Can MRI contribute to pulmonary nodule analysis?

Chi Wan Koo, Aiming Lu, Edwin A. Takahashi, Curtis L. Simmons, Jennifer R. Geske, Dennis A Wigle, Tobias D Peikert

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJournal of Magnetic Resonance Imaging
DOIs
StateAccepted/In press - Jan 1 2018

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Lung
Neoplasms
Biomarkers
Carcinoid Tumor
Histology
Adenocarcinoma
Logistic Models
Fats
Odds Ratio
Prospective Studies
Muscles

Keywords

  • biomarker
  • magnetic resonance imaging
  • nodule
  • pulmonary

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Can MRI contribute to pulmonary nodule analysis? / Koo, Chi Wan; Lu, Aiming; Takahashi, Edwin A.; Simmons, Curtis L.; Geske, Jennifer R.; Wigle, Dennis A; Peikert, Tobias D.

In: Journal of Magnetic Resonance Imaging, 01.01.2018.

Research output: Contribution to journalArticle

Koo, Chi Wan ; Lu, Aiming ; Takahashi, Edwin A. ; Simmons, Curtis L. ; Geske, Jennifer R. ; Wigle, Dennis A ; Peikert, Tobias D. / Can MRI contribute to pulmonary nodule analysis?. In: Journal of Magnetic Resonance Imaging. 2018.
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title = "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.",
keywords = "biomarker, magnetic resonance imaging, nodule, pulmonary",
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T1 - Can MRI contribute to pulmonary nodule analysis?

AU - Koo, Chi Wan

AU - Lu, Aiming

AU - Takahashi, Edwin A.

AU - Simmons, Curtis L.

AU - Geske, Jennifer R.

AU - Wigle, Dennis A

AU - Peikert, Tobias D

PY - 2018/1/1

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N2 - 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.

AB - 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.

KW - biomarker

KW - magnetic resonance imaging

KW - nodule

KW - pulmonary

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