Synopsis from Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper

Leveraging Phase Information of 3D Isotropic Ultrashort Echo Time Pulmonary MRI for the Detection of Thoracic Lymphadenopathy: Toward an All-in-One Scan Solution

BACKGROUND

Ultrashort echo time (UTE) allows imaging of tissues with short relaxation times, but it comes with the expense of long scan times. Magnitude images of UTE magnetic resonance imaging (MRI) are widely used in pulmonary imaging due to excellent parenchymal signal, but have insufficient contrast for other anatomical regions of the thorax. Our work investigates the value of UTE phase images (UTE-Ps)-generated simultaneously from the acquired UTE signal used for the magnitude images-for the detection of thoracic lymph nodes based on water-fat contrast. It employs an advanced imaging sequence and reconstruction allowing isotropic 3D UTE MRI in clinically acceptable scan times.

METHODS

In our prospective study, 42 patients with 136 lymph nodes had undergone venous computed tomography and pulmonary MRI scans with UTE within a 14-day interval. 3D isotropic UTE images were acquired using FLORET (fermat looped, orthogonally encoded trajectories). Background-corrected phase images (UTE-P) and magnitude images were reconstructed simultaneously from the UTE-Signal. Three radiologists performed a blinded reading in which all lymph nodes with a short-axis diameter (SAD) of at least 0.5 cm were detected. Detection rates and performance metrics of UTE-P for all lymph node regions and for pathologic (SAD ≥10 mm) and nonpathologic lymph nodes (SAD <10 mm) were calculated using computed tomography as a reference. The interreader agreement defined as the presence or absence of lymph nodes based on patient and region was calculated using Fleiss kappa (κ).

FINDINGS

In the phase images, pathologic lymph nodes in the mediastinal and hilar region were detected with a high diagnostic confidence due to the achieved water-fat contrast (average sensitivity, specificity, positive predictive value, and negative predictive value of 95.83% [confidence interval (CI), 92.76%-98.91%], 100%, 100%, and 99.32% [CI, 98.08%-100%]). Stepwise inclusion of all lymph node regions and nonpathologic lymph nodes was associated with a moderate decrease resulting in an average sensitivity, specificity, positive predictive value, and negative predictive value of 77.9% (CI, 70.9%-84.7%), 99.4% (CI, 98.7%-99.9%), 97.0% (CI, 93.4%-99.7%), and 94.7% (CI, 92.8%-96.4%) for the inclusion of all lymph nodes sizes and regions. Interreader agreement was almost perfect (κ = 0.92).

CONCLUSIONS

Pathological lymph nodes in the mediastinal and hilar region can be detected in phase-images with high diagnostic confidence, thanks to the ability of the phase images to depict water-fat contrast in combination with high spatial 3D resolution, extending the clinical applicability of UTE into the simultaneous assessment of lung parenchyma and lymph nodes.

Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research

Hereby inviting young rising stars in chest radiology in Japan for contributing what they are working currently, we would like to show the potentials and directions of the near future research trends in the research field. I will provide a reflection on my own research topics. At the end, we also would like to discuss on how to choose the themes and topics of research: What to do or not to do? We strongly believe it will stimulate and help investigators in the field.

Deep learning reconstruction for zero echo time lung magnetic resonance imaging: impact on image quality and lesion detection

AIMS

This study aimed to examine the impact of deep-learning reconstruction (DLR) on zero echo time (ZTE) lung MRI.

MATERIALS AND METHODS

Fifty-nine patients who underwent both chest CT and ZTE lung magnetic resonance imaging (MRI) were enrolled. Noise reduction in ZTE lung MRI was compared using various DLR intensities (DLR-M, DLR-H) and conventional image filtering techniques (NF1 ∼ NF4). The normalized noise power spectrum (NPS) was analysed through phantom experiments. Image sharpness was evaluated using a blur metric. We compared subjective image quality and the detection of sub-centimetre nodules and emphysema between the original and noise-reduced images. Statistical analyses included the Wilcoxon signed-rank and McNemar's tests, with inter-reader agreement assessed via Kappa coefficients.

RESULTS

NPS peaks were lower in NF1 through NF4, DLR-M, and DLR-H compared to the original images. While the average spatial frequency of the NPS shifted towards lower frequencies with increasing NF levels, it remained unchanged with DLR. Blur metric values of NF1∼NF4 were significantly higher than those of the original images (p<0.008). However, there were no significant differences in blur metric values between DLR-M, DLR-H, and the original images. Image quality was rated highest for DLR-H, with a statistically significant improvement over the original (p<0.05). DLR-H showed higher diagnostic confidence for detecting sub-centimetre nodules than the original images. DLR-H showed higher diagnostic performance than the original for detecting emphysema.

