Dynamic contrast-enhanced MRI versus 18F-FDG PET/CT: Which is better in differentiation between malignant and benign solitary pulmonary nodules?

Feng, Feng; Fu, Qiang; Shen, Ai; Dong-hui, Shi; Fu, Aiyan; Li, Haiming; Zhang, Mingzhu; Xia, Ganlin; Cao, Peng

doi:10.21147/j.issn.1000-9604.2018.01.03

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…Our study is one of the few that investigated the capability of Tofts derived DCE parameters in distinguishing malignant and benign nodules. Unlike prior studies, our study demonstrated that none of the DCE measurements independently predicted malignancy and there was poor intra‐ and interobserver agreement. Since none of the prior studies included a substantial number of groundglass or part‐solid nodules that we examined, it is uncertain whether our findings reflect differences in our cohorts' composition.…”

Section: Discussioncontrasting

confidence: 99%

“…Our results confirm prior reports promoting MRI as a useful noninvasive means for imaging pulmonary nodules without the use of ionizing radiation . Akin to findings in our prior study and dissimilar to other studies, our current study showed that the ADC of malignant lesions is higher than that of benign lesions, even though our numeric ADC threshold is similar to other studies.…”

Section: Discussionsupporting

confidence: 84%

“…Our results confirm prior reports promoting MRI as a useful noninvasive means for imaging pulmonary nodules without the use of ionizing radiation. [27][28][29][30][31][32][33] Akin to findings in our prior study and dissimilar to other studies, 28 our current study showed that the ADC of malignant lesions is higher than that of benign lesions, even though our numeric ADC threshold is similar to other studies. Such phenomenon may be attributable to the fact that our study is one of the few that included all pulmonary nodule types, including lesions of lower cellularity such as part-solid and groundglass nodules in contrast to the cellular, solid lesions discussed in most prior studies.…”

Section: Discussioncontrasting

confidence: 77%

“…21 Magnetic resonance imaging (MRI) has been explored as an attractive potential alternative to CT due to the lack of ionizing radiation. 22 Recent technologic improvements have improved past issues such as low signal-to-noise ratio, susceptibility artifacts due to air-soft-tissue interfaces, and cardiac and respiratory artifacts, rendering MRI a viable substitute for CT. [23][24][25][26] Although recent studies have demonstrated the value of diffusion-weighted (DWI) and T 1 -weighted dynamic contrast-enhanced (DCE) imaging, [27][28][29][30][31][32][33] most studies included only solid nodules, were limited in power, focused on one specific sequence, and/or involved a 1.5T system. Our aim was to simultaneously investigate multiple MRI parameters' abilities to evaluate all types of pulmonary nodules using a 3T MRI system.…”

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confidence: 99%

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Can MRI contribute to pulmonary nodule analysis?

Koo

Takahashi

et al. 2018

Magnetic Resonance Imaging

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Background There is no accurate method distinguishing different types of pulmonary nodules. Purpose To investigate whether multiparametric 3T MRI biomarkers can distinguish malignant from benign pulmonary nodules, differentiate different types of neoplasms, and compare MRI‐derived measurements with values from commonly used noninvasive imaging modalities. Study Type Prospective. Subjects Sixty‐eight adults with pulmonary nodules undergoing resection. Sequences Respiratory triggered diffusion‐weighted imaging (DWI), periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) fat saturated T2‐weighted imaging, T1‐weighted 3D volumetric interpolated breath‐hold examination (VIBE) using CAIPIRINHA (controlled aliasing in parallel imaging results in a higher acceleration). Assessment/Statistics Apparent diffusion coefficient (ADC), T1, T2, T1 and T2 normalized to muscle (T1/M and T2/M), and dynamic contrast enhancement (DCE) values were compared with histology to determine whether they could distinguish malignant from benign nodules and discern primary from secondary malignancies using logistic regression. Predictability of primary neoplasm types was assessed using two‐sample t‐tests. MRI values were compared with positron emission tomography / computed tomography (PET/CT) to examine if they correlated with standardized uptake value (SUV) or CT Hounsfield unit (HU). Intra‐ and interreader agreements were assessed using intraclass correlations. Results Forty‐nine of 74 nodules were malignant. There was a significant association between ADC and malignancy (odds ratio 4.47, P < 0.05). ADC ≥1.3 μm2/ms predicted malignancy. ADC, T1, and T2 together predicted malignancy (P = 0.003). No MRI parameter distinguished primary from metastatic neoplasms. T2 predicted PET positivity (P = 0.016). T2 and T1/M correlated with SUV (P < 0.05). Of 18 PET‐negative malignant nodules, 12 (67%) had an ADC ≥1.3 μm2/ms. With the exception of T2, all noncontrast MRI parameters distinguished adenocarcinomas from carcinoid tumors (P < 0.05). T1, T2, T1/M, and T2/M correlated with HU and therefore can predict nodule density. Combined with ADC, washout enhancement, arrival time (AT), peak enhancement intensity (PEI), Ktrans, Kep, Ve collectively were predictive of malignancy (P = 0.012). Combined washin, washout, time to peak (TTP), AT, and PEI values predicted malignancy (P = 0.043). There was good observer agreement for most noncontrast MRI biomarkers. Data Conclusion MRI can contribute to pulmonary nodule analysis. Multiparametric MRI might be better than individual MRI biomarkers in pulmonary nodule risk stratification. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionsupporting

confidence: 84%

Section: Discussioncontrasting

confidence: 77%

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confidence: 99%

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Can MRI contribute to pulmonary nodule analysis?

Koo

Takahashi

et al. 2018

Magnetic Resonance Imaging

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“…According to analysis, such result was perhaps caused by active tumor vascular proliferation and a sharp increase in microvessel density inside the malignant SPN [ 21 ]. The WR in the time-signal curve of the MRI image is relevant to interstitial diseases (resilience and collagenous fiber) [ 22 ]. The study result herein showed that there was no statistical difference in WR values in the time-signal curve of MRI images of the two groups of patients ( P > 0.05), which was consistent to the research result of Guan et al [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule

Feng¹,

Zhang²,

Zhu³

et al. 2021

Contrast Media & Molecular Imaging

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This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences ( P > 0.05 ) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group ( P < 0.01 ). The ADC value with different b values in the benign group was significantly lower than that of the malignant group ( P < 0.01 ). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

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