Artificial intelligence-based differential diagnosis of orbital MALT lymphoma and IgG4 related ophthalmic disease using hematoxylin-eosin images.

PURPOSE: To investigate the possibility of distinguishing between IgG4-related ophthalmic disease (IgG4-ROD) and orbital MALT lymphoma using artificial intelligence (AI) and hematoxylin-eosin (HE) images. METHODS: After identifying a total of 127 patients from whom we were able to procure tissue blocks with IgG4-ROD and orbital MALT lymphoma, we performed histological and molecular genetic analyses, such as gene rearrangement. Subsequently, pathological HE images were collected from these patients followed by the cutting out of 10 different image patches from the HE image of each patient. A total of 970 image patches from the 97 patients were used to construct nine different models of deep learning, and the 300 image patches from the remaining 30 patients were used to evaluate the diagnostic performance of the models. Area under the curve (AUC) and accuracy (ACC) were used for the performance evaluation of the deep learning models. In addition, four ophthalmologists performed the binary classification between IgG4-ROD and orbital MALT lymphoma. RESULTS: EVA, which is a vision-centric foundation model to explore the limits of visual representation, was the best deep learning model among the nine models. The results of EVA were ACC = 73.3% and AUC = 0.807. The ACC of the four ophthalmologists ranged from 40 to 60%. CONCLUSIONS: It was possible to construct an AI software based on deep learning that was able to distinguish between IgG4-ROD and orbital MALT. This AI model may be useful as an initial screening tool to direct further ancillary investigations.

Nodal infiltration in endometrial cancer: a prediction model using best subset regression.

OBJECTIVES: To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors. METHODS: In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available. RESULTS: The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient ß values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively. CONCLUSIONS: The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies. CLINICAL RELEVANCE STATEMENT: The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment. KEY POINTS: •Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT.

OBJECTIVES: To develop and evaluate a deep learning-based algorithm (DLA) for automatic detection of bone metastases on CT. METHODS: This retrospective study included CT scans acquired at a single institution between 2009 and 2019. Positive scans with bone metastases and negative scans without bone metastasis were collected to train the DLA. Another 50 positive and 50 negative scans were collected separately from the training dataset and were divided into validation and test datasets at a 2:3 ratio. The clinical efficacy of the DLA was evaluated in an observer study with board-certified radiologists. Jackknife alternative free-response receiver operating characteristic analysis was used to evaluate observer performance. RESULTS: A total of 269 positive scans including 1375 bone metastases and 463 negative scans were collected for the training dataset. The number of lesions identified in the validation and test datasets was 49 and 75, respectively. The DLA achieved a sensitivity of 89.8% (44 of 49) with 0.775 false positives per case for the validation dataset and 82.7% (62 of 75) with 0.617 false positives per case for the test dataset. With the DLA, the overall performance of nine radiologists with reference to the weighted alternative free-response receiver operating characteristic figure of merit improved from 0.746 to 0.899 (p < .001). Furthermore, the mean interpretation time per case decreased from 168 to 85 s (p = .004). CONCLUSION: With the aid of the algorithm, the overall performance of radiologists in bone metastases detection improved, and the interpretation time decreased at the same time. KEY POINTS: • A deep learning-based algorithm for automatic detection of bone metastases on CT was developed. • In the observer study, overall performance of radiologists in bone metastases detection improved significantly with the aid of the algorithm. • Radiologists' interpretation time decreased at the same time.

Deep learning model for predicting gestational age after the first trimester using fetal MRI.

OBJECTIVES: To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD). MATERIALS AND METHODS: Our Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin's concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride's definition. RESULTS: The ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model's prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks). CONCLUSIONS: Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester. KEY POINTS: • The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.

Editorial for "A Multiparametric MRI-based Radiomics Nomogram for Predicting Lymphovascular Space Invasion in Endometrial Carcinoma".

LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 4 J. Magn. Reson. Imaging 2020;52:1263-1264.

Adaptive Voxel Matching for Temporal CT Subtraction.

