Lung cancer screening with MRI: characterization of nodules with different non-enhanced MRI sequences

Lung Nodule Detection With Modern Low-Field MRI (0.55 T) in Comparison to CT

OBJECTIVES

The aim of this study was to evaluate the accuracy of modern low-field magnetic resonance imaging (MRI) for lung nodule detection and to correlate nodule size measurement with computed tomography (CT) as reference.

MATERIALS AND METHODS

Between November 2020 and July 2021, a prospective clinical trial using low-field MRI at 0.55 T was performed in patients with known pulmonary nodules from a single academic medical center. Every patient underwent MRI and CT imaging on the same day. The primary aim was to evaluate the detection accuracy of pulmonary nodules using MRI with transversal periodically rotated overlapping parallel lines with enhanced reconstruction in combination with coronal half-Fourier acquired single-shot turbo spin-echo MRI sequences. The secondary outcome was the correlation of the mean lung nodule diameter with CT as reference according to the Lung Imaging Reporting and Data System. Nonparametric Mann-Whitney U test, Spearman rank correlation coefficient, and Bland-Altman analysis were applied to analyze the results.

RESULTS

A total of 46 participants (mean age ± SD, 66 ± 11 years; 26 women) were included. In a blinded analysis of 964 lung nodules, the detection accuracy was 100% for those ≥6 mm (126/126), 80% (159/200) for those ≥4-<6 mm, and 23% (147/638) for those <4 mm in MRI compared with reference CT. Spearman correlation coefficient of MRI and CT size measurement was r = 0.87 ( P < 0.001), and the mean difference was 0.16 ± 0.9 mm.

CONCLUSIONS

Modern low-field MRI shows excellent accuracy in lesion detection for lung nodules ≥6 mm and a very strong correlation with CT imaging for size measurement, but could not compete with CT in the detection of small nodules.

Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo

BACKGROUND

Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and subsolid pulmonary nodules using T1 gradient-echo (GRE) (VIBE, Volumetric interpolated breath-hold examination), ultrashort time echo (UTE) and T2 Fast Spin Echo (HASTE, Half fourier Single-shot Turbo spin-Echo).

METHODS

Patients underwent a lung MRI in a 3 T scanner as part of a prospective research project. A baseline Chest CT was obtained as part of their standard of care. Nodules were identified and measured on the baseline CT and categorized according to their density (solid and subsolid) and size (> 4 mm/ ≤ 4 mm). Nodules seen on the baseline CT were classified as present or absent on the different MRI sequences by two thoracic radiologists independently. Interobserver agreement was determined using the simple Kappa coefficient. Paired differences were compared using nonparametric Mann-Whitney U tests. The McNemar test was used to evaluate paired differences in nodule detection between MRI sequences.

RESULTS

Thirty-six patients were prospectively enrolled. One hundred forty-nine nodules (100 solid/49 subsolid) with mean size 10.8 mm (SD = 9.4) were included in the analysis. There was substantial interobserver agreement (k = 0.7, p = 0.05). Detection for all nodules, solid and subsolid nodules was respectively; UTE: 71.8%/71.0%/73.5%; VIBE: 61.6%/65%/55.1%; HASTE 72.4%/72.2%/72.7%. Detection rate was higher for nodules > 4 mm in all groups: UTE 90.2%/93.4%/85.4%, VIBE 78.4%/88.5%/63.4%, HASTE 89.4%/93.8%/83.8%. Detection of lesions ≤4 mm was low for all sequences. UTE and HASTE performed significantly better than VIBE for detection of all nodules and subsolid nodules (diff = 18.4 and 17.6%, p = < 0.01 and p = 0.03, respectively). There was no significant difference between UTE and HASTE. There were no significant differences amongst MRI sequences for solid nodules.

CONCLUSIONS

Lung MRI shows adequate performance for the detection of solid and subsolid pulmonary nodules larger than 4 mm and can serve as a promising radiation-free alternative to CT.