CONCLUSIONS

DLR can improve ZTE lung MRI quality while preserving image texture and sharpness, thereby enhancing the potential of ZTE for evaluating pulmonary parenchymal disease.

Comparison of single-shot, FOCUS single-shot, MUSE, and FOCUS MUSE diffusion weighted imaging for pulmonary lesions: A pilot study

Rationale and objectives

To compare the performance of SS, FOCUS SS, MUSE, and FOCUS MUSE DWI for pulmonary lesions to obtain a better technique for pulmonary DWI imaging.

Materials and methods

44 patients with pulmonary lesions were recruited to perform pulmonary DWI using SS, FOCUS SS, MUSE, and FOCUS MUSE sequences. Then, two radiologists with 12 and 10 years of chest MRI experiences assessed the overall image quality while another two radiologists both with 3 years of experiences evaluated the SNR, DR, and ADC of pulmonary lesions. Using interclass correlation coefficient (ICC) and kappa statistics to assess consistency of readers, Friedman test and Dunn-Bonferroni post hoc were used to calculate the difference between sequences. Mann-Whitney test and ROC curve were used to distinguish malignant from benign lesions.

Results

All the assessed variables of the four sequences presented good to excellent intra-/inter-observer consistency. Compared with SS, FOCUS SS and MUSE, FOCUS MUSE demonstrated better image quality, including significantly higher 5-point Likert scale score (P < 0.001) and smaller DR (P < 0.001). SNR was comparable among SS, FOCUS SS, and FOCUS MUSE (P > 0.05) while MUSE presented with significantly higher SNR over them (P < 0.01). ADC of malignant was significantly smaller than that of benign for all the four sequences (P < 0.05). ROC analysis showed relatively better diagnostic performance of FOCUS MUSE (AUC = 0.820) over SS (AUC = 0.748), FOCUS SS (AUC = 0.778), and MUSE (AUC = 0.729) in distinguishing malignant from benign lesions.

Conclusion

FOCUS MUSE possessed sufficient SNR and was better over SS, FOUCS SS, and MUSE for characterizing pulmonary lesions.

Chest Magnetic Resonance Imaging: Advances and Clinical Care

Many promising study results as well as technical advances for chest magnetic resonance imaging (MRI) have demonstrated its academic and clinical potentials during the last few decades, although chest MRI has been used for relatively few clinical situations in routine clinical practice. However, the Fleischner Society as well as the Japanese Society of Magnetic Resonance in Medicine have published a few white papers to promote chest MRI in routine clinical practice. In this review, we present clinical evidence of the efficacy of chest MRI for 1) thoracic oncology and 2) pulmonary vascular diseases.

Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT

OBJECTIVE

The purpose of this study was thus to compare capabilities for quantitative differentiation of non- and minimally invasive adenocarcinomas from other of pulmonary MRIs with ultra-short TE (UTE) obtained with single- and dual-echo techniques (UTE-MRISingle and UTE-MRIDual) and thin-section CT for stage IA lung cancer patients.

METHODS

Ninety pathologically diagnosed stage IA lung cancer patients who underwent thin-section standard-dose CT, UTE-MRISingle, and UTE-MRIDual, surgical treatment and pathological examinations were included in this retrospective study. The largest dimension (Dlong), solid portion (solid Dlong), and consolidation/tumor (C/T) ratio of each nodule were assessed. Two-tailed Student's t-tests were performed to compare all indexes obtained with each method between non- and minimally invasive adenocarcinomas and other lung cancers. Receiver operating characteristic (ROC)-based positive tests were performed to determine all feasible threshold values for distinguishing non- or minimally invasive adenocarcinoma (MIA) from other lung cancers. Sensitivity, specificity, and accuracy were then compared by means of McNemar's test.

RESULTS

Each index showed significant differences between the two groups (p < 0.0001). Specificities and accuracies of solid Dlong for UTE-MRIDual2nd echo and CTMediastinal were significantly higher than those of solid Dlong for UTE-MRISingle and UTE-MRIDual1st echo and all C/T ratios except CTMediastinal (p < 0.05). Moreover, the specificities and accuracies of solid Dlong and C/T ratio were significantly higher than those of Dlong for each method (p < 0.05).

CONCLUSION

Pulmonary MRI with UTE is considered at least as valuable as thin-section CT for quantitative differentiation of non- and minimally invasive adenocarcinomas from other stage IA lung cancers.

