Screening for lung cancer: Does MRI have a role?

Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI

BACKGROUND

The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities.

PURPOSE

To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules.

STUDY TYPE

Prospective.

POPULATION

54 patients, including 21 men and 33 women.

FIELD STRENGTH/SEQUENCE

Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo.

ASSESSMENT

Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded.

STATISTICAL TESTS

The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant.

RESULTS

A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements.

DATA CONCLUSION

The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Nanoparticle trends and hotspots in lung cancer diagnosis from 2006-2023: a bibliometric analysis

Background

Lung cancer possesses the highest incidence and mortality rates among malignancies globally. Despite substantial advancements in oncology, it is frequently diagnosed at an advanced stage, resulting in a poor prognosis. Over recent decades, the swift progress of nanotechnology has precipitated the extensive utilization of nanomaterials as carriers in cancer diagnosis and therapy. The deployment of nanoparticles as an innovative diagnostic strategy aspires to enable the earlier detection of lung cancer, thereby permitting earlier intervention and enhancing prognosis. This study endeavors to deepen our understanding of this domain through a comprehensive analysis employing bibliometric tools.

Method

Related articles were retrieved from the Web of Science Core Collection from January 1st, 2006, to December 14st, 2023. Thereaf CiteSpace, VOSviewer and the online platform of bibliometrics (http://bibliometric.com/) were utilized to visually analyze Author/Country/Institutions/Cited Journals/Keyword, et al.

Results

A total of 966 articles were retrieved for this study. The analysis unveils a progressive increase in annual publications within this field, with China at the forefront in publication volume, followed by the United States and India. Moreover, Chinese research institutions, notably the Chinese Academy of Sciences and Shanghai Jiao Tong University, prevail in publication output. Upon exclusion of irrelevant search terms, keywords clustering analysis highlights that "biomarkers", "sensors", "gold nanoparticles", and "silver nanoparticles" are predominant research focuses.

Conclusion

This bibliometric study furnishes a quantitative perspective on the extant literature, serving scholars in related fields. Furthermore, it anticipates future research trend concerning nanoparticles and lung cancer diagnosis, thereby aiding in the formulation of project planning and the design of experiments.

Case report: Germline POT1 mutation in a patient with GIST and lung adenocarcinoma

The gene protection of telomere 1 (POT1) is involved in telomere maintenance and stability and plays a crucial role in the preservation of genomic stability. POT1 is considered a high-penetrance melanoma susceptibility gene; however, the number of cancer types associated with the pathogenic germline variants of POT1 is gradually increasing, including chronic lymphocytic leukemia (CLL), angiosarcomas, and gliomas, even though many associations are still elusive. Here, we reported a case of a 60-year-old man who showed early-onset multiple neoplasms, including multiple melanomas, gastrointestinal stromal tumor (GIST), and lung adenocarcinoma. Next-generation sequencing (NGS) analyses revealed a germline heterozygous pathogenic variant in the POT1 gene. Notably, GIST and lung adenocarcinoma were not previously reported in association with the POT1 germline variant. Lung cancer susceptibility syndrome is very rare and the actual knowledge is limited to a few genes although major genetic factors are unidentified. Recently, genome-wide association studies (GWAS) have pointed out an association between POT1 variants and lung cancer. This case report highlights the clinical relevance of POT1 alterations, particularly their potential involvement in lung cancer. It also suggests that POT1 testing may be warranted in patients with familial cancer syndrome, particularly those with a history of melanoma and other solid tumors.

Comparison of lung lesion assessment using free-breathing dynamic contrast-enhanced 1.5-T MRI with a golden-angle radial stack-of-stars VIBE sequence and CT

BACKGROUND

Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung.

PURPOSE

To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference.

MATERIAL AND METHODS

In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis.

RESULTS

The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05).

CONCLUSION

Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions.

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.

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

3D magnetic resonance fingerprinting on a low-field 50 mT point-of-care system prototype: evaluation of muscle and lipid relaxation time mapping and comparison with standard techniques

OBJECTIVE

To implement magnetic resonance fingerprinting (MRF) on a permanent magnet 50 mT low-field system deployable as a future point-of-care (POC) unit and explore the quality of the parameter maps.

MATERIALS AND METHODS

3D MRF was implemented on a custom-built Halbach array using a slab-selective spoiled steady-state free precession sequence with 3D Cartesian readout. Undersampled scans were acquired with different MRF flip angle patterns and reconstructed using matrix completion and matched to the simulated dictionary, taking excitation profile and coil ringing into account. MRF relaxation times were compared to that of inversion recovery (IR) and multi-echo spin echo (MESE) experiments in phantom and in vivo. Furthermore, B₀ inhomogeneities were encoded in the MRF sequence using an alternating TE pattern, and the estimated map was used to correct for image distortions in the MRF images using a model-based reconstruction.

RESULTS

Phantom relaxation times measured with an optimized MRF sequence for low field were in better agreement with reference techniques than for a standard MRF sequence. In vivo muscle relaxation times measured with MRF were longer than those obtained with an IR sequence (T₁: 182 ± 21.5 vs 168 ± 9.89 ms) and with an MESE sequence (T₂: 69.8 ± 19.7 vs 46.1 ± 9.65 ms). In vivo lipid MRF relaxation times were also longer compared with IR (T₁: 165 ± 15.1 ms vs 127 ± 8.28 ms) and with MESE (T₂: 160 ± 15.0 ms vs 124 ± 4.27 ms). Integrated ΔB₀ estimation and correction resulted in parameter maps with reduced distortions.