Correlation between PD-L1 Expression of Non-Small Cell Lung Cancer and Data from IVIM-DWI Acquired during Magnetic Resonance of the Thorax: Preliminary Results

This study aims to investigate the correlation between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in magnetic resonance imaging (MRI) and programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC). Twenty-one patients diagnosed with stage III NSCLC from April 2021 to April 2022 were included. The tumors were distinguished into two groups: no PD-L1 expression (<1%), and positive PD-L1 expression (≥1%). Conventional MRI and IVIM-DWI sequences were acquired with a 1.5-T system. Both fixed-size ROIs and freehand segmentations of the tumors were evaluated, and the data were analyzed through a software using four different algorithms. The diffusion (D), pseudodiffusion (D*), and perfusion fraction (pf) were obtained. The correlation between IVIM parameters and PD-L1 expression was studied with Pearson correlation coefficient. The Wilcoxon−Mann−Whitney test was used to study IVIM parameter distributions in the two groups. Twelve patients (57%) had PD-L1 ≥1%, and 9 (43%) <1%. There was a statistically significant correlation between D* values and PD-L1 expression in images analyzed with algorithm 0, for fixed-size ROIs (189.2 ± 65.709 µm²/s × 104 in no PD-L1 expression vs. 122.0 ± 31.306 µm²/s × 104 in positive PD-L1 expression, p = 0.008). The values obtained with algorithms 1, 2, and 3 were not significantly different between the groups. The IVIM-DWI MRI parameter D* can reflect PD-L1 expression in NSCLC.

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

Background

Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.

Methods

Studies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.

Results

The systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).

Conclusion

The models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.

Systematic Review Registration

https://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.

CT Combined with Multiparameter MRI in Differentiating Pathological Subtypes of Non-Small-Cell Lung Cancer before Surgery

Objective

To investigate the diagnostic value of computed tomography (CT) combined with multiparametric magnetic resonance imaging (mpMRI) for preoperative differentiation of non-small-cell lung cancer (NSCLC).

Methods

CT and MRI imaging data were collected from all patients with squamous lung cancer and adenocarcinoma admitted to our hospital from June 2019 to December 2020 (286 cases). ROC curves were plotted to evaluate the performance of CT, mpMRI, and CT combined with mpMRI to differentiate pathological subtypes of NSCLC. Univariate and multivariate regression were used to be independent predictors of pathological subtypes of NSCLC.

Results

ROC curves showed that CT combined with mpMRI had the largest area under the curve, followed by mpMRI and CT successively. Univariate regression analysis showed that gender, smoking, tumor size, morphology, marginal lobulation, marginal burr, bronchial truncation sign, and vascular convergence sign were factors influencing the pathological subtype of NSCLC. Multivariate regression analysis suggested the fact that gender, tumor size, morphology, marginal lobulation, bronchial truncation, and vascular convergence sign are likely the independent predictors of NSCLC pathological subtypes.

Conclusions

CT combined with mpMRI can effectively distinguish NSCLC pathological subtypes, which is worthy of clinical application.

[Imaging of the lung using low-field magnetic resonance imaging]

Hintergrund

Die Untersuchung der Lunge mit der Magnetresonanztomographie (MRT) geht mit hohen Herausforderungen einher und konnte sich im klinischen Alltag bisher nicht durchsetzen. Aktuelle Entwicklungen der Niederfeld-MRT, in Kombination mit neuen computergestützten Aufnahme- und Auswertungsalgorithmen, versprechen neue Perspektiven für die bildgebende Diagnostik pulmonaler Erkrankungen.

Ziel dieser Arbeit

Diese Übersichtsarbeit soll ein Verständnis der physikalischen Vorteile der Niederfeld-MRT für die Lungenbildgebung vermitteln, einen Überblick über die spärlich vorhandenen Vorkenntnisse aus der Literatur bieten und erste Ergebnisse eines neu entwickelten Niederfeld-MRT präsentieren.

Methoden

Inhalte dieses Artikels basieren auf physikalischen Grundlagen, Recherchen in Literaturdatenbanken und eigenen Erfahrungen in der Lungenbildgebung mit einem modernen 0,55-T-MRT.

