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Purpose: This study aimed to investigate whether the quantitative parameters of dual-energy computed tomography (DECT) can predict the effects of chemotherapy in advanced adenocarcinoma based on the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines.Materials and Methods: A total of 90 patients (59 males, 31 females, age 61.4 ± 12.3 (23-85)) with unresectable lung adenocarcinoma (TNM stage IIIB or IV) who underwent DECT before chemotherapy were prospectively included in this study. By comparing baseline studies with the best response achieved during 1 st line chemotherapy, patients were divided into two groups according to RECIST (version 1.1) guidelines as follows; responders (CR or PR) and non-responders (SD or PD). Quantitative measurements were performed on baseline DECT, and a logistic regression model was used to evaluate predictive factors for a response to chemotherapy.Results: Among 90 patients, 38 were categorized as responders, while 52 patients were non-responders. The mean iodine concentration measurements were significantly higher in responders compared with non-responders (1.81 ± 0.51 vs 1.33 ± 0.76 mg/ml, p < 0.001). On multivariate analysis, EGFR mutation (odds ratio (OR): 3.116, 95% confidential interval (CI):1.182-8.213, p = .019) and iodine concentration (OR: 1.112, 95% CI:1.034-1.196, p = .006) were found to be significant for predicting a treatment response.Conclusions: Dual-energy CT using a quantitative analytic method based on iodine concentration measurements can be used to predict the effects of chemotherapy in patients with advanced adenocarcinoma. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: To investigate the feasibility of dual-energy computed tomography (DECT) in differentiating malignant from benign mediastinal tumors. Materials and methods: We prospectively enrolled 25 patients (14 males; mean age: 56.7 years) who had suspected mediastinal tumors on chest radiography or non-contrast chest computed tomography (CT). All patients underwent a two-phase DECT using gemstone spectral imaging (GSI) mode (GE HD750). For the quantitative analysis, two investigators measured the following parameters of the tumors in the early and the delayed phases: CT attenuation value in Hounsfield units (HU) and iodine concentration (mg/ml). Pathological results were used for a final diagnosis. Statistical analyses were performed using the Fisher's exact test and the Mann–Whitney t-test. Results: 10 patients (40%) had benign pathology, while 15 (60%) had malignant pathology. The iodine concentration measurements were significantly different between benign and malignant tumors both in the early phase (1.38mg/ml vs. 2.41mg/ml, p =0.001) and in the delayed phase (1.52mg/ml vs. 2.84mg/ml, p =0.001), while mean attenuation values were not significantly different in both phases (57.8HU vs. 69.1HU, p =0.067 and 67.4HU vs. 78.4HU, p =0.086, respectively). Conclusions: Dual-energy CT using a quantitative analytic methodology can be used to differentiate between benign and malignant mediastinal tumors [Copyright &y& Elsevier]

Purpose: We aimed to develop a deep learning (DL)-based algorithm for automated quantification of aortic valve calcium (AVC) from non-enhanced electrocardiogram-gated cardiac CT scans and compare performance of DL-measured AVC volume and Agatston score with those of visual gradings by radiologist readers for classification of AVC severity.Method: A total of 589 CT examinations performed at a single center between March 2010 and August 2017 were retrospectively included. The DL algorithm was designed to segment AVC and to quantify AVC volume, and Agatston score was calculated using attenuation values. Manually measured AVC volume and Agatston score were used as ground truth. To validate AVC segmentation performance, the Dice coefficient was calculated. For observer performance testing, four radiologists determined AVC grade in two reading rounds. The diagnostic performance of DL-measured AVC volume and Agaston score for classifying severe AVC was compared with that of each reader's assessment.Results: After applying the DL algorithm, the Dice coefficient score was 0.807. In patients with AVC, accuracy of DL-measured AVC volume for AVC grading was 97.0 % with area under the curve (AUC) of 0.964 (95 % confidence interval [CI] 0.923-1) in the test set, which was better than the radiologist readers (accuracy 69.7 %-91.9 %, AUC 0.762-0.923) with manually measured AVC volume as ground truth. When manually measured AVC Agatston score was used as ground truth, accuracy of DL-measured AVC Agatston score for AVC grading was 92.9 % with AUC of 0.933 (95 % CI 0.885-0.981) in the test set, which was also better than the radiologist readers (accuracy 77.8-89.9 %, AUC 0.791-0.903).Conclusions: DL-based automated AVC quantification may be comparable with manual measurements. The diagnostic performance of the DL-measured AVC volume and Agatston score for classification of severe AVC outperforms radiologist readers. [ABSTRACT FROM AUTHOR]

Abstract: Automatic computer-assisted detection (auto-CAD) of significant coronary artery disease (CAD) in coronary computed tomography angiography (cCTA) has been shown to have relatively high accuracy. However, to date, scarce data are available regarding the performance of auto-CAD in the setting of acute chest pain. This study sought to demonstrate the feasibility of an auto-CAD algorithm for cCTA in patients presenting with acute chest pain. We retrospectively investigated 398 consecutive patients (229 male, mean age 50±21 years) who had acute chest pain and underwent cCTA between Apr 2007 and Jan 2011 in the emergency department (ED). All cCTA data were analyzed using an auto-CAD algorithm for the detection of >50% CAD on cCTA. The accuracy of auto-CAD was compared with the formal radiology report. In 380 of 398 patients (18 were excluded due to failure of data processing), per-patient analysis of auto-CAD revealed the following: sensitivity 94%, specificity 63%, positive predictive value (PPV) 76%, and negative predictive value (NPV) 89%. After the exclusion of 37 cases that were interpreted as invalid by the auto-CAD algorithm, the NPV was further increased up to 97%, considering the false-negative cases in the formal radiology report, and was confirmed by subsequent invasive angiogram during the index visit. We successfully demonstrated the high accuracy of an auto-CAD algorithm, compared with the formal radiology report, for the detection of >50% CAD on cCTA in the setting of acute chest pain. The auto-CAD algorithm can be used to facilitate the decision-making process in the ED. [Copyright &y& Elsevier]