DISCUSSION

It is possible to measure volumetric relaxation times with MRF at 2.5 × 2.5 × 3.0 mm³ resolution in a 13 min scan time on a 50 mT permanent magnet system. The measured MRF relaxation times are longer compared to those measured with reference techniques, especially for T₂. This discrepancy can potentially be addressed by hardware, reconstruction and sequence design, but long-term reproducibility needs to be further improved.

Biomedical applications of MnO2 nanomaterials as nanozyme-based theranostics

Manganese dioxide (MnO₂) nanoenzymes/nanozymes (MnO₂-NEs) are 1-100 nm nanomaterials that mimic catalytic, oxidative, peroxidase, and superoxide dismutase activities. The oxidative-like activity of MnO₂-NEs makes them suitable for developing effective and low-cost colorimetric detection assays of biomolecules. Interestingly, MnO₂-NEs also demonstrate scavenging properties against reactive oxygen species (ROS) in various pathological conditions. In addition, due to the decomposition of MnO₂-NEs in the tumor microenvironment (TME) and the production of Mn²⁺, they can act as a contrast agent for improving clinical imaging diagnostics. MnO₂-NEs also can use as an in situ oxygen production system in TME, thereby overcoming hypoxic conditions and their consequences in the progression of cancer. Furthermore, MnO₂-NEs as a shell and coating make the nanosystems smart and, therefore, in combination with other nanomaterials, the MnO₂-NEs can be used as an intelligent nanocarrier for delivering drugs, photosensitizers, and sonosensitizers in vivo. Moreover, these capabilities make MnO₂-NEs a promising candidate for the detection and treatment of different human diseases such as cancer, metabolic, infectious, and inflammatory pathological conditions. MnO₂-NEs also have ROS-scavenging and anti-bacterial properties against Gram-positive and Gram-negative bacterial strains, which make them suitable for wound healing applications. Given the importance of nanomaterials and their potential applications in biomedicine, this review aimed to discuss the biochemical properties and the theranostic roles of MnO₂-NEs and recent advances in their use in colorimetric detection assays of biomolecules, diagnostic imaging, drug delivery, and combinatorial therapy applications. Finally, the challenges of MnO₂-NEs applications in biomedicine will be discussed.

ACR Appropriateness Criteria® Lung Cancer Screening: 2022 Update

Lung cancer remains the leading cause of cancer-related mortality for men and women in the United States. Screening for lung cancer with annual low-dose CT is saving lives, and the continued implementation of lung screening can save many more. In 2015, the CMS began covering annual lung screening for those who qualified based on the original United States Preventive Services Task Force (USPSTF) lung screening criteria, which included patients 55 to 77 year of age with a 30 pack-year history of smoking, who were either currently using tobacco or who had smoked within the previous 15 years. In 2021, the USPSTF issued new screening guidelines, decreasing the age of eligibility to 80 years of age and pack-years to 20. Lung screening remains controversial for those who do not meet the updated USPSTF criteria, but who have additional risk factors for the development of lung cancer. 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.

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.

Favorable Mortality-to-Incidence Ratio Trends of Lung Cancer in Countries with High Computed Tomography Density

Background and Objectives: The prognoses of lung cancer deteriorate dramatically as the cancer progresses through its stages. Therefore, early screening using techniques such as low-dose computed tomography (LDCT) is critical. However, the epidemiology of the association between the popularization of CT and the prognosis for lung cancer is not known. Materials and Methods: Data were obtained from GLOBOCAN and the health data and statistics of the World Health Organization. Mortality-to-incidence ratios (MIRs) and the changes in MIR over time (δMIR; calculated as the difference between MIRs in 2018 and 2012) were used to evaluate the correlation with CT density disparities via Spearman's rank correlation coefficient. Results: Countries with zero CT density presented a relatively low incidence crude rate and a relatively high MIR in 2018 and a negative δMIR. Conversely, countries with a CT density over 30 had a positive δMIR. The CT density was significantly associated with the HDI score and MIR in 2018, whereas it demonstrated no association with MIR in 2012. The CT density and δMIR also showed a significant linear correlation. Conclusions: CT density was significantly associated with lung cancer MIR in 2018 and with δMIR, indicating favorable clinical outcomes in countries in which CT has become popularized.

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

The Application and Value of 3T Magnetic Resonance Imaging in the Display of Pulmonary Nodules

Objective

The aim of this study was to evaluate the sensitivity and accuracy of multi-sequence 3T magnetic resonance imaging (MRI) in the detection of different types of pulmonary nodules.

Methods

A total of 68 patients with pulmonary nodules identified using computed tomography (CT) subsequently underwent MRI. Using CT images with a slice thickness of 1 mm as the gold standard, the sensitivity of three MRI sequences in detecting different types of pulmonary nodules was calculated, and the image quality was also evaluated. Nodule types included solid nodules, ground glass nodules (GGN), and part-solid nodules (PSN). Statistical analyses of data were conducted using the software SPSS 21.0. The intra-class correlation coefficient was calculated in order to compare the consistency of nodule size in both MRI and CT.

