MRI for solitary pulmonary nodule and mass assessment: Current state of the art

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.

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

Increased attention has been given to MRI in radiation-free screening for malignant nodules in recent years. Our objective was to compare the performance of human readers and radiomic feature analysis based on stand-alone and complementary CT and MRI imaging in classifying pulmonary nodules. This single-center study comprises patients with CT findings of pulmonary nodules who underwent additional lung MRI and whose nodules were classified as benign/malignant by resection. For radiomic features analysis, 2D segmentation was performed for each lung nodule on axial CT, T2-weighted (T2w), and diffusion (DWI) images. The 105 extracted features were reduced by iterative backward selection. The performance of radiomics and human readers was compared by calculating accuracy with Clopper-Pearson confidence intervals. Fifty patients (mean age 63 +/- 10 years) with 66 pulmonary nodules (40 malignant) were evaluated. ACC values for radiomic features analysis vs. radiologists based on CT alone (0.68; 95%CI: 0.56, 0.79 vs. 0.59; 95%CI: 0.46, 0.71), T2w alone (0.65; 95%CI: 0.52, 0.77 vs. 0.68; 95%CI: 0.54, 0.78), DWI alone (0.61; 95%CI:0.48, 0.72 vs. 0.73; 95%CI: 0.60, 0.83), combined T2w/DWI (0.73; 95%CI: 0.60, 0.83 vs. 0.70; 95%CI: 0.57, 0.80), and combined CT/T2w/DWI (0.83; 95%CI: 0.72, 0.91 vs. 0.64; 95%CI: 0.51, 0.75) were calculated. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets, pulmonary nodule assessment through radiomics analysis is superior to using one modality alone, even exceeding human readers' performance.

MR Perfusion Imaging of the Lung

Lung perfusion assessment is critical for diagnosing and monitoring a variety of respiratory conditions. MRI perfusion provides a radiation-free technique, making it an ideal choice for longitudinal imaging in younger populations. This review focuses on the techniques and applications of MRI perfusion, including contrast-enhanced (CE) MRI and non-CE methods such as arterial spin labeling (ASL), fourier decomposition (FD), and hyperpolarized 129-Xenon (129-Xe) MRI. ASL leverages endogenous water protons as tracers for a non-invasive measure of lung perfusion, while FD offers simultaneous measurements of lung perfusion and ventilation, enabling the generation of ventilation/perfusion mapsHyperpolarized 129-Xe MRI emerges as a novel tool for assessing regional gas exchange in the lungs. Despite the promise of MRI perfusion techniques, challenges persist, including competition with other imaging techniques and the need for additional validation and standardization. In conditions such as cystic fibrosis and lung cancer, MRI has displayed encouraging results, whereas in diseases like chronic obstructive pulmonary disease, further validation remains necessary. In conclusion, while MRI perfusion techniques hold immense potential for a comprehensive, non-invasive assessment of lung function and perfusion, their broader clinical adoption hinges on technological advancements, collaborative research, and rigorous validation.

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.

Look-Locker T1 relaxometry and high-resolution T2 in the evaluation of lung lesions: a single-center prospective study

Objective

To explore the feasibility of two magnetic resonance imaging (MRI) sequences-high-resolution T2-weighted (HR T2) and Look-Locker T1 (LL T1) relaxometry-for the investigation focal lung lesions (FLLs). As a secondary objective, we analyzed the diagnostic accuracy of these sequences.

Materials and Methods

This was a prospective observational study involving 39 subjects with FLLs scanned in a 1.5-T MRI system with LL T1 relaxometry and HR T2 sequences focused on the FLL region, in addition to a conventional protocol. All images were evaluated by two radiologists, working independently, who were blinded to other findings.