CLINICAL RELEVANCE STATEMENT

Pulmonary MRI with UTE's capability for quantitative differentiation of non- and minimally invasive adenocarcinomas from other lung cancers in stage IA lung cancer patients is equal or superior to that of thin-section CT.

KEY POINTS

• Correlations were excellent for pathologically examined nodules with the largest dimensions (Dlong) and a solid component (solid Dlong) for all indexes (0.95 ≤ r ≤ 0.99, p < 0.0001). • Pathologically examined Dlong and solid Dlong obtained with all methods showed significant differences between non- and minimally invasive adenocarcinomas and other lung cancers (p < 0.0001). • Solid tumor components are most accurately measured by UTE-MRIDual2nd echo and CTMediastinal, whereas the ground-glass component is imaged by UTE-MRIDual1st echo and CTlung with high accuracy. UTE-MRIDual predicts tumor invasiveness with 100% sensitivity and 87.5% specificity at a C/T threshold of 0.5.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer

OBJECTIVES

To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC).

METHODS

A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA).

RESULTS

A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively.

CONCLUSION

The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC.

CLINICAL RELEVANCE STATEMENT

A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice.

KEY POINTS

• The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Lung Magnetic Resonance Imaging: Technical Advancements and Clinical Applications

ABSTRACT

Since lung magnetic resonance imaging (MRI) became clinically available, limited clinical utility has been suggested for applying MRI to lung diseases. Moreover, clinical applications of MRI for patients with lung diseases or thoracic oncology may vary from country to country due to clinical indications, type of health insurance, or number of MR units available. Because of this situation, members of the Fleischner Society and of the Japanese Society for Magnetic Resonance in Medicine have published new reports to provide appropriate clinical indications for lung MRI. This review article presents a brief history of lung MRI in terms of its technical aspects and major clinical indications, such as (1) what is currently available, (2) what is promising but requires further validation or evaluation, and (3) which developments warrant research-based evaluations in preclinical or patient studies. We hope this article will provide Investigative Radiology readers with further knowledge of the current status of lung MRI and will assist them with the application of appropriate protocols in routine clinical practice.

State of the Art MR Imaging for Lung Cancer TNM Stage Evaluation

Since the Radiology Diagnostic Oncology Group (RDOG) report had been published in 1991, magnetic resonance (MR) imaging had limited clinical availability for thoracic malignancy, as well as pulmonary diseases. However, technical advancements in MR systems, such as sequence and reconstruction methods, and adjustments in the clinical protocol for gadolinium contrast media administration have provided fruitful results and validated the utility of MR imaging (MRI) for lung cancer evaluations. These techniques include: (1) contrast-enhanced MR angiography for T-factor evaluation, (2) short-time inversion recovery turbo spin-echo sequences as well as diffusion-weighted imaging (DWI) for N-factor assessment, and (3) whole-body MRI with and without DWI and with positron emission tomography fused with MRI for M-factor or TNM stage evaluation as well as for postoperative recurrence assessment of lung cancer or other thoracic tumors using 1.5 tesla (T) or 3T systems. According to these fruitful results, the Fleischner Society has changed its position to approve of MRI for lung or thoracic diseases. The purpose of this review is to analyze recent advances in lung MRI with a particular focus on lung cancer evaluation, clinical staging, and recurrence assessment evaluation.

The evolving role of radiological imaging in cystic fibrosis

PURPOSE OF REVIEW

Radiological imaging has a crucial role in pulmonary evaluation in cystic fibrosis (CF), having been shown to be more sensitive than pulmonary function testing at detecting structural lung changes. The present review summarizes the latest published information on established and evolving pulmonary imaging techniques for assessing people with this potentially life-limiting disorder.

RECENT FINDINGS

Chest computed tomography (CT) has taken over the predominant role of chest radiography in many centres for the initial assessment and surveillance of CF lung disease. However, several emerging techniques offer a promising means of pulmonary imaging using less ionizing radiation. This is of particular importance given these patients tend to require repeated imaging throughout their lives from a young age. Such techniques include ultra-low-dose CT, tomosynthesis, dynamic radiography and magnetic resonance imaging. In addition, deep-learning algorithms are anticipated to improve diagnostic accuracy.

SUMMARY

The recent introduction of triple-combination CF transmembrane regulator therapy has put further emphasis on the need for sensitive methods of monitoring treatment response to allow for early adaptation of treatment regimens in order to limit irreversible lung damage. Further research is needed to establish how emerging imaging techniques can contribute to this safely and effectively.

Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma

BACKGROUND

The epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma.

METHODS

A total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction.

RESULTS

Among the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUCtest =0.838 (95% CI: 0.685 to 0.992)]. The AUC of the nomogram in the training group was 0.925 (95% CI: 0.855 to 0.994) and 0.727 (95% CI: 0.531 to 0.924) in the test group, respectively.

CONCLUSIONS

The radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.

State-of-the-art MR Imaging for Thoracic Diseases

Since thoracic MR imaging was first used in a clinical setting, it has been suggested that MR imaging has limited clinical utility for thoracic diseases, especially lung diseases, in comparison with x-ray CT and positron emission tomography (PET)/CT. However, in many countries and states and for specific indications, MR imaging has recently become practicable. In addition, recently developed pulmonary MR imaging with ultra-short TE (UTE) and zero TE (ZTE) has enhanced the utility of MR imaging for thoracic diseases in routine clinical practice. Furthermore, MR imaging has been introduced as being capable of assessing pulmonary function. It should be borne in mind, however, that these applications have so far been academically and clinically used only for healthy volunteers, but not for patients with various pulmonary diseases in Japan or other countries. In 2020, the Fleischner Society published a new report, which provides consensus expert opinions regarding appropriate clinical indications of pulmonary MR imaging for not only oncologic but also pulmonary diseases. This review article presents a brief history of MR imaging for thoracic diseases regarding its technical aspects and major clinical indications in Japan 1) in terms of what is currently available, 2) promising but requiring further validation or evaluation, and 3) developments warranting research investigations in preclinical or patient studies. State-of-the-art MR imaging can non-invasively visualize lung structural and functional abnormalities without ionizing radiation and thus provide an alternative to CT. MR imaging is considered as a tool for providing unique information. Moreover, prospective, randomized, and multi-center trials should be conducted to directly compare MR imaging with conventional methods to determine whether the former has equal or superior clinical relevance. The results of these trials together with continued improvements are expected to update or modify recommendations for the use of MRI in near future.

Pulmonary Functional Imaging: Part 1-State-of-the-Art Technical and Physiologic Underpinnings

Over the past few decades, pulmonary imaging technologies have advanced from chest radiography and nuclear medicine methods to high-spatial-resolution or low-dose chest CT and MRI. It is currently possible to identify and measure pulmonary pathologic changes before these are obvious even to patients or depicted on conventional morphologic images. Here, key technological advances are described, including multiparametric CT image processing methods, inhaled hyperpolarized and fluorinated gas MRI, and four-dimensional free-breathing CT and MRI methods to measure regional ventilation, perfusion, gas exchange, and biomechanics. The basic anatomic and physiologic underpinnings of these pulmonary functional imaging techniques are explained. In addition, advances in image analysis and computational and artificial intelligence (machine learning) methods pertinent to functional lung imaging are discussed. The clinical applications of pulmonary functional imaging, including both the opportunities and challenges for clinical translation and deployment, will be discussed in part 2 of this review. Given the technical advances in these sophisticated imaging methods and the wealth of information they can provide, it is anticipated that pulmonary functional imaging will be increasingly used in the care of patients with lung disease. © RSNA, 2021 Online supplemental material is available for this article.

The Clinical Use of Lung MRI in Cystic Fibrosis: What, Now, How?

To assess airway and lung parenchymal damage noninvasively in cystic fibrosis (CF), chest MRI has been historically out of the scope of routine clinical imaging because of technical difficulties such as low proton density and respiratory and cardiac motion. However, technological breakthroughs have emerged that dramatically improve lung MRI quality (including signal-to-noise ratio, resolution, speed, and contrast). At the same time, novel treatments have changed the landscape of CF clinical care. In this contemporary context, there is now consensus that lung MRI can be used clinically to assess CF in a radiation-free manner and to enable quantification of lung disease severity. MRI can now achieve three-dimensional, high-resolution morphologic imaging, and beyond this morphologic information, MRI may offer the ability to sensitively differentiate active inflammation vs scarring tissue. MRI could also characterize various forms of inflammation for early guidance of treatment. Moreover, functional information from MRI can be used to assess regional, small-airway disease with sensitivity to detect small changes even in patients with mild CF. Finally, automated quantification methods have emerged to support conventional visual analyses for more objective and reproducible assessment of disease severity. This article aims to review the most recent developments of lung MRI, with a focus on practical application and clinical value in CF, and the perspectives on how these modern techniques may converge and impact patient care soon.