Results

CT detected 188 pulmonary nodules in 68 patients, including 87 solid nodules and 101 sub-solid nodules, the latter comprising 46 PSNs and 55 GGNs. The average nodule diameter was approximately 7.7 mm. The sensitivity of MRI in detecting nodules ≥ 6 mm in diameter and those of > 8 mm in diameter was 92% and 100%, respectively, and the sequence with the highest detection rate was T2-BLADE. In relation to solid nodules, the sequence with the highest detection rate was T1 Star-VIBE, while the T2-BLADE sequence demonstrated the highest detection rate of sub-solid nodules. The image quality of the T1 Star-VIBE sequence was better than that of both the T2-HASTE and the T2-BLADE sequences. The consistency of CT and MRI sequences for nodule size was high with a consistency coefficient of 0.94–0.98.

Conclusion

The detection rate of MRI for nodules with a diameter of > 8 mm was 100%. The T2-BLADE sequence had the highest detection sensitivity. The sequence with the best image quality was the T1 Star-VIBE.

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.

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.

Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning

This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In this study, on the basis of the 3D U-Net segmentation method, the multilevel boundary sensing RUNet was worked out after optimization. 92 patients with lung cancer were selected, and their clinical data were counted; meanwhile, the lung nodule detection was performed to obtain the segmentation effect under 3D U-Net. The accuracy of 3D U-Net and multilevel boundary sensing RUNet was compared on lung magnetic resonance imaging (MRI) after lung nodule segmentation. Patients with benign lung tumors were taken as controls; the blood immune biochemical indicators progastrin-releasing peptide (pro-CRP), carcinoembryonic antigen (CEA), and neuron-specific enolase (NSE) in patients with malignant lung tumors were analyzed. It was found that the accuracy, sensitivity, and specificity were all greater than 90% under the algorithm-based MRI of benign and malignant tumor patients. Based on the imaging signs for the MRI image of lung nodules, the segmentation effect of the RUNet was clearer than that of the 3D U-Net. In addition, serum levels of pro-CRP, NSE, and CAE in patients with benign lung tumors were 28.9 pg/mL, 12.5 ng/mL, and 10.8 ng/mL, respectively, which were lower than 175.6 pg/mL, 33.6 ng/mL, and 31.9 ng/mL in patients with malignant lung tumors significantly (P < 0.05). Thus, the RUNet image segmentation method was better than the 3D U-Net. The pro-CRP, CEA, and NSE could be used as diagnostic indicators for malignant lung tumors.

Differentiation of Pulmonary Lymphoma Manifestations and Nonlymphoma Infiltrates in Possible Invasive Fungal Disease Using Fast T1-weighted Magnetic Resonance Imaging at 3 T Comparison of Texture Analysis, Mapping, and Signal Intensity Quotients

PURPOSE

This study aimed to evaluate the diagnostic performance of texture analysis (TA), T1 mapping, and signal intensity quotients derived from fast T1-weighted gradient echo (T1w GRE) sequences for differentiating pulmonary lymphoma manifestations and nonlymphoma infiltrates in possible invasive fungal disease in immunocompromised hematological patients.

MATERIALS AND METHODS

Twenty patients with hematologic malignancies and concomitant immunosuppression (including 10 patients with pulmonary lymphoma manifestations and 10 patients with nonlymphoma infiltrates) prospectively underwent 3 T magnetic resonance imaging using a conventional T1w GRE sequence and a T1w GRE mapping sequence with variable flip angle. A region of interest was placed around the most representative lesion in each patient. TA was performed using PyRadiomics. T1 relaxation times were extracted from precompiled maps and calculated manually. Signal intensity quotients (lesion/muscle) were calculated from conventional T1w GRE sequences.

RESULTS

Of all TA features, variance, mean absolute deviation, robust mean absolute deviation, interquartile range, and minimum were significantly different between the 2 entities (P<0.05), with excellent diagnostic performance in receiver operating characteristic analysis (area under the curve [AUC] >80%). Neither T1 relaxation times from precompiled maps (AUC=63%; P=0.353) nor manual calculation (AUC=63%; P=0.353) nor signal intensity quotients (AUC=70%; P=0.143) yielded significant differences.

CONCLUSIONS

TA from fast T1w GRE images can differentiate pulmonary lymphoma manifestations and nonlymphoma infiltrates in possible invasive fungal disease with excellent diagnostic performance using the TA features variance, mean absolute deviation, robust mean absolute deviation, interquartile range, and minimum. Combining a fast T1w GRE sequence with TA seems to be a promising tool to differentiate these 2 entities noninvasively.

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.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

Elaboration of Multiparametric MRI-Based Radiomics Signature for the Preoperative Quantitative Identification of the Histological Grade in Patients With Non-Small-Cell Lung Cancer

BACKGROUND

The histological grading plays an essential role in the treatment decision of lung cancer. Detected tumors are usually biopsied to confirm histologic grade. How to use MRI extracted radiomics features for accurately grading lung cancer is still challenging.

PURPOSE

To examine the diagnostic utility of multiparametric MRI radiomics and clinical factors for grading non-small-cell lung cancer (NSCLC).

STUDY TYPE

Retrospective.