Temporal subtraction (TS) technique calculates a subtraction image between a pair of registered images acquired from the same patient at different times. Previous studies have shown that TS is effective for visualizing pathological changes over time; therefore, TS should be a useful tool for radiologists. However, artifacts caused by partial volume effects degrade the quality of thick-slice subtraction images, even with accurate image registration. Here, we propose a subtraction method for reducing artifacts in thick-slice images and discuss its implementation in high-speed processing. The proposed method is based on voxel matching, which reduces artifacts by considering gaps in discretized positions of two images in subtraction calculations. There are two different features between the proposed method and conventional voxel matching: (1) the size of a searching region to reduce artifacts is determined based on discretized position gaps between images and (2) the searching region is set on both images for symmetrical subtraction. The proposed method is implemented by adopting an accelerated subtraction calculation method that exploit the nature of liner interpolation for calculating the signal value at a point among discretized positions. We quantitatively evaluated the proposed method using synthetic data and qualitatively using clinical data interpreted by radiologists. The evaluation showed that the proposed method was superior to conventional methods. Moreover, the processing speed using the proposed method was almost unchanged from that of the conventional methods. The results indicate that the proposed method can improve the quality of subtraction images acquired from thick-slice images.

CT temporal subtraction improves early detection of bone metastases compared to SPECT.

OBJECTIVES: To compare observer performance of detecting bone metastases between bone scintigraphy, including planar scan and single-photon emission computed tomography, and computed tomography (CT) temporal subtraction (TS). METHODS: Data on 60 patients with cancer who had undergone CT (previous and current) and bone scintigraphy were collected. Previous CT images were registered to the current ones by large deformation diffeomorphic metric mapping; the registered previous images were subtracted from the current ones to produce TS. Definitive diagnosis of bone metastases was determined by consensus between two radiologists. Twelve readers independently interpreted the following pairs of examinations: NM-pair, previous and current CTs and bone scintigraphy, and TS-pair, previous and current CTs and TS. The readers assigned likelihood levels to suspected bone metastases for diagnosis. Sensitivity, number of false positives per patient (FPP), and reading time for each pair of examinations were analysed for evaluating observer performance by performing the Wilcoxon signed-rank test. Figure-of-merit (FOM) was calculated using jackknife alternative free-response receiver operating characteristic analysis. RESULTS: The sensitivity of TS was significantly higher than that of bone scintigraphy (54.3% vs. 41.3%, p = 0.006). FPP with TS was significantly higher than that with bone scintigraphy (0.189 vs. 0.0722, p = 0.003). FOM of TS tended to be better than that of bone scintigraphy (0.742 vs. 0.691, p = 0.070). CONCLUSION: Sensitivity of TS in detecting bone metastasis was significantly higher than that of bone scintigraphy, but still limited to 54%. TS might be superior to bone scintigraphy for early detection of bone metastasis. KEY POINTS: • Computed tomography temporal subtraction was helpful in early detection of bone metastases. • Sensitivity for bone metastasis was higher for computed tomography temporal subtraction than for bone scintigraphy. • Figure-of-merit of computed tomography temporal subtraction was better than that of bone scintigraphy.

Temporal subtraction of computed tomography images improves detectability of bone metastases by radiology residents.

OBJECTIVE: Temporal subtraction of CT (TS) images improves detection of newly developed bone metastases (BM). We sought to determine whether TS improves detection of BM by radiology residents as well. METHODS: We performed an observer study using a previously reported dataset, consisting of 60 oncology patients, each with previous and current CT images. TS images were calculated using in-house software. Four residents independently interpreted twice the 60 sets of CT images, without and with TS. They identified BM by marking suspicious lesions likely to be BM. Lesion-based sensitivity and number of false positives per patient were calculated. Figure-of-merit (FOM) was calculated. Detectability of BM, with and without TS, was compared between radiology residents and board-certified radiologists, as published previously. RESULTS: FOM of residents significantly improved by implementing TS (p value < 0.0001). Lesion-based sensitivity, false positives per patients, and FOM were 40.8%, 0.121, and 0.657, respectively, without TS, and 58.1%, 0.0958, and 0.796, respectively, with TS. These findings were comparable with the previously published values for board-certified radiologists without TS (58.0%, 0.19, and 0.758, respectively). CONCLUSION: The detectability of BM by residents improved markedly by implementing TS and reached that of board-certified radiologists without TS. KEY POINTS: • Detectability of bone metastases on CT by residents improved significantly when using temporal subtraction of CT (TS). • Detections by residents with TS and board-certified radiologists without TS were comparable. • TS is useful for residents as it is for board-certified radiologists.

