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Can MRI contribute to pulmonary nodule analysis?
Author(s) -
Koo Chi Wan,
Lu Aiming,
Takahashi Edwin A.,
Simmons Curtis L.,
Geske Jennifer R.,
Wigle Dennis,
Peikert Tobias
Publication year - 2019
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26587
Subject(s) - medicine , malignancy , effective diffusion coefficient , nuclear medicine , intraclass correlation , radiology , hounsfield scale , solitary pulmonary nodule , magnetic resonance imaging , nodule (geology) , positron emission tomography , standardized uptake value , pathology , computed tomography , clinical psychology , paleontology , biology , psychometrics
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 T 2 ‐weighted imaging, T 1 ‐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), T 1 , T 2 , T 1 and T 2 normalized to muscle (T 1 /M and T 2 /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 μm 2 /ms predicted malignancy. ADC, T 1 , and T 2 together predicted malignancy ( P = 0.003). No MRI parameter distinguished primary from metastatic neoplasms. T 2 predicted PET positivity ( P = 0.016). T 2 and T 1 /M correlated with SUV ( P < 0.05). Of 18 PET‐negative malignant nodules, 12 (67%) had an ADC ≥1.3 μm 2 /ms. With the exception of T 2 , all noncontrast MRI parameters distinguished adenocarcinomas from carcinoid tumors ( P < 0.05). T 1 , T 2 , T 1 /M, and T 2 /M correlated with HU and therefore can predict nodule density. Combined with ADC, washout enhancement, arrival time (AT), peak enhancement intensity (PEI), K trans , K ep , V e 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.