POPULATION

A total of 148 patients (25.7% female) with postoperative pathologically confirmed NSCLC and divided into the training cohort (N = 110) and the validation cohort (N = 38).

FIELD STRENGTH/SEQUENCE

A 1.5 T; single-shot turbo spin-echo (TSE), T2-weighted imaging (T2WI), and integrated shimming-echo planar imaging (ISHIM-EPI) diffusion-weighted imaging (DWI).

ASSESSMENT

A total of 2775 radiomics features were extracted from carcinomatous regions of interest on T2WI, DWI, and the apparent diffusion coefficient (ADC) maps. The five optimal features were selected by using the Student' s t-test, the least absolute shrinkage and selection operator (LASSO) and stepwise regression. The Radscore combined with clinical factors, which selected by univariate and multivariate analyses, to develop a radiomics-clinical nomogram. Its performance was evaluated in the training cohort and the validation cohort. The potential clinical usefulness was analyzed by the receiver operating characteristic curve (ROC), area under the curve (AUC), and the Hosmer-Lemeshow test.

STATISTICAL TESTS

Student's t-test, univariate analyses, multivariate analyses, LASSO, ROC, AUC, and the Hosmer-Lemeshow test. P < 0.05 was considered statistically significant.

RESULTS

Favorable discrimination performance was obtained for five optimal features (out of the 2775 features), using the training cohorts (AUC 0.761) and validation cohorts (AUC 0.753). In addition, the radiomics-clinical nomogram significantly improved the ability to identify histological grades in the training cohort (AUC 0.814) and the validation cohort (AUC 0.767).

DATA CONCLUSIONS

The radiomics-clinical nomogram based on multiparametric MRI might have the potential to distinguish the histological grade of NSCLC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination

The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.

Ultrashort echo time MRI of the lung in children and adolescents: comparison with non-enhanced computed tomography and standard post-contrast T1w MRI sequences

Objectives

To compare the diagnostic value of ultrashort echo time (UTE) magnetic resonance imaging (MRI) for the lung versus the gold standard computed tomography (CT) and two T1-weighted MRI sequences in children.

Methods

Twenty-three patients with proven oncologic disease (14 male, 9 female; mean age 9.0 + / − 5.4 years) received 35 low-dose CT and MRI examinations of the lung. The MRI protocol (1.5-T) included the following post-contrast sequences: two-dimensional (2D) incoherent gradient echo (GRE; acquisition with breath-hold), 3D volume interpolated GRE (breath-hold), and 3D high-resolution radial UTE sequences (performed during free-breathing). Images were evaluated by considering image quality as well as distinct diagnosis of pulmonary nodules and parenchymal areal opacities with consideration of sizes and characterisations.

Results

The UTE technique showed significantly higher overall image quality, better sharpness, and fewer artefacts than both other sequences. On CT, 110 pulmonary nodules with a mean diameter of 4.9 + / − 2.9 mm were detected. UTE imaging resulted in a significantly higher detection rate compared to both other sequences (p < 0.01): 76.4% (84 of 110 nodules) for UTE versus 60.9% (67 of 110) for incoherent GRE and 62.7% (69 of 110) for volume interpolated GRE sequences. The detection of parenchymal areal opacities by the UTE technique was also significantly higher with a rate of 93.3% (42 of 45 opacities) versus 77.8% (35 of 45) for 2D GRE and 80.0% (36 of 45) for 3D GRE sequences (p < 0.05).

Conclusion

The UTE technique for lung MRI is favourable in children with generally high diagnostic performance compared to standard T1-weighted sequences as well as CT.

Key Points

• Due to the possible acquisition during free-breathing of the patients, the UTE MRI sequence for the lung is favourable in children.

• The UTE technique reaches higher overall image quality, better sharpness, and lower artefacts, but not higher contrast compared to standard post-contrast T1-weighted sequences.

• In comparison to the gold standard chest CT, the detection rate of small pulmonary nodules small nodules ≤ 4 mm and subtle parenchymal areal opacities is higher with the UTE imaging than standard T1-weighted sequences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08236-7.

Feasibility of 31 P spectroscopic imaging at 7 T in lung carcinoma patients

Currently, it is difficult to predict effective therapy response to molecular therapies for the treatment of lung cancer based solely on anatomical images. ³¹ P MR spectroscopic imaging could provide as a non-invasive method to monitor potential biomarkers for early therapy evaluation, a necessity to improve personalized care and reduce cost. However, surface coils limit the imaging volume in conventional ³¹ P MRSI. High-energetic adiabatic RF pulses are required to achieve flip angle homogeneity but lead to high SAR. Birdcage coils permit use of conventional amplitude modulated pulses, even over large FOV, potentially decreasing overall SAR massively. Here, we investigate the feasibility of 3D ³¹ P MRSI at 7 T in lung carcinoma patients using an integrated ³¹ P birdcage body coil in combination with either a dual-coil or a 16-channel receiver. Simulations showed a maximum decrease in SNR per unit of time of 8% for flip angle deviations in short TR low flip-angle excitation 3D CSI. The minimal SNR loss allowed for fast 3D CSI without time-consuming calibration steps (>10:00 min.). ³¹ P spectra from four lung carcinoma patients were acquired within 29:00 minutes and with high SNR using both receivers. The latter allowed discrimination of individual phosphodiesters, inorganic phosphate, phosphocreatine and ATP. The receiver array allowed for an increased FOV compared to the dual-coil receiver. 3D ³¹ P-CSI were acquired successfully in four lung carcinoma patients using the integrated ³¹ P body coil at ultra-high field. The increased spectral resolution at 7 T allowed differentiation of multiple ³¹ P metabolites related to phospholipid and energy metabolism. Simulations provide motivation to exclude ³¹ P B₁ calibrations, potentially decreasing total scan duration. Employing large receiver arrays improves the field of view allowing for full organ coverage. ³¹ P MRSI is feasible in lung carcinoma patients and has potential as a non-invasive method for monitoring personalized therapy response in lung tumors.