Detection of suspected brain infarctions on CT can be significantly improved with temporal subtraction images.

OBJECTIVE: To assess whether temporal subtraction (TS) images of brain CT improve the detection of suspected brain infarctions. METHODS: Study protocols were approved by our institutional review board, and informed consent was waived because of the retrospective nature of this study. Forty-two sets of brain CT images of 41 patients, each consisting of a pair of brain CT images scanned at two time points (previous and current) between January 2011 and November 2016, were collected for an observer performance study. The 42 sets consisted of 23 cases with a total of 77 newly developed brain infarcts or hyperdense artery signs confirmed by two radiologists who referred to additional clinical information and 19 negative control cases. To create TS images, the previous images were registered to the current images by partly using a non-rigid registration algorithm and then subtracted. Fourteen radiologists independently interpreted the images to identify the lesions with and without TS images with an interval of over 4 weeks. A figure of merit (FOM) was calculated along with the jackknife alternative free-response receiver-operating characteristic analysis. Sensitivity, number of false positives per case (FPC) and reading time were analyzed by the Wilcoxon signed-rank test. RESULTS: The mean FOM increased from 0.528 to 0.737 with TS images (p < 0.0001). The mean sensitivity and FPC improved from 26.5% and 0.243 to 56.0% and 0.153 (p < 0.0001 and p = 0.239), respectively. The mean reading time was 173 s without TS and 170 s with TS (p = 0.925). CONCLUSION: The detectability of suspected brain infarctions was significantly improved with TS CT images. KEY POINTS: • Although it is established that MRI is superior to CT in the detection of strokes, the first choice of modality for suspected stroke patients is often CT. • An observer performance study with 14 radiologists was performed to evaluate whether temporal subtraction images derived from a non-rigid transformation algorithm can significantly improve the detectability of newly developed brain infarcts on CT. • Temporal subtraction images were shown to significantly improve the detectability of newly developed brain infarcts on CT.

Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection.

OBJECTIVE: The purpose of this study was to evaluate agreement between standard-dose CT (SDCT) and ultralow-dose CT (ULDCT) findings with respect to emphysema quantification. ULDCT images were reconstructed with and without iterative reconstruction (IR). Adaptive iterative dose reduction with 3D processing was used for IR. MATERIALS AND METHODS: Fifty patients who underwent SDCT and ULDCT were included. The tube current for SDCT was 250 mA, and that for ULDCT was 10 mA. SDCT, ULDCT without IR, and ULDCT with IR were used for emphysema quantification. The low-attenuation volume percentage (LAV%) in the lungs at four thresholds (-970, -950, -930, and -910 HU), mean lung attenuation, and total lung volume were computed. Concordance correlation coefficients (CCC) were used to assess the agreement of emphysema quantification between SDCT and ULDCT. RESULTS: The LAV% CCC values were 0.310-0.789 between SDCT and ULDCT without IR and 0.934-0.966 between SDCT and ULDCT with IR. The agreement of LAV% improved when IR was used for ULDCT. The mean lung attenuation CCC value between SDCT and ULDCT without IR was substantial (0.957), whereas that between SDCT and ULDCT with IR was poor (0.890). The total lung volume CCC values were substantial (0.982 with IR, 0.983 without IR). CONCLUSION: ULDCT with and without IR can substitute for SDCT in emphysema quantification.

Solitary pulmonary nodules: Comparison of dynamic first-pass contrast-enhanced perfusion area-detector CT, dynamic first-pass contrast-enhanced MR imaging, and FDG PET/CT.