Results

Most of the examinations (31 of the LL T1 relaxometry sequences and 36 of the HR T2 sequences) were of adequate diagnostic quality. Nondiagnostic examinations were considered so mainly because of limited coverage of the sequences. Of the FLLs studied, 19 were malignant, 17 were benign, and three were excluded from the accuracy analysis because there was no definitive diagnosis. Although LL T1 relaxometry could not distinguish between benign and malignant lesions, the signal intensity at its first inversion time (160 ms) differed between the two groups. The HR T2 sequence was considered the best sequence for assessing specific morphological characteristics, especially pseudocavities and pleural tags. We found that MRI showed better accuracy than did computed tomography (86% vs. 74%).

Conclusion

Both MRI sequences are feasible for the evaluation of FLLs. Images at 160 ms of the LL T1 relaxometry sequence helped distinguish between benign and malignant lesions, and the HR T2 sequence was considered the best sequence for evaluating specific morphological characteristics.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Washout-Computed Tomography Discriminates Pulmonary "Fat-poor" Hamartomas From Neuroendocrine Neoplasms: A Simple Method in the Radiomics Era

PURPOSE

Pulmonary hamartomas (HAs) and neuroendocrine neoplasms (NENs) are often impossible to discriminate using high-resolution computed tomography (CT) as they share morphologic features. This challenge makes differential diagnosis crucial as HAs are invariably benign, whereas NENs must be considered malignant, thus requiring them to be evaluated for surgical excision.Our aim was, therefore, to develop a simple method to discriminate between pulmonary "fat-poor" HAs and NENs using contrast-enhanced CT (CECT).

MATERIALS AND METHODS

Between September 2015 and December 2021, 95 patients with a histologically proven diagnosis of lung NENs (74) and HAs (21) and who underwent a preoperative CECT scan were initially identified through a review of our pathologic and radiologic databases. Among these, 55 cases (18 HAs and 37 NENs), which have been studied with biphasic CECT, were ultimately selected and reviewed by 3 radiologists with different levels of experience, analyzing their morphologic and enhancement features.The enhancement analysis was performed by placing a region of interest within the lesion in noncontrast (NCp), postcontrast (PCp, 55 to 65 s after intravenous contrast injection), and delayed phases (Dp, 180 to 300 s). A subgroup of 35 patients who underwent 18FDG-PET/CT was evaluated in a secondary analysis.

RESULTS

HU values were significantly different between NENs and HAs in the PCp ( P <0.001). NCp and Dp attenuation values did not show significant differences in the 2 groups. Differences in values of HUs in PCp and Dp allowed to discriminate between NENs and HAs.

CONCLUSION

Wash-out analysis, ΔHU (PCp-Dp), can perfectly discriminate pulmonary "fat-poor" HAs from NENs.

Lung nodule pre-diagnosis and insertion path planning for chest CT images

Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.

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.

Sensitivity and specificity of magnetic resonance imaging in routine diagnosis of pulmonary lesions: a comparison with computed tomography

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.

Comparison Between Magnetic Resonance Imaging and Computed Tomography in the Detection and Volumetric Assessment of Lung Nodules: A Prospective Study

Rationale and Objectives

Computed tomography (CT) lung nodule assessment is routinely performed and appears very promising for lung cancer screening. However, the radiation exposure through time remains a concern. With the overall goal of an optimal management of indeterminate lung nodules, the objective of this prospective study was therefore to evaluate the potential of optimized ultra-short echo time (UTE) MRI for lung nodule detection and volumetric assessment.

Materials and Methods

Eight (54.9 ± 13.2 years) patients with at least 1 non-calcified nodule ≥4 mm were included. UTE under high-frequency non-invasive ventilation (UTE-HF-NIV) and in free-breathing at tidal volume (UTE-FB) were investigated along with volumetric interpolated breath-hold examination at full inspiration (VIBE-BH). Three experienced readers assessed the detection rate of nodules ≥4 mm and ≥6 mm, and reported their location, 2D-measurements and solid/subsolid nature. Volumes were measured by two experienced readers. Subsequently, two readers assessed the detection and volume measurements of lung nodules ≥4mm in gold-standard CT images with soft and lung kernel reconstructions. Volumetry was performed with lesion management software (Carestream, Rochester, New York, USA).