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.

Value of 18F-FDG Hybrid PET/MR in Differentiated Thyroid Cancer Patients with Negative 131I Whole-Body Scan and Elevated Thyroglobulin Levels

Purpose

To evaluate the diagnostic performance of ¹⁸F-FDG PET/MR in detecting recurrent or metastatic disease in patients with differentiated thyroid cancer (DTC) who have increased thyroglobulin (Tg) levels but a negative ¹³¹I whole-body scan (WBS). The relationship between ¹⁸F-FDG PET/MR and serum Tg levels was explored. We also evaluated the therapeutic impact of PET/MR on patient clinical management.

Patients and Methods

Twenty-nine DTC patients with a negative ¹³¹I-WBS of the last post-therapeutic and increased Tg levels under thyroid-stimulating hormone suppression treatment who underwent ¹⁸F-FDG PET/MR examination were retrospectively analyzed.

Results

Of those 29 patients, ¹⁸F-FDG PET/MR findings were true positive, true negative, false positive, and false negative in 18, 7, 2, and 2 patients, respectively. The overall sensitivity, specificity, and accuracy were 90.0%, 77.8%, and 86.2%, respectively. We noticed significant differences in serum Tg levels between the PET/MR-positive and PET/MR-negative patient groups (P=0.049). Receiver operating characteristic curve analysis showed that a Tg level of 2.4 ng/mL was the optimal cut-off value for predicting PET/MR results. The sensitivity, specificity, and accuracy of PET/MR were higher in patients with Tg levels greater than 2.4 ng/mL than in patients with lower levels. By detecting recurrent or metastatic disease, ¹⁸F-FDG PET/MR altered the clinical management in 7 patients (24.1%) of the overall population.

Conclusion

¹⁸F-FDG PET/MR has high diagnostic accuracy for detecting recurrent or metastatic diseases in DTC patients and is useful for clinical management.

EGFR-specific single-chain variable fragment antibody-conjugated Fe3O4/Au nanoparticles as an active MRI contrast agent for NSCLC

Overexpression of epidermal growth factor receptor (EGFR) is closely associated with a poor prognosis in non-small cell lung cancer (NSCLC), thus making it a promising biomarker for NSCLC diagnosis. Here, we conjugated a single-chain antibody (scFv) targeting EGFR with Fe₃O₄/Au nanoparticles to form an EGFR-specific molecular MRI bioprobe (scFv@Fe₃O₄/Au) to better detect EGFR-positive NSCLC tumors in vivo. In vitro, we demonstrated that the EGFR-specific scFv could specifically deliver Fe₃O₄/Au to EGFR-positive NSCLC cells. In vivo experiments showed that the accumulation of scFv@Fe₃O₄/Au in tumor tissue was detectable by magnetic resonance imaging (MRI) at the indicated time points after systemic injection. The T2W signal-to-noise ratio (SNR) of EGFR-positive SPC-A1 tumors was significantly decreased after scFv@Fe₃O₄/Au injection, which was not observed in the tumors of mice injected with BSA@Fe₃O₄/Au. Furthermore, transmission electron microscopy (TEM) analysis showed the specific localization of scFv@Fe₃O₄/Au in the SPC-A1 tumor cell cytoplasm. Collectively, the results of our study demonstrated that scFv@Fe₃O₄/Au might be a useful probe for the noninvasive diagnosis of EGFP-positive NSCLC.

[Advances in Diagnosis and Management of Subcentimeter Pulmonary Nodules]

With the widespread use of high-resolution multislice spiral computed tomography and the popularization of regular physical examinations, the prevalence of clinically diagnosed subcentimeter pulmonary nodules is increasing. Subcentimeter pulmonary nodules have low malignant probability, however, the diagnosis and management are of high difficulty and it is likely to misdiagnose and miss malignant nodules. Therefore, the evaluation and management of subcentimeter pulmonary nodules have always been the key points of clinical work. This article reviews and summarizes the progress in the evaluation and management of subcentimeter pulmonary nodules.

Diffusion-weighted MRI improves response assessment after definitive radiotherapy in patients with NSCLC

Background

Computed tomography (CT) is the standard procedure for follow-up of non-small-cell lung cancer (NSCLC) after radiochemotherapy. CT has difficulties differentiating between tumor, atelectasis and radiation induced lung toxicity (RILT). Diffusion-weighted imaging (DWI) may enable a more accurate detection of vital tumor tissue. The aim of this study was to determine the diagnostic value of MRI versus CT in the follow-up of NSCLC.

