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Chen Y, Han Q, Huang Z, Lyu M, Ai Z, Liang Y, Yan H, Wang M, Xiang Z. Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis. Front Surg 2022; 9:817443. [PMID: 36017515 PMCID: PMC9396547 DOI: 10.3389/fsurg.2022.817443] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/09/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis. Results A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D* (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in “nodules or masses” than that in “nodules.” Conclusions The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses. Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664
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Affiliation(s)
- Yirong Chen
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhiwei Huang
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Mo Lyu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
- School of Life Sciences, South China Normal University, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yuying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Haowen Yan
- Department of Oncology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Mengzhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
- Correspondence: Zhiming Xiang
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Liu H, Chen R, Tong C, Liang XW. MRI versus CT for the detection of pulmonary nodules: A meta-analysis. Medicine (Baltimore) 2021; 100:e27270. [PMID: 34678861 PMCID: PMC8542155 DOI: 10.1097/md.0000000000027270] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/31/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Computed tomography (CT) is the current gold standard for the detection of pulmonary nodules but has high radiation burden. In contrast, many radiologists tried to use magnetic resonance imaging (MRI) to replace CT because MRI has no radiation burden associated. Due to the lack of high-level evidence of comparison of the diagnostic accuracy of MRI versus CT for detecting pulmonary nodules, it is unknown whether CT can be replaced successfully by MRI. Therefore, the aim of this study was to compare the diagnostic accuracy of MRI versus CT for detecting pulmonary nodules. METHODS Electronic databases PubMed, EmBase, and Cochrane Library were systematically searched from their inception to September 2017 to identify studies in which CT/MRI was used to diagnose pulmonary nodules. According to true positive, true negative, false negative, and false positive extracted from the included studies, we calculate the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve (AUC) using Stata version 14.0 software (STATA Corp, TX). RESULTS A total of 8 studies involving a total of 653 individuals were included. The pooled sensitivity, specificity, PLR, NLR, and AUC were 0.91 (95% confidence interval [CI]: 0.80-0.96), 0.76 (95%CI: 0.58-0.87), 3.72 (95%CI: 2.05-6.76), 0.12 (95%CI: 0.06-0.27), and 0.91 (95%CI: 0.88-0.93) for MRI respectively, while the pooled sensitivity, specificity, PLR, NLR, and AUC for CT were 1.00 (95%CI: 0.95-1.00), 0.99 (95%CI: 0.78-1.00), 79.35 (95%CI: 3.68-1711.06), 0.00 (95%CI: 0.00-0.06), and 1.00 (95%CI: 0.99-1.00), respectively. Further, we compared the diagnostic accuracy of CT versus MRI and found that compared with MRI, CT shows statistically higher sensitivity (odds ratio [OR] for MRI vs CT: 0.91; 95%CI: 0.85-0.98; P value .010), specificity (OR: 0.82; 95%CI: 0.69-0.97; P value .019), PLR (OR: 0.29; 95%CI: 0.10-0.83; P value 0.02), AUC (OR: 0.91; 95%CI: 0.89-0.94; P value < .001), and lower NLR (OR: 8.72; 95%CI: 1.57-48.56; P value .013). CONCLUSION Our study suggested both CT and MRI have a high diagnostic accuracy in diagnosing pulmonary nodules, while CT was superior to MRI in sensitivity, specificity, PLR, NLR, and AUC, indicating that in terms of the currently available evidence, MRI could not replace CT in diagnosing pulmonary nodules.
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Enhancing the differentiation of pulmonary lymphoma and fungal pneumonia in hematological patients using texture analysis in 3-T MRI. Eur Radiol 2020; 31:695-705. [PMID: 32822054 PMCID: PMC7813714 DOI: 10.1007/s00330-020-07137-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/08/2020] [Accepted: 08/03/2020] [Indexed: 11/15/2022]
Abstract
Objectives To evaluate texture analysis in nonenhanced 3-T MRI for differentiating pulmonary fungal infiltrates and lymphoma manifestations in hematological patients and to compare the diagnostic performance with that of signal intensity quotients (“nonenhanced imaging characterization quotients,” NICQs). Methods MR scans were performed using a speed-optimized imaging protocol without an intravenous contrast medium including axial T2-weighted (T2w) single-shot fast spin-echo and T1-weighted (T1w) gradient-echo sequences. ROIs were drawn within the lesions to extract first-order statistics from original images using HeterogeneityCAD and PyRadiomics. NICQs were calculated using signal intensities of the lesions, muscle, and fat. The standard of reference was histology or clinical diagnosis in follow-up. Statistical testing included ROC analysis, clustered ROC analysis, and DeLong test. Intra- and interrater reliability was tested using intraclass correlation coefficients (ICC). Results Thirty-three fungal infiltrates in 16 patients and 38 pulmonary lymphoma manifestations in 19 patients were included. Considering the leading lesion in each patient, diagnostic performance was excellent for T1w entropy (AUC 80.2%; p < 0.005) and slightly inferior for T2w energy (79.9%; p < 0.005), T1w uniformity (79.6%; p < 0.005), and T1w energy (77.0%; p < 0.01); the best AUC for NICQs was 72.0% for T2NICQmean (p < 0.05). Intra- and interrater reliability was good to excellent (ICC > 0.81) for these parameters except for moderate intrarater reliability of T1w energy (ICC = 0.64). Conclusions T1w entropy, uniformity, and energy and T2w energy showed the best performances for differentiating pulmonary lymphoma and fungal pneumonia and outperformed NICQs. Results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters. Key Points • Texture analysis in nonenhanced pulmonary MRI improves the differentiation of pulmonary lymphoma and fungal pneumonia compared with signal intensity quotients. • T1w entropy, uniformity, and energy along with T2w energy show the best performances for differentiating pulmonary lymphoma from fungal pneumonia. • The results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.
