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.

Chest Magnetic Resonance Imaging: Advances and Clinical Care

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

Imaging of Solid Pulmonary Nodules

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Lung Magnetic Resonance Imaging: Technical Advancements and Clinical Applications

ABSTRACT

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

Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules

Background

In this study, we developed and validated machine learning (ML) models by combining radiomic features extracted from magnetic resonance imaging (MRI) with clinicopathological factors to assess pulmonary nodule classification for benign malignant diagnosis.

Methods

A total of 333 consecutive patients with pulmonary nodules (233 in the training cohort and 100 in the validation cohort) were enrolled. A total of 2,824 radiomic features were extracted from the MRI images (CE T1w and T2w). Logistic regression (LR), Naïve Bayes (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers were used to build the predictive models, and a radiomics score (Rad-score) was obtained for each patient after applying the best prediction model. Clinical factors and Rad-scores were used jointly to build a nomogram model based on multivariate logistic regression analysis, and the diagnostic performance of the five prediction models was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

A total of 161 women (48.35%) and 172 men (51.65%) with pulmonary nodules were enrolled. Six important features were selected from the 2,145 radiomic features extracted from CE T1w and T2w images. The XGBoost classifier model achieved the highest discrimination performance with AUCs of 0.901, 0.906, and 0.851 in the training, validation, and test cohorts, respectively. The nomogram model improved the performance with AUC values of 0.918, 0.912, and 0.877 in the training, validation, and test cohorts, respectively.

Conclusion

MRI radiomic ML models demonstrated good nodule classification performance with XGBoost, which was superior to that of the other four models. The nomogram model achieved higher performance with the addition of clinical information.

Area-Detector Computed Tomography for Pulmonary Functional Imaging

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

BACKGROUND

Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.

RESEARCH QUESTION

What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?

STUDY DESIGN AND METHODS

This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.

RESULTS

Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).

INTERPRETATION

An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier; No.: NCT02013063.

Effect of contrast medium on early detection and analysis of mediastinal lymph nodes in computed tomography

OBJECTIVE

This study aimed to evaluate the diagnostic efficiency of contrast-to-noise and signal-to-noise ratios created by the contrast medium in detecting lymph nodes.

METHODS

In this study, 57 short-axis subcentimeter lymph nodes in 40 cardiac computed tomography patients with noncontrast- and contrast-enhanced phases were evaluated. The contrast-to-noise ratios and signal-to-noise ratios of noncontrast- and contrast-enhanced lymph node-mediastinal fat and aortic-mediastinal fat tissues were determined. In addition, lymph nodes in noncontrast- and contrast-enhanced series were evaluated subjectively.

RESULTS

There was a significant difference in lymph node-mediastinal fat signal-to-noise values between the contrast and noncontrast phases (p=0.0002). In the contrast phase, aortic density values were found to be 322.04±18.51 HU, lymph node density values were 76.41±23.41 HU, and mediastinal adipose tissue density values were -65.73±22.96 HU. Aortic-mediastinal fat contrast-to-noise ratio value was 20.23±6.92 and the lymph node-mediastinal fat contrast-to-noise ratio value was 6.43±2.07. A significant and moderate correlation was observed between aortic-mediastinal fat and lymph node-mediastinal fat contrast-to-noise ratio values in the contrast phase (r=0.605; p<0.001). In the contrast-enhanced series, there was a significant increase in the subjective detection of lymph nodes (p=0.0001).

CONCLUSION

In the detection of paratracheal lymph nodes, the contrast agent increases the detection of short-axis subcentimeter lymph nodes quantitatively and qualitatively. Contrast enhances and facilitates the detection of paratracheal lymph nodes.

Radiomic Analysis of Pulmonary Nodules for Distinguishing Malignancy From Benignancy: The Value of Using Iodine Maps From Dual-Energy Computed Tomography

OBJECTIVE

The aim of the study is to investigate the diagnostic accuracy of radiomics on iodine maps from dual-energy computed tomography (DECT) in distinguishing lung cancer from benign pulmonary nodules.

METHODS

This retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 109 patients with 55 malignant nodules and 62 benign nodules underwent contrast-enhanced DECT. Eight iodine uptake parameters on iodine maps generated by DECT were calculated and established a predictive model. Eighty-seven radiomics features of entire tumor were extracted from iodine maps and established a radiomics model. The iodine uptake model and radiomics model were independently built based on the highly reproducible features using the least absolute shrinkage and selection operator method. The diagnostic accuracy of 2 models were assessed using receiver operating curve analysis. For external validation, 47 patients (25 benign and 22 malignant) from another hospital were assigned to testing data set.

RESULTS

All iodine uptake features showed significant association with malignancy (P < 0.01) and 2 selected features (mean value of virtual noncontrast images and mean value of vital part on contrast-enhanced image) constituted the iodine model. The radiomics model comprised 2 features (original shape sphericity and original glszm small area high gray level emphasis), which showed good discrimination both in the training cohort (area under the curve, 0.957) and validation cohort (area under the curve, 0.800). Radiomics model showed superior performance than iodine uptake model (accuracy, 89.7% vs 80.6%).

CONCLUSIONS

Radiomics model extracted from iodine maps provided a robust diagnostic tool for discriminating pulmonary malignant nodules and had high potential in clinical application.