Methods

Twelve patients with NSCLC stages I-III scheduled for radiochemotherapy were enrolled in this prospective study. CT with i.v. contrast agent and non enhanced MRI were performed before and 3, 6 and 12 months after treatment. Standardized ROIs were used to determine the apparent diffusion weighted coefficient (ADC) within the tumor. Tumor size was assessed by the longest longitudinal diameter (LD) and tumor volume on DWI and CT. RILT was assessed on a 4-point-score in breath-triggered T2-TSE and CT.

Results

There was no significant difference regarding LD and tumor volume between MRI and CT (p ≥ 0.6221, respectively p ≥ 0.25). Evaluation of RILT showed a very high correlation between MRI and CT at 3 (r = 0.8750) and 12 months (r = 0.903). Assessment of the ADC values suggested that patients with a good tumor response have higher ADC values than non-responders.

Conclusions

DWI is equivalent to CT for tumor volume determination in patients with NSCLC during follow up. The extent of RILT can be reliably determined by MRI. DWI could become a beneficial method to assess tumor response more accurately. ADC values may be useful as a prognostic marker.

Artificial Intelligence Tools for Refining Lung Cancer Screening

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.

Detecting small pulmonary nodules with spiral ultrashort echo time sequences in 1.5 T MRI

OBJECTIVE

This study investigated ultrashort echo time (UTE) sequences in 1.5 T magnetic resonance imaging (MRI) for small lung nodule detection.

MATERIALS AND METHODS

A total of 120 patients with 165 small lung nodules before video-associated thoracoscopic resection were enrolled. MRI sequences included conventional volumetric interpolated breath-hold examination (VIBE, scan time 16 s), spiral UTE (TE 0.05 ms) with free-breathing (scan time 3.5-5 min), and breath-hold sequences (scan time 20 s). Chest CT provided a standard reference for nodule size and morphology. Nodule detection sensitivity was evaluated on a lobe-by-lobe basis.

RESULTS

The nodule detection rate was significantly higher in spiral UTE free-breathing (> 78%, p < 0.05) and breath-hold sequences (> 75%, p < 0.05) compared with conventional VIBE (> 55%), reaching 100% when nodule size was > 16 mm, and reaching 95% when nodules were in solid morphology, regardless of size. The inter-sequence reliability between free-breathing and breath-hold spiral UTE was good (κ > 0.80). Inter-reader agreement was also high (κ > 0.77) for spiral UTE sequences. Nodule size measurements were consistent between CT and spiral UTE MRI, with a minimal bias up to 0.2 mm.

DISCUSSION

Spiral UTE sequences detect small lung nodules that warrant surgery, offers realistic scan times for clinical work, and could be implemented as part of routine lung MRI.

A review of the role of MRI in diagnosis and treatment of early stage lung cancer

Despite magnetic resonance imaging (MRI) being a mainstay in the oncologic care for many disease sites, it has not routinely been used in early lung cancer diagnosis, staging, and treatment. While MRI provides improved soft tissue contrast compared to computed tomography (CT), an advantage in multiple organs, the physical properties of the lungs and mediastinum create unique challenges for lung MRI. Although multi-detector CT remains the gold standard for lung imaging, advances in MRI technology have led to its increased clinical relevance in evaluating early stage lung cancer. Even though positron emission tomography is used more frequently in this context, functional MR imaging, including diffusion-weighted MRI and dynamic contrast-enhanced MRI, are emerging as useful modalities for both diagnosis and evaluation of treatment response for lung cancer. In parallel with these advances, the development of combined MRI and linear accelerator devices (MR-linacs), has spurred the integration of MRI into radiation treatment delivery in the form of MR-guided radiotherapy (MRgRT). Despite challenges for MRgRT in early stage lung cancer radiotherapy, early data utilizing MR-linacs shows potential for the treatment of early lung cancer. In both diagnosis and treatment, MRI is a promising modality for imaging early lung cancer.

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

Pulmonary MRI provides structural and quantitative functional images of the lungs without ionizing radiation, but it has had limited clinical use due to low signal intensity from the lung parenchyma. The lack of radiation makes pulmonary MRI an ideal modality for pediatric examinations, pregnant women, and patients requiring serial and longitudinal follow-up. Fortunately, recent MRI techniques, including ultrashort echo time and zero echo time, are expanding clinical opportunities for pulmonary MRI. With the use of multicoil parallel acquisitions and acceleration methods, these techniques make pulmonary MRI practical for evaluating lung parenchymal and pulmonary vascular diseases. The purpose of this Fleischner Society position paper is to familiarize radiologists and other interested clinicians with these advances in pulmonary MRI and to stratify the Society recommendations for the clinical use of pulmonary MRI into three categories: (a) suggested for current clinical use, (b) promising but requiring further validation or regulatory approval, and (c) appropriate for research investigations. This position paper also provides recommendations for vendors and infrastructure, identifies methods for hypothesis-driven research, and suggests opportunities for prospective, randomized multicenter trials to investigate and validate lung MRI methods.