PURPOSE: To prospectively compare the capabilities of dynamic perfusion area-detector computed tomography (CT), dynamic magnetic resonance (MR) imaging, and positron emission tomography (PET) combined with CT (PET/CT) with use of fluorine 18 fluorodeoxyglucose (FDG) for the diagnosis of solitary pulmonary nodules. MATERIALS AND METHODS: The institutional review board approved this study, and written informed consent was obtained from each subject. A total of 198 consecutive patients with 218 nodules prospectively underwent dynamic perfusion area-detector CT, dynamic MR imaging, FDG PET/CT, and microbacterial and/or pathologic examinations. Nodules were classified into three groups: malignant nodules (n = 133) and benign nodules with low (n = 53) or high (n = 32) biologic activity. Total perfusion was determined with dual-input maximum slope models at area-detector CT, maximum and slope of enhancement ratio at MR imaging, and maximum standardized uptake value (SUVmax) at PET/CT. Next, all indexes for malignant and benign nodules were compared with the Tukey honest significant difference test. Then, receiver operating characteristic analysis was performed for each index. Finally, sensitivity, specificity, and accuracy were compared with the McNemar test. RESULTS: All indexes showed significant differences between malignant nodules and benign nodules with low biologic activity (P < .0001). The area under the receiver operating characteristic curve for total perfusion was significantly larger than that for other indexes (.0006 ≤ P ≤ .04). The specificity and accuracy of total perfusion were significantly higher than those of maximum relative enhancement ratio (specificity, P < .0001; accuracy, P < .0001), slope of enhancement ratio (specificity, P < .0001; accuracy, P < .0001), and SUVmax (specificity, P < .0001; accuracy, P < .0001). CONCLUSION: Dynamic perfusion area-detector CT is more specific and accurate than dynamic MR imaging and FDG PET/CT in the diagnosis of solitary pulmonary nodules in routine clinical practice.

Asthma: comparison of dynamic oxygen-enhanced MR imaging and quantitative thin-section CT for evaluation of clinical treatment.

PURPOSE: To compare the use of dynamic oxygen-enhanced magnetic resonance (MR) imaging with the use of quantitatively assessed computed tomography (CT) for assessment of clinical stage and evaluation of pulmonary functional change due to treatment in patients with asthma. MATERIALS AND METHODS: The institutional review board of Kobe University Hospital approved this study, and written informed consent was obtained from each subject. Thirty consecutive patients with asthma (17 men and 13 women; age range, 27-78 years) underwent dynamic oxygen-enhanced MR imaging, multidetector CT, and assessment of forced expiratory volume in 1 second. All patients were classified as having one of four stages of asthma according to the guidelines of the National Asthma Education and Prevention Program. Relative enhancement ratio ( RER relative enhancement ratio ) and wash-in time maps were generated by means of pixel-by-pixel analyses. Regions of interest were placed on images of the lung in all sections, and all measurements were averaged to determine mean RER relative enhancement ratio and mean wash-in time for each subject. Percentage of airway wall area and mean lung density were determined at quantitative CT. For comparison of the modalities for assessment of clinical stage, indexes of subjects at all clinical stages were compared by means of the Tukey honestly significant difference test. Evaluation of pulmonary functional improvement was assessed by correlating improvement of each index with that of forced expiratory volume. RESULTS: Mean wash-in time was significantly different among patients with asthma of different clinical stages (P < .05), but significant differences between mean RER relative enhancement ratio and percentage of airway wall area were observed for a limited number of clinical stages (P < .05). Improvement of mean RER relative enhancement ratio (r = 0.63, P = .0002) and mean wash-in time (r = -0.75, P < .0001) was significantly correlated with forced expiratory volume. CONCLUSION: Dynamic oxygen-enhanced MR imaging has potential as a tool for assessment of clinical stage and evaluation of pulmonary functional change due to treatment in patients with asthma.

Pulmonary 3 T MRI with ultrashort TEs: influence of ultrashort echo time interval on pulmonary functional and clinical stage assessments of smokers.

PURPOSE: To assess the influence of ultrashort TE (UTE) intervals on pulmonary magnetic resonance imaging (MRI) with UTEs (UTE-MRI) for pulmonary functional loss assessment and clinical stage classification of smokers. MATERIALS AND METHODS: A total 60 consecutive smokers (43 men and 17 women; mean age 70 years) with and without COPD underwent thin-section multidetector row computed tomography (MDCT), UTE-MRI, and pulmonary functional measurements. For each smoker, UTE-MRI was performed with three different UTE intervals (UTE-MRI A: 0.5 msec, UTE-MRI B: 1.0 msec, UTE-MRI C: 1.5 msec). By using the GOLD guidelines, the subjects were classified as: "smokers without COPD," "mild COPD," "moderate COPD," and "severe or very severe COPD." Then the mean T2* value from each UTE-MRI and CT-based functional lung volume (FLV) were correlated with pulmonary function test. Finally, Fisher's PLSD test was used to evaluate differences in each index among the four clinical stages. RESULTS: Each index correlated significantly with pulmonary function test results (P < 0.05). CT-based FLV and mean T2* values obtained from UTE-MRI A and B showed significant differences among all groups except between "smokers without COPD" and "mild COPD" groups (P < 0.05). CONCLUSION: UTE-MRI has a potential for management of smokers and the UTE interval is suggested as an important parameter in this setting.

