Can MRI contribute to pulmonary nodule analysis?

Abstract

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.

Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis

“…Jiang et al ( 41 ) suggested that the nodule with a diameter smaller than 2 cm or a lower lung zone location would negatively affect the reproducibility. In addition, Koo et al ( 42 ) found that most nodules (74%) in their study were <2 cm, and nearly half of the lesions were in the lower lobes. The “3.0 T” MRI scanner showed a higher sensitivity for ADC than the “1.5 T” MRI scanner.…”

Diagnostic Performance and Reliability of Non-Enhanced Imaging Characterization Quotients for the Differentiation of Infectious and Malignant Pulmonary Nodules in Hematological Patients Using 3T MRI

“…A total of 6 patients could not be included in the final analysis, with only two not being able to be examined due to their poor general condition. T1 and T2 values normalized to muscles correlated significantly with the standard uptake values (SUV) from PET/CT images in a study by Koo et al that considered the differentiation of benign and malignant nodules [20]. In another study by Li et al, the qualitative assessment of the T1 and T2 signal intensity of nodules in relation to muscle and fat was used in the follow-up after cryoablation of lung tumors [21].…”

Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI

“…Many studies have already demonstrated the value of analyzing radiomic features in CT and MRI datasets in evaluating pulmonary nodules [ 27 , 28 , 29 , 30 , 31 , 32 ]. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets in a backward selection process, assessing pulmonary nodules through radiomics analysis is superior to that of one modality alone, even exceeding human readers’ performance.…”