Neuromodulation and the Epidemiology of Magnetic Resonance Utilization for Lung, Breast, Colon, and Prostate Cancer

BACKGROUND

Neuromodulation is a growing therapeutic modality for the treatment of chronic pain. Determining whether a patient is an appropriate candidate for implantation of a neuromodulatory device and whether the device requires an MRI conditional feature necessitates understanding the patient's likelihood of requiring an MRI. Active treatment of cancer represents known high-risk clinical scenarios for MRI. However, the growth of MRI as a tool for diagnosis of cancer also warrants consideration by implanting physicians when assessing high-risk patients.

MATERIALS AND METHODS

Here, we conduct a systematic review of the literature to determine the epidemiology for MR utilization for breast, lung, prostate, and colon cancer. Out of 126 papers reviewed, 39 were ultimately analyzed to determine the relative likelihood of an MRI in the course of oncologic care.

RESULTS

We find that there is a low likelihood for MRI to be utilized as part of any screening process and a variable likelihood during the staging and surveillance phases across all cancer subtypes depending on the clinical circumstances. Certain populations present special consideration for MRI screening, such as the high at-risk breast cancer population, and MRI surveillance and staging, such as aging males (>50 years old) at risk for prostate cancer or individuals diagnosed with rectal cancers.

CONCLUSION

High likelihood of MRI within the oncologic context represents important distinction criteria for neuromodulation as patients may benefit from implantation of an MR conditional system.

Proton MRI of the Lung: How to Tame Scarce Protons and Fast Signal Decay

Pulmonary proton MRI techniques offer the unique possibility of assessing lung function and structure without the requirement for hyperpolarization or dedicated hardware, which is mandatory for multinuclear acquisition. Five popular approaches are presented and discussed in this review: 1) oxygen enhanced (OE)-MRI; 2) arterial spin labeling (ASL); 3) Fourier decomposition (FD) MRI and other related methods including self-gated noncontrast-enhanced functional lung (SENCEFUL) MR and phase-resolved functional lung (PREFUL) imaging; 4) dynamic contrast-enhanced (DCE) MRI; and 5) ultrashort TE (UTE) MRI. While DCE MRI is the most established and well-studied perfusion measurement, FD MRI offers a free-breathing test without any contrast agent and is predestined for application in patients with renal failure or with low compliance. Additionally, FD MRI and related methods like PREFUL and SENCEFUL can act as an ionizing radiation-free V/Q scan, since ventilation and perfusion information is acquired simultaneously during one scan. For OE-MRI, different concentrations of oxygen are applied via a facemask to assess the regional change in T₁ , which is caused by the paramagnetic property of oxygen. Since this change is governed by a combination of ventilation, diffusion, and perfusion, a compound functional measurement can be achieved with OE-MRI. The known problem of fast T₂ * decay of the lung parenchyma leading to a low signal-to-noise ratio is bypassed by the UTE acquisition strategy. Computed tomography (CT)-like images allow the assessment of lung structure with high spatial resolution without ionizing radiation. Despite these different branches of proton MRI, common trends are evident among pulmonary proton MRI: 1) free-breathing acquisition with self-gating; 2) application of UTE to preserve a stronger parenchymal signal; and 3) transition from 2D to 3D acquisition. On that note, there is a visible convergence of the different methods and it is not difficult to imagine that future methods will combine different aspects of the presented methods.

Performing clinical 18F-FDG-PET/MRI of the mediastinum optimising a dedicated, patient-friendly protocol

OBJECTIVE

To construct a mediastinal-specific fluorine-18-fluorodeoxyglucose (F-FDG)-PET/MR protocol with high-quality MRI of minimal acquisition-time and comparable diagnostic value to F-FDG-PET/computed tomography (CT).

MATERIALS AND METHODS

Fifteen healthy participants received PET/MRI and 10 patients with mediastinal tumours (eight non-small-cell lung, two oesophageal cancer) received F-FDG-PET/MRI immediately after F-FDG-PET/CT. Sequences volume interpolated breath-hold examination (T1-VIBE) and Half-Fourier acquisition single-shot turbo spin echo (T2-HASTE) were optimised by varying the parameters: breath-hold (BH, end-expiration), fat suppression (spectral adiabatic inversion recovery), and ECG-triggering (ECG, end-diastole). Image quality (IQ) of each sequence-variation was qualitatively scored by medical experts and quantitatively assessed by calculating signal-to-noise ratios, contrast relative to muscle, standardized-uptake-value, and tumour-to-blood ratios. Patient comfort was evaluated on patients' experience. Diagnostic accuracy of F-FDG-PET/MRI was compared to F-FDG-PET/CT, in reference to histopathology/cytopathology.

RESULTS

ECG-triggered T1-VIBE images showed the highest signal-to-noise ratio (P < 0.01) and the largest contrast between mediastinal soft-tissues, regardless of BH or free-breathing acquisition. IQ of ECG-triggered T1-VIBE scans in BH were scored qualitatively highest with good reader agreement (κ = 0.62). IQ of T2-HASTE was not significantly affected by BH acquisition (P > 0.9). Qualitative IQ of T1-VIBE and T2-HASTE declined after spectral adiabatic inversion recovery fat-suppression. All patients could maintain BH at end-expiration and reported no discomfort. Diagnostic performance of F-FDG-PET/MR was not significantly different from F-FDG-PET/CT with comparable staging, standardized-uptake-values, and tumour-to-blood ratios. However, T-status was more often over-staged on F-FDG-PET/CT, while N-status was more frequently under-staged on F-FDG-PET/MR.