Results

UTE-HF-NIV provided the highest detection rate for nodules ≥4 mm (n = 66) and ≥6 mm (n = 32) (35 and 50%, respectively). No dependencies were found between nodule detection and their location in the lung with UTE-HF-NIV (p > 0.4), such a dependency was observed for two readers with VIBE-BH (p = 0.002 and 0.03). Dependencies between the nodule's detection and their size were noticed among readers and techniques (p < 0.02). When comparing nodule volume measurements, an excellent concordance was observed between CT and UTE-HF-NIV, with an overestimation of 13.2% by UTE-HF-NIV, <25%-threshold used for nodule's growth, conversely to VIBE-BH that overestimated the nodule volume by 28.8%.

Conclusion

UTE-HF-NIV is not ready to replace low-dose CT for lung nodule detection, but could be used for follow-up studies, alternating with CT, based on its volumetric accuracy.

Efficacy of Ultrashort Echo Time Pulmonary MRI for Lung Nodule Detection and Lung-RADS Classification

Background Pulmonary MRI with ultrashort echo time (UTE) has been compared with chest CT for nodule detection and classification. However, direct comparisons of these methods' capabilities for Lung CT Screening Reporting and Data System (Lung-RADS) evaluation remain lacking. Purpose To compare the capabilities of pulmonary MRI with UTE with those of standard- or low-dose thin-section CT for Lung-RADS classification. Materials and Methods In this prospective study, standard- and low-dose chest CT (270 mA and 60 mA, respectively) and MRI with UTE were used to examine consecutive participants enrolled between January 2017 and December 2020 who met American College of Radiology Appropriateness Criteria for lung cancer screening with low-dose CT. Probability of nodule presence was assessed for all methods with a five-point visual scoring system by two board-certified radiologists. All nodules were then evaluated in terms of their Lung-RADS classification using each method. To compare nodule detection capability of the three methods, consensus for performances was rated by using jackknife free-response receiver operating characteristic analysis, and sensitivity was compared by means of the McNemar test. In addition, weighted κ statistics were used to determine the agreement between Lung-RADS classification obtained with each method and the reference standard generated from standard-dose CT evaluated by two radiologists who were not included in the image analysis session. Results A total of 205 participants (mean age: 64 years ± 7 [standard deviation], 106 men) with 1073 nodules were enrolled. Figure of merit (FOM) (P < .001) had significant differences among three modalities (standard-dose CT: FOM = 0.91, low-dose CT: FOM = 0.89, pulmonary MRI with UTE: FOM = 0.94), with no evidence of false-positive findings in participants with all modalities (P > .05). Agreements for Lung-RADS classification between all modalities and the reference standard were almost perfect (standard-dose CT: κ = 0.82, P < .001; low-dose CT: κ = 0.82, P < .001; pulmonary MRI with UTE: κ = 0.82, P < .001). Conclusion In a lung cancer screening population, ultrashort echo time pulmonary MRI was comparable to standard- or low-dose CT for Lung CT Screening Reporting and Data System classification. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation

OBJECTIVES

To develop and validate a radiomics nomogram for differentiating between malignant pulmonary nodules and benign nodules.

METHODS

56 benign and 51 malignant nodules from 96 patients were analyzed using manual segmentation of the T2-fBLADE-TSE, while the nodules signal intensity (SIlesion), lesion muscle ratio (LMR) and nodule size were all measured and recorded. The maximum relevance and minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select nonzero coefficients and develop the model in pulmonary nodules diagnosis. The radiomics nomogram was also developed. The clinical prediction value was determined by the decision curve analysis (DCA).