Differential Diagnosis of Preinvasive Lesions in Small Pulmonary Nodules by Dual Source Computed Tomography Imaging

This study was aimed to explore the differential diagnosis value of preinvasive lesions/minimally invasive adenocarcinoma and invasive adenocarcinoma manifesting as small pulmonary nodules under dual source computed tomography (DSCT) imaging. The patients with nodular manifestations of adenocarcinoma in situ (AIS)/microinfiltrating adenocarcinoma (MIA) were selected as group X, including 14 cases. A total of 31 cases with nodular infiltrating adenocarcinoma were selected as group Y. The enhanced dual-energy image obtained by DSCT dual-energy scan was transferred to the software to obtain the energy image and iodine distribution map. SPSS 18.0 was used for statistical analysis. P < 0.05 was considered statistically significant. All measurements were labeled as mean x͞±S standard deviation. In the CT findings of microinfiltrating adenocarcinoma and infiltrating adenocarcinoma, lobulation sign, burr sign, vacuole sign, and pleural depression sign can help the diagnosis of infiltrating adenocarcinoma. The results showed that lobulation sign, burr sign, vacuole sign, and pleural depression sign could be used as the distinguishing feature of preinvasive lesion/microinvasive adenocarcinoma and invasive adenocarcinoma. Receiver-operating characteristic (ROC) curve analysis showed that the critical value, sensitivity, and specificity of lesion diameter ≥1.4 cm and CT value ≥14.14HU for diagnosis of invasive lung adenocarcinoma were 1.32 and 14.14, 88.4% and 94.4%, and 67.3% and 75.8%, respectively. There were substantial differences in CT values between the two groups under low energy level (42-99 kev) (P < 0.05). DSCT dual-energy imaging can quantitatively identify preinvasive pulmonary nodules with multiple parameters.

Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules

Objective

This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention.

Methods

Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’.

Results

The image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset.

Conclusion

The deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

BACKGROUND

The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT).

PATIENTS AND METHODS

Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic ¹⁸F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUV_max) was measured on conventional ¹⁸F-FDG PET/CT images. The influx constant (K_i ) was calculated from the dynamic ¹⁸F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test.

RESULTS

Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUV_max values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average K_i values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min^-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613).

CONCLUSIONS

Dynamic ¹⁸F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.

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.

Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer

RATIONALE AND OBJECTIVES

To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer.

MATERIALS AND METHODS

This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [T_max] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs).

RESULTS

There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower T_max (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either T_max (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011).

CONCLUSION

This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.

Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules

OBJECTIVE

To test the performance of free-breathing Dynamic Contrast-Enhanced MRI (DCE-MRI) using a radial volumetric interpolated breath-hold examination (VIBE) sequence combined with diffusion-weighted imaging (DWI) for quantitative solitary pulmonary nodule (SPN) assessment.

METHODS

A total of 67 SPN cases receiving routine MRI routine scans, DWI, and dynamic-enhanced MRI in our hospital from May 2017 to November 2018 were collected. These cases were divided into a malignant group and a benign group according to the characteristics of the SPNs. The quantitative DCE-MRI parameters (K^trans, K_ep, V_e) and apparent diffusion coefficient (ADC) values of the nodules were measured.

RESULTS

The K^trans and K_ep values in the malignant group were higher than those in the benign group, while the ADC values in the malignant group were lower than those in the benign group. Furthermore, the K^trans value of adenocarcinoma was higher than that of squamous cell carcinoma and small cell carcinoma (P < 0.05). The V_e value was significantly different between non-small cell carcinoma and small cell carcinoma (P < 0.05). With an ADC value of 0.98 × 10^-3 mm²/s as the threshold, the specificity and sensitivity to diagnose benign and malignant nodules was 90.6% and 80%, respectively.

CONCLUSION

High-temporal-resolution DCE-MRI using the r-VIBE technique in combination with DWI could contribute to pulmonary nodule analysis and possibly serve as a potential alternative to distinguish malignant from benign nodules as well as differentiate different types of malignancies.

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.

Assessment of Solitary Pulmonary Nodules Based on Virtual Monochrome Images and Iodine-Dependent Images Using a Single-Source Dual-Energy CT with Fast kVp Switching

With lung cancer being the most common malignancy diagnosed worldwide, lung nodule assessment has proved to be one of big challenges of modern medicine. The aim of this study was to examine the usefulness of Dual Energy Computed Tomography (DECT) in solitary pulmonary nodule (SPN) assessment. Between January 2017 and June 2018; 65 patients (42 males and 23 females) underwent DECT scans in the late arterial phase (AP) and venous phase (VP). We concluded that imaging at an energy level of 65 keV was the most accurate in detecting malignancy in solitary pulmonary nodules (SPNs) measuring ≤30 mm in diameter on virtual monochromatic maps. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. As for iodine concentration maps, the analysis of venous phase images resulted in the highest clinical usefulness. To summarize, DECT may be a useful tool in the differentiation of benign and malignant SPNs. A single-phase DECT examination with scans acquired 90 s after contrast media injection is recommended.

Delayed-Phase Enhancement for Evaluation of Malignant Pleural Mesothelioma on Computed Tomography: A Prospective Cohort Study

BACKGROUND

Radiologic assessment of malignant pleural mesothelioma (MPM) on computed tomography (CT) imaging can be limited by similar attenuations of MPM and adjacent tissues. This can result in inaccuracies in defining the presence and extent of pleural tumor burden. We hypothesized that increasing the time delay for pleural enhancement will optimize discrimination between MPM and noncancerous tissues on CT. Here we conduct a prospective observational study to determine the optimal time delay for imaging MPM on CT.

PATIENTS AND METHODS

Adult MPM patients (n = 15) were enrolled in this prospective exploratory imaging trial. Patients with < 1 cm MPM thickness, prior pleurectomy, pleurodesis, pleural radiotherapy, or antiangiogenic therapy were excluded. All patients underwent a dynamically-enhanced CT with multiple time delays (0 - 10 minutes) after intravenous contrast administration. Tumor tissue attenuation was measured at each phase of enhancement. A qualitative assessment of tumor enhancement kinetics was also performed. The optimal phase of enhancement based on qualitative lesion conspicuity and quantitative tumor enhancement was then compared.

