for optic nerve in the intraorbital segment compared with ss-EPI [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Reproducibility and feasibility of optic nerve diffusion MRI techniques: single-shot echo-planar imaging (EPI), readout-segmented EPI, and reduced field-of-view diffusion-weighted imaging

Zhou

Zhang

et al. 2022

BMC Med Imaging

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Background Diffusion-weighted imaging (DWI) is an essential technique for optic nerve diseases. However, the image quality of optic nerve DWI is decreased by the distortions and artifacts associated with conventional techniques. In order to establish this method as a critical tool in optic nerve diseases, reproducibility and feasibility of new technical and conventional approaches of DWI need to be systematically investigated. Methods DWIs were acquired using ss-EPI, readout-segmented EPI (rs-EPI) DWI, and reduced field-of-view (rFOV) DWI. 26 volunteers (mean age 31.2 years) underwent repeated MRI examinations in order to assess scan–rescan reproducibility and accuracy. The apparent diffusion coefficient (ADC) values (three ROIs were measured on each side) were determined to evaluate the reproducibility of each sequence and the differences between the three techniques. To quantify the geometric distortion artifacts, the length of optic nerve and the maximum angle of optic nerve were defined and compared to T2-weighted imaging. In addition, two readers evaluated four different aspects of image quality on 5-point Likert scales. Results rs-EPI DWI (ICCs: 0.916, 0.797 and 0.781) and rFOV DWI (ICCs: 0.850, 0.595 and 0.750) showed higher reproducibility (ICCs: ROI1, ROI2 and ROI3) of mean ADC value in all three ROIs than ss-EPI DWI (ICCs: 0.810, 0.442 and 0.379). The quantitative analysis of geometric distortion yielded a higher agreement of both rs-EPI DWI and rFOV DWI with T2-weighted imaging than ss-EPI. rs-EPI DWI (2.38 ± 0.90) and rFOV DWI (2.46 ± 0.58) were superior to ss-EPI DWI (1.58 ± 0.64) with respect to overall image quality and other aspects of image quality, each with P < 0.05. The mean ADC values of rFOV DWI were significantly lower than those of rs-EPI DWI and ss-EPI DWI in all three ROIs (P < 0.001). Conclusions Both rs-EPI DWI and rFOV-EPI DWI are suitable techniques for the assessment of diffusion restriction and provide significantly improved image quality compared with ss-EPI DWI. For methods using the same acquisition time, rFOV DWI is superior to ss-EPI DWI, while rs-EPI showed an overall superiority, although this technique took 47% longer to perform.

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“…The DWI signal varies with the magnetic gradient‐induced movement of water molecules within a tissue, which is a function of microstructural properties of the tissue such as cellular density and membrane integrity. Some studies have explored the role of DWI in differentiating malignant and benign thyroid nodules 20–24 . Moreover, there is recent evidence that the DWI‐based estimate of tissue average diffusivity, that is, the apparent diffusion coefficient (ADC), can be used to predict thyroid carcinoma aggressiveness 21 .…”

Section: Introductionmentioning

confidence: 99%

Texture and shape analysis of diffusion‐weighted imaging for thyroid nodules classification using machine learning

et al. 2021

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To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification. Patients and methods: In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases.To quantify the nodules'morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests. Results: The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a

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Application of whole-lesion intravoxel incoherent motion analysis using iZOOM DWI to differentiate malignant from benign thyroid nodules

Cited by 9 publications

References 33 publications

Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules

Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules

Reproducibility and feasibility of optic nerve diffusion MRI techniques: single-shot echo-planar imaging (EPI), readout-segmented EPI, and reduced field-of-view diffusion-weighted imaging

Texture and shape analysis of diffusion‐weighted imaging for thyroid nodules classification using machine learning

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