Iterative reconstruction technique vs filter back projection: utility for quantitative bronchial assessment on low-dose thin-section MDCT in patients with/without chronic obstructive pulmonary disease.

OBJECTIVES: The aim of this study was to evaluate the utility of the iterative reconstruction (IR) technique for quantitative bronchial assessment during low-dose computed tomography (CT) as a substitute for standard-dose CT in patients with/without chronic obstructive pulmonary disease. METHODS: Fifty patients (mean age, 69.2; mean % predicted FEV1, 79.4) underwent standard-dose CT (150mAs) and low-dose CT (25mAs). Except for tube current, the imaging parameters were identical for both protocols. Standard-dose CT was reconstructed using filtered back-projection (FBP), and low-dose CT was reconstructed using IR and FBP. For quantitative bronchial assessment, the wall area percentage (WA%) of the sub-segmental bronchi and the airway luminal volume percentage (LV%) from the main bronchus to the peripheral bronchi were acquired in each dataset. The correlation and agreement of WA% and LV% between standard-dose CT and both low-dose CTs were statistically evaluated. RESULTS: WA% and LV% between standard-dose CT and both low-dose CTs were significant correlated (r > 0.77, p < 0.00001); however, only the LV% agreement between SD-CT and low-dose CT reconstructed with IR was moderate (concordance correlation coefficient = 0.93); the other agreement was poor (concordance correlation coefficient <0.90). CONCLUSIONS: Quantitative bronchial assessment via low-dose CT has potential as a substitute for standard-dose CT by using IR and airway luminal volumetry techniques. KEY POINTS: • Quantitative bronchial assessment of COPD using low-dose CT is possible. • Airway luminal volumetry with iterative reconstruction is insusceptible to dose reduction. • Filtered back-projection is susceptible to the effect of dose reduction. • Wall area percentage assessment is easily influenced by dose reduction.

Emphysema quantification by combining percentage and size distribution of low-attenuation lung regions.

OBJECTIVE: The purpose of this study was to investigate efficacy of two types of emphysema quantification: percentage of low-attenuation lung regions (%LA); and size distribution of these regions. On a log-log plot, cumulative frequency-size distribution of low-attenuation lung regions can be fitted by a straight line whose slope (D) has been reported to reflect diffusing capacity. In this study, %LA and D were compared with pulmonary function test (PFT) parameters, especially with ratio of diffusing capacity of carbon monoxide to effective alveolar ventilation (i.e., DLCO/VA). MATERIALS AND METHODS: Thin-section unenhanced CT images were acquired from 30 patients (25 men, five women; mean [SD] age, 70.1 ± 12.1 years), of whom 25 had received diagnosis of COPD, and %LA and D were calculated at 20 thresholds, ranging from -995 to -900 HU. To determine utility of %LA and D, we used Pearson's correlation for emphysema quantification and PFT. Significance of the coefficients was determined with Bonferroni correction (p < 0.0025). Finally, the relationships between emphysema quantification and DLCO/VA were examined by linear models and Akaike information criterion (AIC). RESULTS: The correlation coefficients for %LA and DLCO/VA were statistically significant at all the thresholds (optimal coefficient, -0.761). The correlation coefficients for D and DLCO/VA were statistically significant at the thresholds from -945 to -900 HU (optimal coefficient, -0.646). AIC values showed that the most accurate prediction of DLCO/VA was obtained by the model incorporating both %LA and D. CONCLUSION: Both %LA and D showed significant correlation with DLCO/VA. Combining %LA and D resulted in more accurate evaluation of DLCO/VA than did using %LA or D alone.

Journal Club: Comparison of assessment of preoperative pulmonary vasculature in patients with non-small cell lung cancer by non-contrast- and 4D contrast-enhanced 3-T MR angiography and contrast-enhanced 64-MDCT.