RESULTS

MPM tumor enhancement was quantitatively and qualitatively increased at time delays beyond the conventional time delay for thoracic CT imaging (40-60 seconds). Patient tumor enhancement kinetics, displayed as the fraction of maximal tumor tissue attenuation as a function of time, revealed an optimal time delay of 230 to 300 seconds after intravenous contrast administration. There was an association between degree of tumor enhancement and subjective lesion conspicuity.

CONCLUSION

Optimal MPM contrast enhancement occurs at a later phase than typically acquired with conventional thoracic CT imaging.

Application of low-concentration contrast agents and low-tube-voltage computed tomography to chest enhancement examinations: A multicenter prospective study

To evaluate the influence of low-concentration contrast agents and low-tube-voltage computed tomography on chest enhancement examinations, we conducted a multicenter prospective study. A total of 216 inpatients enrolled from 12 different hospitals were randomly divided into four groups: A: voltage, 120 kVp; iohexol, 350 mgI/mL; B: voltage, 100 kVp, iohexol, 350 mgI/mL; C: voltage, 120 kVp, iodixanol, 270 mgI/mL; and D: voltage, 100 kVp, iodixanol, 270 mgI/mL. Subjective image quality was assessed by two radiologists and compared by weighted kappa test. The objective image scores, scanning radiation doses, and pathological coincidence rates were analyzed. There were no significant differences in gender, age, height, weight, and body mass index between the four groups (p > 0.05). The consistency of the radiologists' ratings were good, with kappa value ranging from 0.736 (95% confidence interval: 0.54-0.933) to 0.809 (95% confidence interval: 0.65-0.968), and there was no difference in subjective image score between the four groups. The computed tomography value of group D had no difference with group A. The volume computed tomography dose index, dose length product, and effective dose of group D (6.93 ± 3.03, 241.55 ± 104.75, and 3.38 ± 1.47, respectively) were all significantly lower than those of group A (10.30 ± 4.37, 359.70 ± 152.65, and 5.04 ± 2.14, respectively). There was no significant difference in the imaging diagnosis accuracy rate between the four groups (p > 0.05). The results indicated that low-concentration contrast agents (270 mgI/mL) and low-tube-voltage (100 kVp) computed tomography can not only decrease radiation dose but also guarantee the image quality and meet the needs of imaging diagnosis in chest enhancement examinations, which make it possible for its generalization and application.

Photoacoustic imaging as a tool to probe the tumour microenvironment

The tumour microenvironment (TME) is a complex cellular ecosystem subjected to chemical and physical signals that play a role in shaping tumour heterogeneity, invasion and metastasis. Studying the roles of the TME in cancer progression would strongly benefit from non-invasive visualisation of the tumour as a whole organ in vivo, both preclinically in mouse models of the disease, as well as in patient tumours. Although imaging techniques exist that can probe different facets of the TME, they face several limitations, including limited spatial resolution, extended scan times and poor specificity from confounding signals. Photoacoustic imaging (PAI) is an emerging modality, currently in clinical trials, that has the potential to overcome these limitations. Here, we review the biological properties of the TME and potential of existing imaging methods that have been developed to analyse these properties non-invasively. We then introduce PAI and explore the preclinical and clinical evidence that support its use in probing multiple features of the TME simultaneously, including blood vessel architecture, blood oxygenation, acidity, extracellular matrix deposition, lipid concentration and immune cell infiltration. Finally, we highlight the future prospects and outstanding challenges in the application of PAI as a tool in cancer research and as part of a clinical oncologist's arsenal.

Summary: This Review details the potential of photoacoustic imaging to visualise features of the tumour microenvironment such as blood vessels, hypoxia, fibrosis and immune infiltrate to provide unprecedented insight into tumour biology.

Pulmonary MALT Lymphoma has variable features on CT

Pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma is the most common primary pulmonary lymphoma. There are limited studies on imaging features of pulmonary MALT lymphoma. We present the computed tomography (CT) manifestations of pulmonary MALT lymphoma and the correlation between CT manifestations and clinical characteristics. Patients (n = 53) with histologically confirmed pulmonary MALT lymphoma who underwent chest CT scanning were retrospectively analyzed. Evaluated findings included distribution of pulmonary lesions, morphological pattern of appearance, contrast enhancement features, size, presence of thoracic lymphadenopathy, and secondary associated features. Pulmonary MALT lymphoma was observed in multiple (79%) and bilateral (66%) disease with random distribution (≥70%) of pulmonary lesions. The most frequent morphological pattern was consolidation (n = 33, 62%), followed by nodule (n = 23, 43%) and mass (n = 11, 21%). Common associated features were air bronchograms and bronchiectasis, especially cystic bronchiectasis and angiogram sign. Asymptomatic patients had less consolidation and bronchiectasis than did symptomatic patients. Cystic bronchiectasis was only observed in the symptomatic group. In conclusion, pulmonary MALT lymphoma manifests as diverse patterns on CT scans. Consolidation combined with cystic bronchiectasis was a characteristic late sign, which may assist in differential diagnosis. High-resolution CT images and multiplanar reconstruction techniques are helpful for accurately determining imaging manifestations.

Diagnostic Performance of Perfusion Computed Tomography for Differentiating Lung Cancer from Benign Lesions: A Meta-Analysis

Background

Numerous studies have explored diagnosis of pulmonary nodules using perfusion computed tomography (CT); however, findings were not always consistent between studies. Th e present study aimed to summarize evidence on the diagnostic value of perfusion CT for distinguishing between lung cancer and benign lesions.