Schlussfolgerung

Die Niederfeld-MRT (< 1 T) kann technische und ökonomische Vorteile gegenüber höheren Feldstärken für die Lungenbildgebung haben. Die physikalischen Voraussetzungen sind aufgrund geringerer Suszeptibilitätseffekte, längerer transversaler Relaxationszeiten und niedrigerer spezifischer Absorptionsraten besonders für die Anatomie der Lunge vorteilhaft. Die geringeren Anschaffungs- und Betriebskosten haben zudem ein großes Potenzial, die Verfügbarkeit zu erhöhen und gleichzeitig die Nachhaltigkeit zu verbessern. Durch die Kombination moderner Sequenzen und computergestützter Auswertungen kann die morphologische Bildgebung um orts- und zeitaufgelöste funktionelle Untersuchungen der Lunge ohne Strahlenbelastung ergänzt werden. Sowohl für kritische Szenarien, wie Screening und engmaschiges Therapiemonitoring, als auch für besonders gefährdete Patientengruppen könnten Lücken geschlossen werden. Dazu gehören beispielsweise akute und chronische Lungenerkrankungen bei Kindern oder die Abklärung einer Lungenembolie bei Schwangeren.

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.

Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

Objective

To evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).

Material and Methods

A total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.

Results

The 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.

Conclusions

After algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

Magnetic Resonance Imaging Segmentation on the Basis of Boundary Tracking Algorithm in Lung Cancer Surgery

This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) (P < 0.05). The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) (P < 0.05). Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time (P < 0.05). The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.

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.

T2-weighted Lung Imaging Using a 0.55-T MRI System

Purpose

To assess a 0.55-T MRI system for imaging lung disease and to compare image quality with clinical CT scans.

Materials and Methods

In this prospective study conducted between November 2018 and December 2019, respiratory-triggered T2-weighted turbo spin-echo MRI at 0.55 T was compared with clinical CT scans in 24 participants (mean age, 59 years ± 16 [standard deviation]; 18 women) with common lung abnormalities. MR images were reviewed and scored by experienced readers. Abnormal findings identified with MRI and CT were compared using the Cohen κ statistic.

Results

High-quality structural pulmonary MR images were attained with an average acquisition time of 11 minutes ± 3. MRI generated sufficient image quality to robustly detect bronchiectasis (κ = 0.61), consolidative opacities (κ = 1.00), cavitary lesions (κ = 1.00), effusion (κ = 0.64), mucus plug (κ = 0.68), and solid scattered nodularity (κ = 0.82). Diffuse disease, including ground-glass opacities (κ = 0.57) and tree-in-bud nodules (κ = 0.48), were the findings that were most difficult to discern using MRI, with false readings in four of 18 patients for each feature. Nodule size, which was measured independently at CT and MRI, was strongly correlated (R ² = 0.99) for nodules with a measurement of 10 mm ± 5 (range, 5-23 mm).

Conclusion

This initial study indicates that high-performance 0.55-T MRI holds promise in the evaluation of common lung disease.Clinical trials registration no. NCT03331380Supplemental material is available for this article. Keywords: MRI, Pulmonary, Technology Assessment© RSNA, 2021.

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 [AUC_test =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.

The Value of PETRA in Pulmonary Nodules of <3 cm Among Patients With Lung Cancer

Objective

This study aimed to evaluate the visibility of different subgroups of lung nodules of <3 cm using the pointwise encoding time reduction with radial acquisition (PETRA) sequence on 3T magnetic resonance imaging (MRI) in comparison with that obtained using low-dose computed tomography (LDCT).

Methods

The appropriate detection rate was calculated for each of the different subgroups of lung nodules of <3 cm. The mean diameter of each detected nodule was determined. The detection rates and diameters of the lung nodules detected by MRI with the PETRA sequence were compared with those detected by computed tomography (CT). The sensitivity of detection for the different subgroups of pulmonary nodules was determined based on the location, size, type of nodules and morphologic characteristics. Agreement of nodule characteristics between CT and MRI were assessed by intraclass correlation coefficient (ICC) and Kappa test.

Results

The CT scans detected 256 lung nodules, comprising 99 solid nodules (SNs) and 157 subsolid nodules with a mean nodule diameter of 8.3 mm. For the SNs, the MRI detected 30/47 nodules of <6 mm in diameter and 52/52 nodules of ≥6 mm in diameter. For the subsolid nodules, the MRI detected 30/51 nodules of <6 mm in diameter and 102/106 nodules of ≥6 mm in diameter. The PETRA sequence returned a high detection rate (84%). The detection rates of SN, ground glass nodules, and PSN were 82%, 72%, and 94%, respectively. For nodules with a diameter of >6 mm, the sensitivity of the PETRA sequence reached 97%, with a higher rate for nodules located in the upper lung fields than those in the middle and lower lung fields. Strong agreement was found between the CT and PETRA results (correlation coefficients = 0.97).