OBJECTIVE: The purpose of this article is to prospectively and directly compare the capabilities of non-contrast-enhanced MR angiography (MRA), 4D contrast-enhanced MRA, and contrast-enhanced MDCT for assessing pulmonary vasculature in patients with non-small cell lung cancer (NSCLC) before surgical treatment. SUBJECTS AND METHODS: A total of 77 consecutive patients (41 men and 36 women; mean age, 71 years) with pathologically proven and clinically assessed stage I NSCLC underwent thin-section contrast-enhanced MDCT, non-contrast-enhanced and contrast-enhanced MRA, and surgical treatment. The capability for anomaly assessment of the three methods was independently evaluated by two reviewers using a 5-point visual scoring system, and final assessment for each patient was made by consensus of the two readers. Interobserver agreement for pulmonary arterial and venous assessment was evaluated with the kappa statistic. Then, sensitivity, specificity, and accuracy for the detection of anomalies were directly compared among the three methods by use of the McNemar test. RESULTS: Interobserver agreement for pulmonary artery and vein assessment was substantial or almost perfect (κ=0.72-0.86). For pulmonary arterial and venous variation assessment, there were no significant differences in sensitivity, specificity, and accuracy among non-contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 50%; specificity, 98.5%; accuracy, 93.2%), 4D contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 62.5%; specificity, 100.0%; accuracy, 95.9%), and thin-section contrast-enhanced MDCT (pulmonary arteries: sensitivity, 91.4%; specificity, 89.5%; accuracy, 90.4%; pulmonary veins: sensitivity, 50%; specificity, 100.0%; accuracy, 95.9%) (p>0.05). CONCLUSION: Pulmonary vascular assessment of patients with NSCLC before surgical resection by non-contrast-enhanced MRA can be considered equivalent to that by 4D contrast-enhanced MRA and contrast-enhanced MDCT.

Dynamic contrast-enhanced CT and MRI for pulmonary nodule assessment.

OBJECTIVE: The purpose of this article is to review advanced imaging of pulmonary nodules, including pathologic and pharmacokinetic background, conventional contrast-enhanced CT and MRI assessment, dynamic contrast-enhanced CT and MRI techniques, and dual-source and area-detector CT systems for pulmonary nodule evaluation. CONCLUSION: Clinicians need to understand the underlying principles and pathologic and pharmacokinetic backgrounds of contrast-enhanced CT and MRI to further improve diagnostic performance. With adjustments in image acquisition and postprocessing, contrast-enhanced CT and MRI, especially the dynamic versions, can have enhanced clinical application for pulmonary nodules and expanded clinical relevance for other thoracic diseases.

Predicting the O'Kelly-Marotta scale score after flow-diverter stent placement using silent MRA.

PURPOSE: Flow-diverter (FD) stents were developed to treat aneurysms that are difficult to treat with conventional coiling or surgery. This study aimed to compare usefulness of Silent MRA and TOF (time of flight) -MRA in patients with aneurysms after FD placement. MATERIALS AND METHODS: We retrospectively collected images from 22 patients with 23 internal carotid artery aneurysms treated with FD. Two radiologists conducted MRA and DSA experiments. In the first reading experiment, the radiologists evaluated the aneurysm filling by employing Silent MRA and TOF-MRA and utilizing the modified O'Kelly-Marotta (OKM) scale, a four-class classification system for aneurysms after FD placement. We then calculated the agreement between the modified OKM scale on MRA and the original OKM scale on DSA. In the second reading experiment, the radiologists rated blood flow within the FD using a five-point scale. RESULTS: The weighted kappa value of the OKM scale between DSA and TOF-MRA was 0.436 (moderate agreement), and that between DSA and Silent MRA was 0.943 (almost perfect agreement). The accuracies for the four-class classification were 0.435 and 0.870 for TOF-MRA and Silent MRA, respectively. The mean score of blood flow within FD for TOF-MRA was 2.43 ± 0.90 and that for Silent MRA was 3.04 ± 1.02 (P < 0.001). CONCLUSION: Silent MRA showed a higher degree of agreement than TOF-MRA in aneurysm filling with DSA. In addition, Silent MRA was significantly superior to TOF-MRA in depicting blood flow within the FD. Therefore, Silent MRA is clinically useful for the follow-up of patients after FD placement.