Material/Methods

We performed a systematic literature search on lung cancer and benign pulmonary lesions performed with perfusion CT. The searches were undertaken in English or Chinese language in Medline, PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Infrastructure database from Jan 2010 to Nov 2018. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) of blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS) were calculated using Review Manager 5.3. Publication bias, sensitivity, specificity, and the area under the curve (AUC) were calculated using Stata12.0.

Results

Fourteen studies comprising 1032 malignant and 447 benign pulmonary lesions were analyzed. Lung cancer had higher BV, BF, MTT, and PS values than benign lesions. SMDs and 95% CIs of BV, BF, MTT, and PS were 2.29 (1.43, 3.16), 0.50 (0.14, 0.86), 0.55 (0.39, 0.72), and 1.21 (0.87, 1.56), respectively. AUC values of BV and PS were 0.92 (0.90, 0.94) and 0.83 (0.80, 0.86), respectively.

Conclusions

CT perfusion imaging is a valuable technique for the diagnosis of pulmonary nodules. Lung cancer had higher perfusion and permeability than benign lesions. The evidence suggests blood volume is the best surrogate marker for characterizing the blood supply, while permeability surface has a high specificity in quantifying the vascular permeability.

Solitary pulmonary nodule: Comparison of quantitative capability for differentiation and management among dynamic CE-perfusion MRI at 3 T system, dynamic CE-perfusion ADCT and FDG-PET/CT

PURPOSE

To prospectively compare the capability of dynamic first-pass contrast-enhanced (CE) perfusion MR imaging with ultra-short TE and area-detector CT (ADCT), analyzed with the same mathematical methods, and that of FDG-PET/CT for diagnosis and management of solitary pulmonary nodules (SPNs).

METHODS AND MATERIALS

Our institutional review board approved this study and written informed consent was obtained from all subjects. A total 57 consecutive patients with 71 nodules prospectively underwent dynamic CE-perfusion ADCT and MR imaging with ultra-short TE, FDG-PET/CT, as well as microbacterial and/or pathological examinations. The nodules were classified into malignant nodules (n = 45) and benign nodules (n = 26). Pulmonary arterial, systemic arterial and total perfusions were determined by means of dual-input maximum slope models on ADCT and MR imaging and maximum values of standard uptake values (SUV_max) on PET/CT. Receiver operating characteristic (ROC) analysis was performed for each index, and sensitivity, specificity and accuracy were compared by McNemar's test.

RESULTS

Areas under the curve (Azs) of total perfusion on ADCT (Az = 0.89) and MR imaging (Az = 0.88) were significantly larger than those of systemic arterial perfusion and MR imaging (p<0.05). Accuracy of total perfusion on ADCT (87.3% [62/71]) and MR imaging (87.3% [62/71]) was significantly higher than that of systemic arterial perfusion for both methods (77.5% [55/71] p = 0.02) and SUV_max (78.9% [56/71], p = 0.03).

CONCLUSION

Dynamic CE-perfusion MR imaging with ultra-short TE and ADCT and have similar potential capabilities, and are superior to FDG-PET/CT in this setting.

Dynamic Contrast-enhanced Area-detector CT vs Dynamic Contrast-enhanced Perfusion MRI vs FDG-PET/CT: Comparison of Utility for Quantitative Therapeutic Outcome Prediction for NSCLC Patients Undergoing Chemoradiotherapy

PURPOSE

To directly compare the utility for therapeutic outcome prediction of dynamic first-pass contrast-enhanced (CE)-perfusion area-detector computed tomography (ADCT), MR imaging assessed with the same mathematical method and 2-[fluorine-18]-fluoro-2-deoxy-d-glucose-positron emission tomography combined with CT (PET/CT) for non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy.

MATERIALS AND METHODS

Forty-three consecutive stage IIIB NSCLC patients, consisting of 25 males (mean age ± standard deviation: 66.6 ± 8.7 years) and 18 females (66.4 ± 8.2 years) underwent PET/CT, dynamic CE-perfusion ADCT and MR imaging, chemoradiotherapy, and follow-up examination. In each patient, total, pulmonary arterial, and systemic arterial perfusions were calculated from both perfusion data and SUV_max on PET/CT, assessed for each targeted lesion, and averaged to determine final values. Receiver operating characteristics analyses were performed to compare the utility for distinguishing responders from non-responders using Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 criteria. Overall survival (OS) assessed with each index were compared between two groups by means of the Kaplan-Meier method followed by the log-rank test.

RESULTS

Area under the curve (Az) for total perfusion on ADCT was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Az of total perfusion on MR imaging was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Mean OS of responder and non-responder groups were significantly different for total and systemic arterial (P < 0.05) perfusion.

CONCLUSION

Dynamic first-pass CE-perfusion ADCT and MR imaging as well as PET/CT are useful for early prediction of treatment response by NSCLC patients treated with chemoradiotherapy.

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

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

Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules?

BACKGROUND

This study quantitatively assessed the efficacy of spectral computed tomography (CT) imaging parameters for differentiating the malignancy and benignity of solitary pulmonary nodules (SPNs) manifesting as ground glass nodules (GGNs) and solid nodules (SNs).

METHODS

The study included 114 patients with SPNs (61 GGNs, and 53 SNs) who underwent CT plain and enhanced scans in the arterial (a) and venous (v) phases using the spectral imaging mode. The spectral CT imaging parameters included: iodine concentrations (IC) of lesions in the arterial (ICLa) and venous (ICLv) phases; normalized IC (NICa/NICv, normalized to the IC in the aorta); the slope of the spectral Hounsfield unit (HU) curve (λHUa/λHUv); and monochromatic CT number (CT40keVa/v, CT70keVa/v) enhancement on 40 and 70 keV images. The two-sample Mann-Whitney U test was used to compare quantitative parameters between malignant and benign SPNs, SNs, and GGNs.