RESULTS

The nodule size, SIlesion and LMR of the benign group were 1.78 ± 0.57 cm, 227.50 ± 81.39 and 2.40 ± 1.27 respectively, in contrast to the 2.00 ± 0.64 cm, 232.87 ± 82.21 and 2.17 ± 0.91, respectively, in the malignant group (P = 0.09, 0.60 and 0.579). A total of 13 radiomics features were retained. The Rad-score of the benign nodules group was lower than that of the malignant nodules group (P < 0.001 & 0.049, training & test set). The AUC of radiomics signature for nodules diagnosis was 0.82 (95% CI, 0.73-0.91) in the training set and 0.71 (95% CI, 0.51-0.90) in the test set. A nomogram, consisting of 13 radiomics features and nodule size, produced good prediction in the training set (AUC, 0.82; 95% CI, 0.73-0.91), which was significantly better than that of T2-based quantitative parameters (AUC, 0.62; 95% CI, 0.50-0.75, P = 0.003). In the test set, the performance of radiomics nomogram (AUC, 0.70; 95% CI, 0.51-0.90) was also better than that of T2-based quantitative parameters (AUC, 0.46; 95% CI, 0.25-0.67) (P = 0.145). The DCA showed that radiomics nomogram and T2-based quantitative parameter had overall net benefits, while the performance of nomogram was better.

CONCLUSION

We constructed and validated a T2-fBLADE-TSE-based radiomics nomogram that can help to differentiate between malignant pulmonary nodules and benign nodules.

Whole-lesion histogram analysis of mono-exponential and bi-exponential diffusion-weighted imaging in differentiating lung cancer from benign pulmonary lesions using 3 T MRI

AIM

To investigate whether whole-lesion histogram analysis of apparent diffusion coefficient (ADC) values derived from mono-exponential and bi-exponential diffusion-weighted imaging (DWI) can differentiate lung cancer from benign pulmonary lesions.

MATERIALS AND METHODS

Thirty-two patients with lung cancer and 17 patients with benign pulmonary lesions were included retrospectively. All patients underwent DWI before surgery or biopsy. ADC histogram parameters, including mean, percentile values (10^th and 90^th), kurtosis, and skewness, were calculated independently by two radiologists. The histogram parameters were compared between patients with lung cancer and benign lesions. Receiver operating characteristic curves were constructed to evaluate the diagnostic performance.

RESULTS

The ADC_Mean, ADC_10th, D_Mean, D_10th were significantly lower in lung cancer (1.187 ± 0.144 × 10^-3; 0.440 ± 0.062 × 10^-3; 1.068 ± 0.108 × 10^-3; and 0.422 ± 0.049 × 10^-3 mm/s) compared to benign lesions (1.418 ± 0.274 × 10^-3; 0.555 ± 0.113 × 10^-3; 1.216 ± 0.149 × 10^-3; and 0.490 ± 0.044 × 10^-3 mm/s; p<0.05). The ADC_Skewness and D_Skewness were significantly different between lung cancer (2.35 ± 0.72; 2.58 ± 1.14) and benign lesions (1.85 ± 0.54; 1.59 ± 1.47; p<0.05). D_10th was robust in differentiating lung cancer from benign lesions. Using 0.453 × 10^-3 mm/s as the optimal threshold, the sensitivity, specificity, and accuracy of D_10th were 78.12%, 82.35%, and 79.6%, respectively.

CONCLUSION

Whole-lesion histogram analysis of ADC values derived by mono-exponential and bi-exponential DWI using 3 T magnetic resonance imaging helps distinguish lung cancer from benign pulmonary lesions.

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

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.

