Intravoxel incoherent motion and ADC measurements for differentiating benign from malignant thyroid nodules: utilizing the most repeatable region of interest delineation at 3.0 T

Song, Minghui; Yue, Yunlong; Jin, Yichao; Guo, Juan; Zuo, Lili; Hong, Peng; Chan, Queenie

doi:10.1186/s40644-020-0289-2

Cited by 21 publications

(17 citation statements)

References 42 publications

(55 reference statements)

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“…IVIM-DWI is a non-invasive technique that shows superiority in reflecting tumor cellularity and perfusion without requiring a contrast agent. It has already been applied in the differentiation of lung nodules ( 29 ), thyroid nodules ( 30 ), and breast ( 31 ) and brain tumors ( 32 ) with good diagnostic performance. To our best knowledge, no prostate study with a large sample size has been reported to determine the value of IVIM to quantitatively distinguish PCa from non-cancerous tissues and identify the tumor Gleason grades, in the background of IVIM becoming a research hotspot in whole-body tumors.…”

Section: Discussionmentioning

confidence: 99%

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Xie³

et al. 2020

Front. Oncol.

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Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa. Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (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: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D * value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D * , and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D * (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76). Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

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Section: Discussionmentioning

confidence: 99%

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Xie³

et al. 2020

Front. Oncol.

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“…Fine-needle aspiration is an invasive examination, and one-third of the results are inconclusive (3). With the rapid development of magnetic resonance imaging, some functional sequences have achieved better diagnostic performance, but this method has the disadvantages of being high cost and time-consuming (6). Computed tomography (CT) can be used to identify nodules, establish preoperative localization, provide information for the diagnosis, and assess the therapeutic effect.…”

Section: Introductionmentioning

confidence: 99%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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Background: This study aimed to investigate the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules.Methods: Patients with thyroid nodules and pathological surgical results who underwent preoperative DS-DECT were selected. Quantitative parameter-derived DS-DECT was applied to classify benign and malignant nodules. Then, machine learning and binary logistic regression analysis models were constructed using the DS-DECT quantitative parameters to distinguish between benign and malignant nodules. The receiver operating characteristic curve was used to assess the diagnostic performance. The DeLong test was used to compare the diagnostic efficacy. Results: One hundred and thirty patients with 139 confirmed thyroid nodules were involved in the study. The malignant group had a significantly higher iodine concentration nodule (arterial phase) (P=0.001), normalized iodine concentration (arterial phase) (P=0.002), iodine concentration difference (P<0.001), spectral curve slope (nonenhancement) (P=0.007), spectral curve slope (arterial phase) (P=0.001), effective atomic number (nonenhancement) (P<0.001), and effective atomic number (arterial phase) (P=0.039) than the benign group. The binary logistic regression analysis model had an AUC (area under the curve) of 0.76, a sensitivity of 0.821, and a specificity of 0.667. The machine learning model had an AUC of 0.86, a sensitivity of 0.822, specificity of 0.791 in the training cohort, an AUC of 0.84, a sensitivity of 0.727, and specificity of 0.750 in the testing cohort. Conclusions: Multiple quantitative parameters of DS-DECT combined with machine learning could differentiate between benign and malignant thyroid nodules.

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“…IVIM-DWI is a noninvasive technique that shows superiority in reflecting tumor cellularity and perfusion without the need of contrast agent. It had already been applied in the differentiation of thyroid nodules [ 22 ], breast [ 23 ], liver [ 24 ] and brain tumors [ 25 ] with good diagnostic performance. To our best knowledge, there is still no pulmonary study with large sample size to settle down the value of IVIM for quantitatively distinguishing lung cancer from benign tissues, in the background of IVIM becoming a research hotspot in the whole-body tumors.…”

Section: Discussionmentioning

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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Intravoxel incoherent motion and ADC measurements for differentiating benign from malignant thyroid nodules: utilizing the most repeatable region of interest delineation at 3.0 T

Cited by 21 publications

References 42 publications

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

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

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