RESULTS

Pathology revealed 75 lung cancer cases, 3 metastatic nodules, 14 benign nodules, and 22 inflammatory nodules. Among the 53 SNs there were 37 malignant and 16 benign nodules. Among the 61 GGNs there were 41 malignant and 20 benign nodules. Overall, the CT40keVa, λHUa, CT40keVv, λHUv, and ICLv of benign SPNs were all greater than those of malignant SPNs (all P < 0.05). For GGNs, CT40keVa/v, CT70keVa/v, λHUa/λHUv, and ICLv of malignant GGNs were all lower than those of benign GGNs.

CONCLUSION

Spectral CT imaging is a more promising method for distinguishing malignant from benign nodules, especially in nodules manifesting as GGNs in contrast-enhanced scanning.

Pulmonary histoplasmoma: a disguised malady

Histoplasmosis is a mycosis caused by the dimorphic fungus, Histoplasma capsulatum, which is transmitted via dust and aerosols. Lung involvement is the most common, with a varied clinical presentation. Although it is not the only source of infection, H. capsulatum is frequently found in bat guano, which is the reason why it is highly prevalent among caving practitioners. The solitary histoplasmoma of the lung is an unusual and chronic manifestation of this entity, which mimics, or at least is frequently misconstrued, as a malignancy. Almost invariably, the diagnosis of this type of histoplasmosis presentation is achieved after lung biopsy. The authors present the case of a young woman who sought medical care because of chest pain. The diagnostic work-up revealed the presence of a pulmonary nodule. She was submitted to a thoracotomy and wedge pulmonary resection. The histologic analysis rendered the diagnosis of histoplasmoma. This report aims to call attention to this diagnosis as the differential diagnosis of a pulmonary nodule.

Early Contrast Enhancement: A novel magnetic resonance imaging biomarker of pleural malignancy

INTRODUCTION

Pleural Malignancy (PM) is often occult on subjective radiological assessment. We sought to define a novel, semi-objective Magnetic Resonance Imaging (MRI) biomarker of PM, targeted to increased tumour microvessel density (MVD) and applicable to minimal pleural thickening.

MATERIALS AND METHODS

60 consecutive patients with suspected PM underwent contrast-enhanced 3-T MRI then pleural biopsy. In 58/60, parietal pleura signal intensity (SI) was measured in multiple regions of interest (ROI) at multiple time-points, generating ROI SI/time curves and Mean SI gradient (MSIG: SI increment/time). The diagnostic performance of Early Contrast Enhancement (ECE; which was defined as a SI peak in at least one ROI at or before 4.5 min) was compared with subjective MRI and Computed Tomography (CT) morphology results. MSIG was correlated against tumour MVD (based on Factor VIII immunostain) in 31 patients with Mesothelioma.

RESULTS

71% (41/58) patients had PM. Pleural thickening was <10 mm in 49/58 (84%). ECE sensitivity was 83% (95% CI 61-94%), specificity 83% (95% CI 68-91%), positive predictive value 68% (95% CI 47-84%), negative predictive value 92% (78-97%). ECE performance was similar or superior to subjective CT and MRI. MSIG correlated with MVD (r = 0.4258, p = .02).

DISCUSSION

ECE is a semi-objective, perfusion-based biomarker of PM, measurable in minimal pleural thickening. Further studies are warranted.

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

Since the clinical introduction of magnetic resonance imaging (MRI), the chest has been one of its most challenging applications, and many physicists and radiologists have tried since the 1980s to use MR for assessment of different lung diseases as well as mediastinal and pleural diseases. Since then, however, technical advances in sequencing, scanners, and coils, adaptation of parallel imaging techniques, utilization of contrast media, and development of postprocessing tools have been reported by many basic and clinical researchers. As a result, state-of-the-art thoracic MRI is now substituted for traditional imaging techniques and/or plays a complementary role in the management of patients with various chest diseases, and especially in the detection of pulmonary nodules and in thoracic oncology. In addition, MRI has continued to be developed to help overcome the limitations of computed tomography (CT) and nuclear medicine examinations. It can currently provide not only morphological, but also functional, physiological, pathophysiological, and molecular information at 1.5T with a gradual shift from 1.5T to 3T MR systems. In this review, we focus on these recent advances in MRI for pulmonary nodule detection and pulmonary nodule and mass evaluation by using noncontrast-enhanced and contrast-enhanced techniques as well as new molecular imaging methods such as chemical exchange saturation transfer imaging for a comparison with other modalities such as single or multidetector row CT, 18F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET), and/or PET/CT.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1437-1458.

Free-breathing dynamic contrast-enhanced MRI for assessment of pulmonary lesions using golden-angle radial sparse parallel imaging

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been shown to be a promising technique for assessing lung lesions. However, DCE-MRI often suffers from motion artifacts and insufficient imaging speed. Therefore, highly accelerated free-breathing DCE-MRI is of clinical interest for lung exams.

PURPOSE

To test the performance of rapid free-breathing DCE-MRI for simultaneous qualitative and quantitative assessment of pulmonary lesions using Golden-angle RAdial Sparse Parallel (GRASP) imaging.

STUDY TYPE

Prospective.

POPULATION

Twenty-six patients (17 males, mean age = 55.1 ± 14.4) with known pulmonary lesions.

FIELD STRENGTH/SEQUENCE

3T MR scanner; a prototype fat-saturated, T₁ -weighted stack-of-stars golden-angle radial sequence for data acquisition and a Cartesian breath-hold volumetric-interpolated examination (BH-VIBE) sequence for comparison.