Canadian Society of Thoracic Radiology/Canadian Association of Radiologists Clinical Practice Guidance for Non-Vascular Thoracic MRI

Historically thoracic MRI has been limited by the lower proton density of lung parenchyma, cardiac and respiratory motion artifacts and long acquisition times. Recent technological advancements in MR hardware systems and improvement in MR pulse sequences have helped overcome these limitations and expand clinical opportunities for non-vascular thoracic MRI. Non-vascular thoracic MRI has been established as a problem-solving imaging modality for characterization of thymic, mediastinal, pleural chest wall and superior sulcus tumors and for detection of endometriosis. It is increasingly recognized as a powerful imaging tool for detection and characterization of lung nodules and for assessment of lung cancer staging. The lack of ionizing radiation makes thoracic MRI an invaluable imaging modality for young patients, pregnancy and for frequent serial follow-up imaging. Lack of familiarity and exposure to non-vascular thoracic MRI and lack of consistency in existing MRI protocols have called for clinical practice guidance. The purpose of this guide, which was developed by the Canadian Society of Thoracic Radiology and endorsed by the Canadian Association of Radiologists, is to familiarize radiologists, other interested clinicians and MR technologists with common and less common clinical indications for non-vascular thoracic MRI, discuss the fundamental imaging findings and focus on basic and more advanced MRI sequences tailored to specific clinical questions.

A novel application of pulmonary transit time to differentiate between benign and malignant pulmonary nodules using myocardial contrast echocardiography

Malignant pulmonary nodules (PNs) are often accompanied by vascular dilatation and structural abnormalities. Pulmonary transit time (PTT) measurement by contrast echocardiograghy has used to assess the cardiopulmonary function and pulmonary vascular status, such as hepatopulmonary syndrome and pulmonary arteriovenous fistula, but has not yet been attempted in the diagnosis and differential diagnosis of PNs. The aim of this work was to evaluate the feasibility and performance of myocardial contrast echocardiography (MCE) for differentiating malignant PNs from benign ones. The study population consisted of 201 participant: 66 healthy participants, 65 patients with benign PNs and 70 patients with malignant PNs. Their clinical and conventional echocardiographic characteristics were collected. MCE with measurements of PTT were performed. There was no difference in age, sex, heart rate, blood pressure, smoking rate, background lung disease, pulmonary function, ECG, myocardial enzymes, cardiac size and function among the healthy participant, patients with benign and malignant PNs (P > 0.05). PTT did not differ significantly in patients with PNs of different sizes, nor did they differ in patients with PNs of different enhancement patterns (P > 0.05). However, the PTT were far shorter (about one half) in patients with malignant PNs than in patients with benign ones (1.88 ± 0.37 vs. 3.73 ± 0.35, P < 0.001). There was no significantly different between patients with benign PNs and healthy participant (3.73 ± 0.35 vs.3.89 ± 0.36, P > 0.05). The area under the receiver operating characteristics curve (AUC) of PTT was 0.99(0.978-1.009) in discriminating between benign and malignant PNs. The optimal cutoff value was 2.78 s, with a sensitivity of 98.52%, a specificity of 97.34%, and a accuracy of 97.69%. MCE had a powerful performance in differentiating between benign and malignant PNs, and a pulmonary circulation time of < 2.78 s indicated malignant PNs.

Thoracoscopic Resection of Congenital Lung Malformation: Looking for the Right Preoperative Assessment

INTRODUCTION

 Consensus on the best postnatal radiological evaluation of congenital lung malformations (CLMs) is still lacking. In recent years, the interest on magnetic resonance imaging (MRI) has grown, but its role is still unknown.

AIM

 The aim of the study was to identify the best preoperative diagnostic assessment for CLM.

MATERIALS AND METHODS

 All patients with a prenatal suspicion of CLM between January 2014 and February 2018 were studied. Asymptomatic newborns underwent MRI, during spontaneous sleep without contrast. Patients with a positive MRI were scheduled for computed tomography (CT) within the fourth month of life. Thoracoscopic resection was performed in cases with a pathological CT. MRI, CT, and surgical findings were compared based on dimension, localization, and features of the CLM using the Cohen's kappa test (K).

