ArticleJournal of thoracic disease2022
Sensitivity and specificity of magnetic resonance imaging in routine diagnosis of pulmonary lesions: a comparison with computed tomography.
Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 5 citations in OpenAlex.
- Diagnostic performance of a single breath-hold lung MRI scan with AI-powered compressed sensing for nodule detection in comparison to photon counting detector-CT.European radiology · 2026Article
- Recurrent aseptic peritonitis in a dog diagnosed with peritoneal mesothelioma post-mortem.The Canadian veterinary journal = La revue veterinaire canadienne · 2026Article
- Thoracic magnetic resonance imaging in non-tuberculous mycobacterial pulmonary disease: Characteristics and potential implementation.PloS one · 2025Article
- Differentiating inflammatory and malignant pulmonary lesions on 3T lung MRI with radiomics of apparent diffusion coefficient maps and T2w derived radiomic feature maps.Journal of thoracic disease · 2024Article
- Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI.Diagnostics (Basel, Switzerland) · 2024Article
- Role of Diffusion-Weighted Magnetic Resonance Imaging for Characterization of Mediastinal Lymphadenopathy.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: State-of-the-art thoracic magnetic resonance imaging (MRI) plays a complementary role in the assessment of pulmonary nodules/masses which potentially indicate to cancer. We aimed to evaluate the sensitivity and specificity of MRI in diagnosis of pulmonary nodules/masses. Methods: Sixty-eight patients with computed tomography (CT)-detected pulmonary nodules/masses underwent 3T MRI (T1-VIBE, T1-starVIBE, T2-fBLADE turbo spin-echo, and T2-SPACE). The detection rate was calculated for each of the different subgroups of pulmonary nodules according to lung imaging reporting and data system (Lung-RADS). The four MRI sequences were compared in terms of detection rate and image quality-signal to noise ratio (SNR), contrast to noise ratio (CNR) and 5-point scoring scale. Agreement of lesion size measurement between CT and MRI was assessed by intraclass correlation coefficient (ICC). The picture-SNR, lesion-SNR and CNR of each sequence were analyzed by Mann-Whitney U test. Results: In total, 232 pulmonary lesions were detected by CT. The CT showed 86 solid nodules (SNs) <6 mm, 15 SNs between 6-8 mm, 35 SNs between 8-15 mm, and 52 SNs between 15-30 mm. The T1-VIBE, T1-starVIBE, T2-fBLADE TSE and T2-SPACE sequences accurately detected 141 SNs (141/188, 75%/83.3%), 150 SNs (150/188, 79.8%/100%), 166 SNs (166/188, 88.3%/66.7%) and 169 SNs (169/188, 89.9%/53.3%), respectively. Four ground glass nodules (GGNs) (4/6) were detected by T2-fBLADE TSE. Twelve part-solid nodules (PSNs) (12/22) were detected by T1-VIBE and 20 PSNs (20/22) by T2-SPACE. A total of 100 lesions (2.2±1.4 cm, 0.8-7.3 cm) were accurately detected and measured by the four MRI sequences with ICC >0.96. The picture-SNR, lesion-SNR and CNR by T1-starVIBE were higher than those by T1-VIBE (P<0.001). The lesion-SNR and CNR by T2-fBLADE TSE were higher than those by T2-SPACE (P=0.006, 0.038). 86% of images by T1-starVIBE, 92% by T2-fBLADE TSE, 90% by T2-SPACE and 93% by T1-VIBE were scored 3 or more. Conclusions: MRI achieves high sensitivity and specificity for different type of pulmonary nodules detection and is an effective alternative to CT as a diagnostic tool for pulmonary nodules.
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