ASSESSMENT

After a dual-mode GRASP reconstruction, one with 3-second temporal resolution (3s-GRASP) and the other with 15-second temporal resolution (15s-GRASP), all GRASP and BH-VIBE images were pooled together for blind assessment by two experienced radiologists, who independently scored the overall image quality, lesion delineation, overall artifact level, and diagnostic confidence of each case. Perfusion analysis was performed for the 3s-GRASP images using a Tofts model to generate the volume transfer coefficient (K^trans ) and interstitial volume (V_e ).

STATISTICAL TESTS

Nonparametric paired two-tailed Wilcoxon signed-rank test; Cohen's kappa; unpaired Student's t-test.

RESULTS

15s-GRASP achieved comparable image quality with conventional BH-VIBE (P > 0.05), except for the higher overall artifact level in the precontrast phase (P = 0.018). The K^trans and V_e in inflammation were higher than those in malignant lesions (K^trans : 0.78 ± 0.52 min^-1 vs. 0.37 ± 0.22 min^-1 , P = 0.020; V_e : 0.36 ± 0.16 vs. 0.26 ± 0.1, P = 0.177). Also, the K^trans and V_e in malignant lesions were also higher than those in benign lesions (K^trans : 0.37 ± 0.22 min^-1 vs. 0.04 ± 0.04 min^-1 , P = 0.001; V_e : 0.26 ± 0.12 vs. 0.10 ± 0.00, P = 0.063).

DATA CONCLUSION

This feasibility study demonstrated the performance of high spatiotemporal resolution free-breathing DCE-MRI of the lung using GRASP for qualitative and quantitative assessment of pulmonary lesions.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:459-468.

Dynamic contrast-enhanced MRI versus 18F-FDG PET/CT: Which is better in differentiation between malignant and benign solitary pulmonary nodules?

Objective

To prospectively compare the discriminative capacity of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with that of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) in the differentiation of malignant and benign solitary pulmonary nodules (SPNs).

Methods

Forty-nine patients with SPNs were included in this prospective study. Thirty-two of the patients had malignant SPNs, while the other 17 had benign SPNs. All these patients underwent DCE-MRI and ¹⁸F-FDG PET/CT examinations. The quantitative MRI pharmacokinetic parameters, including the trans-endothelial transfer constant (K^trans), redistribution rate constant (K_ep), and fractional volume (V_e), were calculated using the Extended-Tofts Linear two-compartment model. The ¹⁸F-FDG PET/CT parameter, maximum standardized uptake value (SUV_max), was also measured. Spearman's correlations were calculated between the MRI pharmacokinetic parameters and the SUV_max of each SPN. These parameters were statistically compared between the malignant and benign nodules. Receiver operating characteristic (ROC) analyses were used to compare the diagnostic capability between the DCE-MRI and ¹⁸F-FDG PET/CT indexes.

Results

Positive correlations were found between K^trans and SUV_max, and between K_ep and SUV_max (P<0.05). There were significant differences between the malignant and benign nodules in terms of the K^trans, K_ep and SUV_max values (P<0.05). The areas under the ROC curve (AUC) of K^trans, K_ep and SUV_max between the malignant and benign nodules were 0.909, 0.838 and 0.759, respectively. The sensitivity and specificity in differentiating malignant from benign SPNs were 90.6% and 82.4% for K^trans; 87.5% and 76.5% for K_ep; and 75.0% and 70.6% for SUV_max, respectively. The sensitivity and specificity of K^trans and K_ep were higher than those of SUV_max, but there was no significant difference between them (P>0.05).

Conclusions

DCE-MRI can be used to differentiate between benign and malignant SPNs and has the advantage of being radiation free.

Rationale of Using Dynamic Imaging for Characterization of Suspicious Lung Masses into Benign or Malignant on Contrast Enhanced Multi Detector Computed Tomography

Objectives:

To assess the utility of dynamic imaging namely, wash-in and wash-out characteristics through multidetector contrast-enhanced computed tomography in differentiating benign and malignant pulmonary masses.

Materials and Methods:

Seventy-three patients who were suspected to have malignant pulmonary mass on the basis of clinical symptoms and chest radiograph were included in the study. All the patients underwent multidetector computed tomography scanning, and three series of images were obtained for each patient-noncontrast, early enhanced, and 15 min delayed enhanced scans. Computed tomography (CT) findings were assessed in terms of washin, absolute, and relative percentage washout of contrast. Biopsy of the mass was done and sent for histopathological evaluation. Sensitivity, specificity, and area under curve for diagnosing malignancy in the lung masses were calculated by considering both the wash-in and wash-out characteristics at dynamic CT and plotting the receiver operating curve after the final diagnosis which was obtained by histopathological evaluation.

Results:

Threshold net enhancement (washin) value of >22.5 HU had sensitivity, specificity, and diagnostic accuracy of 88.5%, 57.1%, and 82%, respectively, in predicting malignancy. Threshold relative percentage washout of <16.235% had 98.1%, 85.7%, and 94% sensitivity, specificity, and diagnostic accuracy, respectively, and threshold absolute percentage washout of <42.72% had 98.1%, 95.2%, and 95% sensitivity, specificity, and diagnostic accuracy, respectively, in predicting malignancy.

Conclusion:

Threshold net enhancement (washin), absolute and relative washout percentages can be used to predict malignancy with very high diagnostic yield, and possibly obviate the need of invasive procedures for diagnosis of bronchogenic carcinoma.