RESULTS

 A total of 20 patients were included (10 males). No difference was found in the diameter and site of the lesions always localized in the same side (K = 1) and in the same pulmonary lobe (K = 1). Infants who underwent thoracoscopic resection included: three congenital pulmonary airway malformations (CPAMs), five extralobar and eight intralobar sequestrations (bronchopulmonary sequestrations [BPSs]), three bronchogenic cysts, and one congenital emphysema. The concordance between MRI and CT and between radiological investigations and pathology was satisfactory for the greatest part of the studied variables. MRI showed sensitivity of 100%, specificity of 82%, positive predictive value of 50% and negative predictive value of 100% for CPAM and 77, 100, 100, and 80% for BPS, respectively.

CONCLUSION

 MRI proved to be a reliable diagnostic investigation for CLM with high sensitivity and specificity. Early MRI in spontaneous sleep without contrast and preoperative contrast CT scan is a valuable preoperatory assessment.

A pilot study of native T1-mapping for focal pulmonary lesions in 3.0 T magnetic resonance imaging: size estimation and differential diagnosis

Background

To investigate the accuracy of size estimation and potential diagnosis efficacy of native T1-mapping in focal pulmonary lesion, compared to T1-star 3D-volumetric interpolated breath-hold sequence (VIBE), T2-fBLADE turbo-spin echo (TSE), and computed tomography (CT).

Methods

Thirty-nine patients with CT-detected focal pulmonary lesions underwent thoracic 3.0-T magnetic resonance imaging (MRI) using axial free-breathing 3D T1-star VIBE, respiratory triggered T2-fBLADE TSE, breath-hold T1-Turbo fast low angle shot (FLASH) and T1-FLASH 3D. Native T1-mapping images were generated by T1-FLASH 3D with B1-filed correction by T1-Turbo FLASH. The intraclass correlation coefficient (ICC) and Bland-Altman plots were used to evaluate intra-observer agreement and inter-method reliability of diameter measurements. Native T1-values were measured and compared among the malignancy, tuberculosis, non-tuberculosis benign groups using Mann-Whitney U tests.

Results

Forty-five focal pulmonary lesions were displayed by CT, native T1-mapping, T1-star VIBE, and T2-fBLADE TSE. T1-mapping-based diameter measurements yielded an intra-observer ICC of 0.995. Additionally, inter-method measurements were highly consistent (T1-mapping & T1-star VIBE: ICC 0.982, T1-mapping & T2-fBLADE TSE: ICC 0.978, T1-mapping & CT: ICC 0.972). For lesions <3.00 cm, T1-mapping intra-observer (ICC 0.982) and inter-method diameter measurements were also highly consistent (T1-mapping & CT: ICC 0.823). Native T1-values of malignant tumors were lower than those of the non-tuberculosis benign lesions (P=0.003). Native T1-values of tuberculosis were lower than those of the non-tuberculosis benign lesions (P=0.002). Native T1-values showed no statistically significant differences between malignant tumors and tuberculosis (P=0.059).

Conclusions

Native T1-mapping enable accurate and reliable diameter measurement. Native T1-values potentially differentiate malignant tumors or tuberculosis from non-tuberculosis benign lesions.

Magnetic Resonance Imaging for the Follow-up of Treated Thymic Epithelial Malignancies

PURPOSE

The purpose of this article was to compare magnetic resonance imaging (MRI) depiction of thymic malignancy progression/recurrence with that of computed tomography (CT).

METHODS

We retrospectively reviewed all surgically treated thymic epithelial malignancy (TEM) patients between 2011 and 2018 who were followed-up with chest CT and MRI. We compared the detection of recurrence and metastatic disease between the CT and MRI scans in each of these patients.