Conclusion

The PETRA technique had high sensitivity for different type of nodule detection and enabled accurate assessment of their diameter and morphologic characteristics. It may be an effective alternative to CT as a tool for screening and follow up 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.

[When are contrast agents really needed? : Cross-sectional imaging with computed tomography and magnetic resonance imaging]

CLINICAL ISSUE

The intravenous administration of contrast agents increases the contrast between diverse tissues and vessels against their surroundings in both computed tomography (CT) and magnetic resonance imaging (MRI) scans and has been generously used for years. There are only a few scientific publications that have systematically evaluated the impact of this contrast-enhancing technique over noncontrast enhancing techniques.

RADIOLOGICAL STANDARD

According to these publications and our clinical experiences, there are far more indications to use non-contrast-enhancing techniques as they are used in clinical practice. The most important requirement to renounce the use of a contrast agent is sufficient clinical information and differentiated justified indication. The present review shows useful non-contrast-enhanced examination techniques for neuroradiology, musculoskeletal system, lymphatic system, and thorax, including the hearth, abdomen and breasts.

CLINICAL RECOMMENDATIONS

Good indications for non-contrast imaging are generally follow-ups. In cerebral related questions, like in traumatic or atraumatic emergencies, transient ischemic attacks, minor stroke diagnostic, dementia and in follow-ups of multiple sclerosis, there is usually no need for contrast agent. Examinations of the musculoskeletal systems and follow-up examinations of the lymphatic system can generally be done without a contrast agent. There is no major loss of value in CT and MRI scans of the thorax by examining without contrast. The value of using a contrast agent in the abdomen is far less than expected. Up to now use of a contrast agent is essential in evaluating questions related to vessels or angiomatous tissue and in breast MRI.

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.

Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging?

OBJECTIVE

To quantitatively compare the diagnostic values of various diffusion parameters obtained from mono- and biexponential diffusion-weighted imaging (DWI) models and diffusion kurtosis imaging (DKI) in differentiating between benign and malignant solitary pulmonary lesions (SPLs).

METHODS

Multiple b-value DWIs and DKIs were performed in 89 patients with SPL by using a 3-T magnetic resonance (MR) imaging unit. The apparent diffusion coefficient (ADC) of various b-value sets, true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), apparent diffusional kurtosis (K_app), and kurtosis-corrected diffusion coefficient (D_app) were calculated and compared between the malignant and benign groups using a Mann-Whitney U test. Receiver-operating characteristic analysis was performed for all parameters.

RESULT

The ADC_{(0, 150)} values of malignant tumors were lower than those of the benign group (p = 0.01). The ADC_{(0, 300)}, ADC_{(0, 500)}, ADC_{(0, 600),} ADC_{(0, 800)}, ADC_{(0, 1000),} ADC_total, D, and D_app of malignant tumors were significantly lower than those of benign lesions (all p < 0.001). D*, f, and K_app showed no statistically significant differences between the two groups. ADC_total showed the highest area under the curve (AUC = 0.862), followed by ADC_{(0, 800)}(AUC = 0.844), ADC_{(0, 600)}(AUC = 0.843), D(AUC = 0.834), ADC_{(0, 1000)}(AUC = 0.834) and ADC_{(0, 500)}(AUC = 0.824), D_app(AUC = 0.796), and ADC_{(0, 300)} (AUC = 0.773). However, the difference in diagnostic efficacy among these parameters was not statistically significant (p > 0.05).

CONCLUSION

Intravoxel incoherent motion (IVIM) and DKI-derived parameters have similar performance compared with conventional ADC in differentiating SPLs.

KEY POINTS

• Mono- and biexponential DWI and DKI are feasible for differentiating SPLs. • ADC _{(0, ≥500)} has better performance than ADC _{(0, <500)} in assessing SPLs. • IVIM and DKI have similar performance compared with conventional DWI in differentiating SPLs.