Contrast-enhanced CT- and MRI-based perfusion assessment for pulmonary diseases: basics and clinical applications

Assessment of regional pulmonary perfusion as well as nodule and tumor perfusions in various pulmonary diseases are currently performed by means of nuclear medicine studies requiring radioactive macroaggregates, dual-energy computed tomography (CT), and dynamic first-pass contrast-enhanced perfusion CT techniques and unenhanced and dynamic first-pass contrast enhanced perfusion magnetic resonance imaging (MRI), as well as time-resolved three-dimensional or four-dimensional contrast-enhanced magnetic resonance angiography (MRA). Perfusion scintigraphy, single-photon emission tomography (SPECT) and SPECT fused with CT have been established as clinically available scintigraphic methods; however, they are limited by perfusion information with poor spatial resolution and other shortcomings. Although positron emission tomography with 15O water can measure absolute pulmonary perfusion, it requires a cyclotron for generation of a tracer with an extremely short half-life (2 min), and can only be performed for academic purposes. Therefore, clinicians are concentrating their efforts on the application of CT-based and MRI-based quantitative and qualitative perfusion assessment to various pulmonary diseases. This review article covers 1) the basics of dual-energy CT and dynamic first-pass contrast-enhanced perfusion CT techniques, 2) the basics of time-resolved contrast-enhanced MRA and dynamic first-pass contrast-enhanced perfusion MRI, and 3) clinical applications of contrast-enhanced CT- and MRI-based perfusion assessment for patients with pulmonary nodule, lung cancer, and pulmonary vascular diseases. We believe that these new techniques can be useful in routine clinical practice for not only thoracic oncology patients, but also patients with different pulmonary vascular diseases.

Dual-energy Computed Tomography for the Evaluation of Enhancement of Pulmonary Nodules≤3 cm in Size

PURPOSE

The aim of the study was to compare the accuracies of 4 different methods of assessing pulmonary nodule enhancement to distinguish benign from malignant solid pulmonary nodules using nondynamic contrast-enhanced dual-energy computed tomography.

MATERIALS AND METHODS

Seventy-two patients (mean age, 62 y) underwent dual-energy chest computed tomography 3 minutes after intravenous contrast administration. Each of 118 pulmonary nodules (9±5.9 mm) were evaluated for enhancement by 4 methods: visual assessment, 3-dimensional automated postprocessing measurement tool, manually drawn region of interest with calculated iodine-related attenuation, and measurement of iodine concentration. The optimal cutoff for enhancement was defined as having the largest specificity among all cutoffs while maintaining 100% sensitivity. Accuracy of the methods was assessed with receiver operating characteristic curves.

RESULTS

Ninety-three of 118 pulmonary nodules were benign (79%). Visual assessment of enhancement had sensitivity and specificity of 100% and 44%, respectively. For the automated 3-dimensional measurement tool, 20 HU was found to be the optimal threshold for defining enhancement, resulting in a specificity of 71% and a sensitivity of 100%, as well as an area under the curve (AUC) of 0.87 (95% confidence interval [CI], 0.82-0.92). The AUC was 0.79 (95% CI, 0.73-0.85) for the measured enhancement using a manually drawn region of interest. When a threshold of 21 HU was used for defining enhancement, maximum specificity was obtained (56%) while maintaining 100% sensitivity. The AUC for measured iodine concentration was 0.79 (95% CI, 0.77-0.85). At a cutoff iodine concentration of 0.6 mg/mL, the sensitivity was 100% with a specificity of 57%.

CONCLUSIONS

Although use of automated postprocessing had the highest specificity while maintaining 100% sensitivity, there were only minor clinically relevant differences between measurement techniques given that no single technique misclassified a malignant nodule as nonenhancing.

Dynamic contrast-enhanced perfusion area-detector CT assessed with various mathematical models: Its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT

PURPOSE

To directly compare the capability of dynamic first-pass contrast-enhanced (CE-) perfusion area-detector CT (ADCT) and PET/CT for early prediction of treatment response, disease progression and overall survival of non-small cell carcinoma (NSCLC) patients treated with chemoradiotherapy.

MATERIALS AND METHODS

Fifty-three consecutive Stage IIIB NSCLC patients who had undergone PET/CT, dynamic first-pass CE-perfusion ADCT, chemoradiotherapy, and follow-up examination were enrolled in this study. They were divided into two groups: 1) complete or partial response (CR+PR) and 2) stable or progressive disease (SD+PD). Pulmonary arterial and systemic arterial perfusions and total perfusion were assessed at targeted lesions with the dual-input maximum slope method, permeability surface and distribution volume with the Patlak plot method, tumor perfusion with the single-input maximum slope method, and SUV_max, and results were averaged to determine final values for each patient. Next, step-wise regression analysis was used to determine which indices were the most useful for predicting therapeutic effect. Finally, overall survival of responders and non-responders assessed by using the indices that had a significant effect on prediction of therapeutic outcome was statistically compared.

RESULTS

The step-wise regression test showed that therapeutic effect (r²=0.63, p=0.01) was significantly affected by the following three factors in order of magnitude of impact: systemic arterial perfusion, total perfusion, and SUV_max. Mean overall survival showed a significant difference for total perfusion (p=0.003) and systemic arterial perfusion (p=0.04).

CONCLUSION

Dynamic first-pass CE-perfusion ADCT as well as PET/CT are useful for treatment response prediction in NSCLC patients treated with chemoradiotherapy.

Improving Image Quality for Lung Cancer Imaging With Optimal Monochromatic Energy Level in Dual Energy Spectral Computed Tomography

OBJECTIVE

The aim of this study was to find optimal monochromatic spectral computed tomography (CT) level to improve image quality of lung cancer.