RESULTS

Of 187 patients treated in our institution for TEM, 22 were followed-up with both CT and MRI. TNM stage at diagnosis was as follows: I (n=14), II (n=1), IIIa (n=4), IIIb (n=2), IVa (n=1), and IVb (n=0). Patients were followed-up for a mean of 6.2 years, range 0.7 to 17.7 years. The mean interval between CT and MRI was 5.4 (range, 1 to 15) months. Most patients had no recurrence (n=16), 4 had recurrence after R0 or R1 resection, 1 had stable disease, and 1 had progression of disease after R2 resection. CT and MRI performed equally in the identification of pleural spread (n=5), lymphadenopathy (n=4), and pulmonary metastases (n=1). Retrosternal recurrence (n=1) was identified by MRI despite sternotomy wire artifacts. MRI identified bone involvement and extension of disease into the thecal sac earlier and more readily. Three patients had an indeterminate mediastinal finding on CT that was correctly identified as a benign cyst or pericardial fluid collection by MRI.

CONCLUSION

MRI is an alternative option to follow-up patients after treatment for TEM. However, for those with metallic sternotomy wires, we recommend alternating the follow-up with CT as well.

Research progress of computer aided diagnosis system for pulmonary nodules in CT images

BACKGROUND AND OBJECTIVE

Since CAD (Computer Aided Diagnosis) system can make it easier and more efficient to interpret CT (Computer Tomography) images, it has gained much attention and developed rapidly in recent years. This article reviews recent CAD techniques for pulmonary nodule detection and diagnosis in CT Images.

METHODS

CAD systems can be classified into computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. This review reports recent researches of both systems, including the database, technique, innovation and experimental results of each work. Multi-task CAD systems, which can handle segmentation, false positive reduction, malignancy prediction and other tasks at the same time. The commercial CAD systems are also briefly introduced.

RESULTS

We have found that deep learning based CAD is the mainstream of current research. The reported sensitivity of deep learning based CADe systems ranged between 80.06% and 94.1% with an average 4.3 false-positive (FP) per scan when using LIDC-IDRI dataset, and between 94.4% and 97.9% with an average 4 FP/scan when using LUNA16 dataset, respectively. The overall accuracy of deep learning based CADx systems ranged between 86.84% and 92.3% with an average AUC of 0.956 reported when using LIDC-IDRI dataset.

CONCLUSIONS

We summarized the current tendency and limitations as well as future challenges in this field. The development of CAD needs to meet the rigid clinical requirements, such as high accuracy, strong robustness, high efficiency, fine-grained analysis and classification, and to provide practical clinical functions. This review provides helpful information for both engineering researchers and radiologists to learn the latest development of CAD systems.

The current status and further prospects for lung magnetic resonance imaging in pediatric radiology

Lung MRI makes it possible to replace up to 90% of CT examinations with radiation-free magnetic resonance diagnostics of the lungs without suffering any diagnostic loss. The individual radiation exposure can thus be relevantly reduced. This applies in particular to children who repeatedly require sectional imaging of the lung, e.g., in tumor surveillance or in chronic lung diseases such as cystic fibrosis. In this paper we discuss various factors that favor the establishment of lung MRI in the clinical setting. Among the many sequences proposed for lung imaging, respiration-triggered T2-W turbo spin-echo (TSE) sequences have been established as a good standard for children. Additional sequences are mostly dispensable. The most important pulmonary findings are demonstrated here in the form of a detailed pictorial essay. T1-weighted gradient echo sequences with ultrashort echo time are a new option. These sequences anticipate signal loss in the lung and deliver CT-like images with high spatial resolution. When using self-gated T1-W ultrashort echo time 3-D sequences that acquire iso-voxel geometry in the sub-millimeter range, secondary reconstructions are possible.

Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.