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Koo CW, Lu A, Takahashi EA, Simmons CL, Geske JR, Wigle D, Peikert T. Can MRI contribute to pulmonary nodule analysis? J Magn Reson Imaging 2018; 49:e256-e264. [PMID: 30575193 DOI: 10.1002/jmri.26587] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 11/07/2018] [Accepted: 11/08/2018] [Indexed: 12/19/2022] Open
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.
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Affiliation(s)
- Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Aiming Lu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Jennifer R Geske
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Dennis Wigle
- Department of Surgery, Division of Thoracic Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Tobias Peikert
- Department of Medicine, Division of Pulmonary and Critical Care, Mayo Clinic, Rochester, Minnesota, USA
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Hochhegger B, Zanon M, Altmayer S, Pacini GS, Balbinot F, Francisco MZ, Dalla Costa R, Watte G, Santos MK, Barros MC, Penha D, Irion K, Marchiori E. Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review. Lung 2018; 196:633-642. [DOI: 10.1007/s00408-018-0156-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 08/28/2018] [Indexed: 12/19/2022]
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Feng F, Qiang F, Shen A, Shi D, Fu A, Li H, Zhang M, Xia G, Cao P. Dynamic contrast-enhanced MRI versus 18F-FDG PET/CT: Which is better in differentiation between malignant and benign solitary pulmonary nodules? Chin J Cancer Res 2018; 30:21-30. [PMID: 29545716 DOI: 10.21147/j.issn.1000-9604.2018.01.03] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Objective To prospectively compare the discriminative capacity of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with that of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in the differentiation of malignant and benign solitary pulmonary nodules (SPNs). Methods Forty-nine patients with SPNs were included in this prospective study. Thirty-two of the patients had malignant SPNs, while the other 17 had benign SPNs. All these patients underwent DCE-MRI and 18F-FDG PET/CT examinations. The quantitative MRI pharmacokinetic parameters, including the trans-endothelial transfer constant (Ktrans), redistribution rate constant (Kep), and fractional volume (Ve), were calculated using the Extended-Tofts Linear two-compartment model. The 18F-FDG PET/CT parameter, maximum standardized uptake value (SUVmax), was also measured. Spearman's correlations were calculated between the MRI pharmacokinetic parameters and the SUVmax of each SPN. These parameters were statistically compared between the malignant and benign nodules. Receiver operating characteristic (ROC) analyses were used to compare the diagnostic capability between the DCE-MRI and 18F-FDG PET/CT indexes. Results Positive correlations were found between Ktrans and SUVmax, and between Kep and SUVmax (P<0.05). There were significant differences between the malignant and benign nodules in terms of the Ktrans, Kep and SUVmax values (P<0.05). The areas under the ROC curve (AUC) of Ktrans, Kep and SUVmax between the malignant and benign nodules were 0.909, 0.838 and 0.759, respectively. The sensitivity and specificity in differentiating malignant from benign SPNs were 90.6% and 82.4% for Ktrans; 87.5% and 76.5% for Kep; and 75.0% and 70.6% for SUVmax, respectively. The sensitivity and specificity of Ktrans and Kep were higher than those of SUVmax, but there was no significant difference between them (P>0.05). Conclusions DCE-MRI can be used to differentiate between benign and malignant SPNs and has the advantage of being radiation free.
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Affiliation(s)
- Feng Feng
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Fulin Qiang
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Aijun Shen
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Donghui Shi
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Aiyan Fu
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Haiming Li
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Mingzhu Zhang
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Ganlin Xia
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
| | - Peng Cao
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong 226361, China
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