METHODS

Fifty patients with lung cancers were scanned by spectral CT; monochromatic images at 50, 60, 70 and 80 keV energy levels were generated; and objective analysis including image noise, lesion-to-lung contrast-to-noise ratio, and CT number difference between central and peripheral regions of tumor (dCT value) were measured and compared. Subjective assessment about the overall image quality and inhomogeneity enhancement was compared.

RESULTS

The highest contrast-to-noise ratio value and subjective score of image quality were obtained at 70 keV, which were superior to those of 50- and 80-keV series (all P < 0.05). The subjective score of the inhomogeneity evaluation was peaked at 60-keV series and significantly higher than other energy levels (all P < 0.05).

CONCLUSIONS

Both objective and subjective image analysis of lung cancers may be improved with the combined observation of 60 keV and 70 keV monochromatic images in spectral CT.

Microflow imaging of contrast-enhanced ultrasound for evaluation of neovascularization in peripheral lung cancer

The aim of this study was to investigate the role of microflow imaging (MFI) of contrast-enhanced ultrasound (CEUS) for evaluating microvascular architecture of different types of peripheral lung cancer (PLC) and to explore the correlated pathological basis.Ninety-five patients with PLC were enrolled in this study. Two radiologists independently evaluated the microvascular architecture of PLC with MFI. The interobserver agreement was measured with Kappa test. The diagnosis value of MFI was calculated. With pathological analysis, the correlation between MFI and microvascular density (MVD)/microvascular diameter (MD) was evaluated.Of the 95 PLCs, MFI were mainly classified "dead wood" (27.4%, 25.3%), "vascular" (47.4%, 49.5%), and "cotton" (20.0%, 20.0%) patterns by the 2 readers. Kappa test showed a good agreement between the 2 readers (Kappa = 0.758). The "dead wood" can be regarded as a specific diagnostic factor for squamous carcinoma; the sensitivity, specificity, and accuracy was 62.9%, 93.3%, and 82.1%, respectively. The "vascular" and "cotton" patterns correlated well with adenocarcinoma and SCLC (small cell lung cancer); diagnostic sensitivity, specificity, and accuracy were 86.7%, 65.7%, and 78.9%, respectively. MVD of "dead wood" was lower than "vascular" and "cotton," while MD was bigger than the other 2 patterns (P < 0.05). There was a good correlation between MFI and histopathological types of PLC as well as between MFI and MVD/MD (P < 0.05).MFI has the advantage to display the microvascular architecture of PLCs and might become a promising diagnostic method of histopathological types of PLC. MFI features also correlated well with its pathological basis, including MVD and MD.

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer

Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.

Advanced imaging tools in pulmonary nodule detection and surveillance

Lung cancer is a leading cause of death worldwide. The National Lung Screening Trial has demonstrated that lung cancer screening can reduce lung cancer specific and all cause mortality. With approval of national coverage for lung cancer screening, it is expected that an increase in exams related to pulmonary nodule detection and surveillance will ensue. Advanced imaging technologies for nodule detection and surveillance will be more important than ever. While computed tomography (CT) remains the modality of choice, other emerging modalities such as magnetic resonance imaging provides viable alternatives to CT.

Improvement in imaging diagnosis technique and modalities for solitary pulmonary nodules: from ground-glass opacity nodules to part-solid and solid nodules

With advances in CT technology and the popularity of low-dose CT as a device for lung cancer screening, the detection rate of sub-solid pulmonary nodules as well as solid nodules has been increased. Distinguishing solid from sub-solid features is an essential step in the CT evaluation of solitary pulmonary nodules (SPNs) because strategies for nodule characterization and guidelines for management are different for each category. In addition to conventional CT parameters, numerous novel concepts and modalities have been developed. Although there is currently no single effective method for differentiating malignant from benign nodules, growth rate measurement using volumetry, evaluation of tumor vascularity on dynamic helical CT, dual-energy CT and MRI and physiologic evaluation with PET/CT can all be useful for nodule characterization. New techniques such as tomosynthesis can improve detection over radiography alone. The purpose of this article is to enhance our understanding of the evidence-based strategies involved in diagnosing SPNs.

Dynamic contrast-enhanced perfusion area detector CT for non-small cell lung cancer patients: Influence of mathematical models on early prediction capabilities for treatment response and recurrence after chemoradiotherapy

PURPOSE

To determine the capability and influence of the mathematical method on dynamic contrast-enhanced (CE-) perfusion area detector CT (ADCT) for early prediction of treatment response as well as progression free and overall survival (PFS and OS) of non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy.

MATERIALS AND METHODS

Sixty-six consecutive stage III NSCLC patients underwent dynamic CE-perfusion ADCT examinations, chemoradiotherapy and follow-up examinations. Response Evaluation Criteria in Solid Tumors (RECIST) criteria were used to divide all patients into responders and non-responders. Differences in each of the indices for all targeted lesions between measurements obtained 2 weeks prior to the first and the third course of chemotherapy were determined for all patients. ROC analyses were employed to determine the capability of perfusion indices as markers for distinguishing RECIST responders from non-responders. To evaluate their capability for early prediction of therapeutic effect, OS of perfusion index-based responders and non-responders were compared by using the Kaplan-Meier method followed by log-rank test.

RESULTS

Area under the curve (Az) for total perfusion by means of the dual-input maximum slope method was significantly larger than that of pulmonary arterial perfusion using the same method (p=0.007) and of perfusion with the single-input maximum slope method (p=0.007). Mean OS demonstrated significantly difference between responder- and non-responder groups for total perfusion (p=0.02).

CONCLUSION

Mathematical models have significant influence on assessment for early prediction of treatment response, disease progression and overall survival using dynamic CE-perfusion ADCT for NSCLC patients treated with chemoradiotherapy.