Initial M Staging of Rectal Cancer: FDG PET/MRI with a Hepatocyte-specific Contrast Agent versus Contrast-enhanced CT

BackgroundThe performance of PET/MRI in the determination of distant metastases (M stage) in rectal cancer relative to the current practice with contrast material-enhanced CT is largely unknown.PurposeTo compare the staging of clinical M stage rectal cancer with fluorine 18 fluorodeoxyglucose (FDG) PET/MRI (including dedicated liver and rectal MRI) to that of chest and abdominopelvic CT and dedicated rectal MRI.Materials and MethodsFrom January 2016 to August 2017, patients with newly diagnosed advanced mid to low rectal cancers were recruited for this prospective study (clinicaltrials.gov identifier: NCT0265170). Participants underwent both FDG PET/MRI with dedicated liver and rectal MRI and chest and abdominopelvic CT (the standard-of-care protocol) within 3 weeks of each other. Thereafter, M stage assessment performance was determined by using findings from 6-month clinical follow-up or biopsy as the reference standard. Performance was compared between protocols. Agreement in M stage classification was also assessed. Nonparametric statistical analyses were performed, and P < .05 indicated a significance difference.ResultsSeventy-one participants (28 women; mean age ± standard deviation, 61 years ± 9; age range, 39-79 years) were enrolled. The M stage could not be determined with the standard-of-care protocol in 22 of the 71 participants (31%; 95% confidence interval [CI]: 20.5%, 43.1%) because of indeterminate lesions. However, among these participants, PET/MRI correctly helped identify all 14 (100%; 95% CI: 76.8%, 100%) without metastases and seven of eight (88%; 95% CI: 47.4%, 99.7%) who were later confirmed to have metastases. PET/MRI showed high specificity for ruling out metastatic disease compared with the standard-of-care protocol (98% [54 of 55 participants] vs 72% [40 of 55 participants], respectively; P < .001), without increasing the number of participants with missed metastasis (6% [one of 16 participants] vs 6% [one of 16 participants]; P > .99).ConclusionPET/MRI with dedicated rectal and liver MRI can facilitate the staging work-up of newly diagnosed advanced rectal cancers by helping assess indeterminate lesions, metastases, and incidental findings better than contrast-enhanced CT, obviating for additional imaging work-up.© RSNA, 2019Online supplemental material is available for this article.Clinical trial registration no. NCT02651701.

Management of incidental pulmonary nodules: current strategies and future perspectives

Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.

Novelties in imaging in pulmonary fibrosis and nodules. A narrative review

In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes. In addition, new recommendations for classification of pulmonary nodules have been released. Radiological images are of major interest for automated and standardized analysis and so in both cases software tools using artificial intelligence were developed for visualization and quantification of the disease. These tools have been validated by human readers and demonstrated their capabilities. This review summarizes the new recommendations for classification of pulmonary fibrosis and nodules and reviews the capabilities of radiomics within these two entities.

Building blocks for thoracic MRI: Challenges, sequences, and protocol design

Thoracic MRI presents important and unique challenges. Decreased proton density in the lung in combination with respiratory and cardiac motion can degrade image quality and render poorly executed sequences uninterpretable. Despite these challenges, thoracic MRI has an important clinical role, both as a problem-solving tool and in an increasing array of clinical indications. Advances in scanner and sequence design have also helped to drive this development, presenting the radiologist with improved techniques for thoracic MRI. Given this evolving landscape, radiologists must be familiar with what thoracic MR has to offer. The first step in developing an effective thoracic MRI practice requires the creation of efficient and malleable protocols that can answer clinical questions. To do this, radiologists must have a working knowledge of the MR sequences that are used in the thorax, many of which have been adapted from use elsewhere in the body. These sequences can be broadly divided into three categories: traditional/anatomic, functional, and cine based. Traditional/anatomic sequences allow for the depiction of anatomy and pathologic processes with the ability for characterization of signal intensity and contrast enhancement. Functional sequences, including diffusion-weighted imaging, and high temporal resolution dynamic contrast enhancement, allow for the noninvasive measurement of tissue-specific parameters. Cine-based sequences can depict the motion of structures in the thorax, either with retrospective ECG gating or in real time. The purpose of this article is to review these categories, the building block sequences that comprise them, and identify basic questions that should be considered in thoracic MRI protocol design. Level of Evidence: 5 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:682-701.

Can MRI contribute to pulmonary nodule analysis?

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), K^trans , K_ep , V_e 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.