Histogram analysis of ADC maps based on entire tumor volume can be a useful tool for grading gliomas. The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter for differentiating high- from low-grade gliomas.

In part-solid GGNs, higher kurtosis and smaller mass are significant differentiators of preinvasive lesions from IPAs, and preinvasive lesions can be accurately differentiated from IPAs by using computerized texture analysis. Online supplemental material is available for this article.

A substantial proportion of PSNs detected at screening CT were transient. Transient PSNs could be predicted with high accuracy by using the features of young patient age, detection of the PSN at follow-up, blood eosinophilia, lesion multiplicity, large solid portion, and ill-defined lesion border.

Institutional review board approval was obtained. Informed patient consent was not required for this retrospective study, which involved review of previously obtained image data. Patient confidentiality was protected; the study was compliant with the Health Insurance Portability and Accountability Act. An automated pulmonary nodule detection program that takes advantage of three-dimensional volumetric data was developed and tested with multi-detector row computed tomographic (CT) images from 20 patients (13 men, seven women; age range, 40-75 years) with pulmonary nodules. A total of 164 nodules 3 mm in diameter and larger were detected by two radiologists in consensus and were used as a reference standard to evaluate the computer-aided detection (CAD) program. The CAD algorithm was structured to process nodules that were categorized into three types: isolated, juxtapleural, and juxtavascular. Overall sensitivity for nodule detection with the CAD program was 95.1% (156 of 164 nodules). The sensitivity according to nodule size was 91.2% (52 of 57 nodules) for nodules 3 mm to less than 5 mm and 97.2% (104 of 107 nodules) for nodules 5 mm and larger. The number of false-positive detections per patient was 6.9 for false nodule structures 3 mm and larger and 4.0 for false nodule structures 5 mm and larger.

Our purpose was to identify thin-section chest computed tomography (CT) findings of malignancy other than the presence of a solid portion within ground-glass nodules (GGNs) and to evaluate whether the radiologists' performance in determining malignancy can be enhanced with this information. The predictive CT findings of malignancy extracted from the CT findings of 80 GGNs (47 malignant, 33 benign) were a size of >8 mm [odds ratio (OR), 10.930; P = 0.045] and a lobulated border (OR, 13.769; P = 0.016) for pure GGNs and a lobulated border (OR, 10.200; P = 0.024) for mixed GGNs. Four chest radiologists and five radiology residents participated in the observer performance study with CT of 130 GGNs (67 malignant, 63 benign). Receiver-operating characteristic (ROC) analysis was used to compare radiologists' performances before and after providing these predictive findings. For pure GGNs, mean areas under the curve (A(z)) of all readers without and with CT predictive information were significantly different (0.621 +/- 0.052 and 0.766 +/- 0.055, P < 0.05). For mixed GGNs, the A(z) values achieved without and with predictive information were not significantly different (0.727 +/- 0.064 and 0.764 +/- 0.056, P > 0.05). Information about lesion size and morphological characteristics can enhance radiologists' performance in determining malignancy of pure GGNs.