CONCLUSION

ECG-triggered T1-VIBE sequences acquired during short, multiple BHs are recommended for mediastinal imaging using F-FDG-PET/MR. With dedicated protocols, F-FDG-PET/MRI will be useful in thoracic oncology and aid in diagnostic evaluation and tailored treatment decision-making.

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.

[Image-based screening]

Screening is a special issue in medical questions concerning disease prevention. Preconditions for screening are clearly defined by the World Health Organization. High prevalence, effectiveness of therapy, availability of accepted test procedure and consensus concerning the economic concerns are necessary for successful implementation of a screening program. Preventive diagnostic studies can only be understood if one is familiar with the statistical terms sensitivity, specificity, prevalence, incidence and bias (especially overdiagnosis and lead time bias). Aspects of radiation protection are especially important in asymptomatic volunteers. The new radiation protection law in Germany also gives the opportunity to define new screening procedures even with use of radiation exposure in individual prevention programs. Potential diseases for radiological secondary prevention with high mortality are malignant tumors (especially breast cancer, lung cancer, colorectal cancer) and cardiovascular diseases (coronary heart disease, stroke or aortic aneurysm).

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

BACKGROUND

There is increased interest in pulmonary magnetic resonance imaging (MRI) as a radiation-free alternative to computed tomography (CT) for lung cancer screening.

PURPOSE

To analyze MRI characteristics of pulmonary nodules with different non-enhanced sequences.

MATERIAL AND METHODS

Eighty-two participants of a lung cancer screening were included. MRI datasets of 32 individuals with 46 different nodules ≥ 6 mm were prospectively evaluated together with 50 controls by two readers. Acquired sequences were T2- short tau inversion recovery (STIR), T2, balanced steady-state free precession (bSSFP), 3D-T1, and diffusion-weighted imaging (DWI). Each sequence was randomly and separately viewed blinded to low-dose CT (LDCT). Size, shape, and contrast of nodules were evaluated on each sequence and then correlated with LDCT and histopathology.

RESULTS

All eight carcinomas were detected by T2-STIR, T2, and bSSFP, and 7/8 by 3D-T1. Contrast was significantly higher for malignant nodules on all sequences. The highest contrast ratio between malignant and benign nodules was provided by T2-STIR. Of eight carcinomas, seven showed restricted diffusion. Size measurement correlated significantly between MRI and LDCT. Sensitivity/specificity for nodules ≥ 6 mm was 85-89%/92-94% for T2-STIR, 80-87%/93-96% for T2, 65-70%/96-98% for bSSFP, and 63-67%/96-100% for 3D-T1. Seven of eight subsolid nodules were visible on T2-sequences with significantly lower lesion contrast compared to solid nodules. Two of eight subsolid nodules were detected by bSFFP, none by 3D-T1. All three calcified nodules were detected by 3D-T1, one by bSSFP, and none by T2-sequences.

CONCLUSION

Malignant as well as calcified and subsolid nodules seem to have distinctive characteristics on different MRI sequences. T2-imaging was most suitable for the detection of nodules ≥ 6 mm.

Fluorine 18-FDG PET/CT and Diffusion-weighted MRI for Malignant versus Benign Pulmonary Lesions: A Meta-Analysis

Purpose To perform a meta-analysis of the literature to compare the diagnostic performance of fluorine 18 fluorodeoxyglucose PET/CT and diffusion-weighted (DW) MRI in the differentiation of malignant and benign pulmonary nodules and masses. Materials and Methods Published English-language studies on the diagnostic accuracy of PET/CT and/or DW MRI in the characterization of pulmonary lesions were searched in relevant databases through December 2017. The primary focus was on studies in which joint DW MRI and PET/CT were performed in the entire study population, to reduce interstudy heterogeneity. For DW MRI, lesion-to-spinal cord signal intensity ratio and apparent diffusion coefficient were evaluated; for PET/CT, maximum standard uptake value was evaluated. The pooled sensitivities, specificities, diagnostic odds ratios, and areas under the receiver operating characteristic curve (AUCs) for PET/CT and DW MRI were determined along with 95% confidence intervals (CIs). Results Thirty-seven studies met the inclusion criteria, with a total of 4224 participants and 4463 lesions (3090 malignant lesions [69.2%]). In the primary analysis of joint DW MRI and PET/CT studies (n = 6), DW MRI had a pooled sensitivity and specificity of 83% (95% CI: 75%, 89%) and 91% (95% CI: 80%, 96%), respectively, compared with 78% (95% CI: 70%, 84%) (P = .01 vs DW MRI) and 81% (95% CI: 72%, 88%) (P = .056 vs DW MRI) for PET/CT. DW MRI yielded an AUC of 0.93 (95% CI: 0.90, 0.95), versus 0.86 (95% CI: 0.83, 0.89) for PET/CT (P = .001). The diagnostic odds ratio of DW MRI (50 [95% CI: 19, 132]) was superior to that of PET/CT (15 [95% CI: 7, 32]) (P = .006). Conclusion The diagnostic performance of diffusion-weighted MRI is comparable or superior to that of fluorine 18 fluorodeoxyglucose PET/CT in the differentiation of malignant and benign pulmonary lesions. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Schiebler in this issue.