Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images.

PURPOSE: To evaluate the added value of computer-aided detection (CAD) for lung nodules on chest radiographs when radiologists have bone-suppressed images (BSIs) available. MATERIALS AND METHODS: Written informed consent was waived by the institutional review board. Selection of study images and study setup was reviewed and approved by the institutional review boards. Three hundred posteroanterior (PA) and lateral chest radiographs (189 radiographs with negative findings and 111 radiographs with a solitary nodule) in 300 subjects were selected from image archives at four institutions. PA images were processed by using a commercially available CAD, and PA BSIs were generated. Five radiologists and three residents evaluated the radiographs with BSIs available, first, without CAD and, second, after inspection of the CAD marks. Readers marked locations suspicious for a nodule and provided a confidence score for that location to be a nodule. Location-based receiver operating characteristic analysis was performed by using jackknife alternative free-response receiver operating characteristic analysis. Area under the curve (AUC) functioned as figure of merit, and P values were computed with the Dorfman-Berbaum-Metz method. RESULTS: Average nodule size was 16.2 mm. Stand-alone CAD reached a sensitivity of 74% at 1.0 false-positive mark per image. Without CAD, average AUC for observers was 0.812. With CAD, performance significantly improved to an AUC of 0.841 (P = .0001). CAD detected 127 of 239 nodules that were missed after evaluation of the radiographs together with BSIs pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates. CONCLUSION: CAD improved radiologists' performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral radiographs and BSIs. Still, most of the true-positive CAD candidates are dismissed by observers.

Diabetes increases atrophy and vascular lesions on brain MRI in patients with symptomatic arterial disease.

BACKGROUND AND PURPOSE: Diabetes type 2 (DM2) is associated with accelerated cognitive decline and structural brain abnormalities. Macrovascular disease has been described as a determinant for brain MRI changes in DM2, but little is known about the involvement of other DM2-related factors. METHODS: Brain MRI was performed in 1043 participants (151 DM2) with symptomatic arterial disease. Brain volumes were obtained through automated segmentation. RESULTS: Patients with arterial disease and DM2 had more global and subcortical brain atrophy (-1.20% brain/intracranial volume [95%CI -1.58 to -0.82], P<0.0005 and 0.20% ventricular/intracranial volume [0.05 to 0.34], P<0.01), larger WMH volumes (0.22 logtransformed volume [0.07 to 0.38], P<0.005), and more lacunar infarcts (OR 1.75 [1.13 to 2.69], P<0.01) than identical patients without DM2. In patients with DM2, high glucose levels (B-0.12% per mmol/L [-0.23 to -0.01], P<0.05) and diabetes duration (B-0.05% per year [-0.10 to -0.001], P<0.05) were associated with global brain atrophy. CONCLUSIONS: In patients with symptomatic arterial disease, DM2 has an added detrimental effect on the brain. In patients with DM2, hyperglycemia and diabetes duration contribute to brain atrophy.

Total cerebral blood flow, white matter lesions and brain atrophy: the SMART-MR study.

We investigated whether total cerebral blood flow (CBF) was associated with brain atrophy, and whether this relation was modified by white matter lesions (WML). Within the Second Manifestations of ARTerial disease-magnetic resonance (SMART-MR) study, a prospective cohort study among patients with arterial disease, cross-sectional analyses were performed in 828 patients (mean age 58+/-10 years, 81% male) with quantitative flow, atrophy, and WML measurements on magnetic resonance imaging (MRI). Total CBF was measured with MR angiography and was expressed per 100 mL brain volume. Total brain volume and ventricular volume were divided by intracranial volume to obtain brain parenchymal fraction (BPF) and ventricular fraction (VF). Lower BPF indicates more global brain atrophy, whereas higher VF indicates more subcortical brain atrophy. Mean CBF was 52.0+/-10.2 mL/min per 100 mL, mean BPF was 79.2+/-2.9%, and mean VF was 2.03+/-0.96%. Linear regression analyses showed that lower CBF was associated with more subcortical brain atrophy, after adjusting for age, sex, vascular risk factors, intima-media thickness, and lacunar infarcts, but only in patients with moderate to severe WML (upper quartile of WML): Change in VF per s.d. decrease in CBF 0.18%, 95% CI: 0.02 to 0.34%. Our findings suggest that cerebral hypoperfusion in the presence of WML may be associated with subcortical brain atrophy.

Semi-automatic classification of textures in thoracic CT scans.

The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64% for the first slice to 13% for the second half of the scan. Overall, 21% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20%, decreasing from 56% in the first slice to 13% in the second half of the scan.

Metabolic syndrome, prediabetes, and brain abnormalities on mri in patients with manifest arterial disease: the SMART-MR study.

OBJECTIVE: Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors leading to atherosclerosis and diabetes. Diabetes is associated with both structural and functional abnormalities of the brain. MetS, even before diabetes is diagnosed, may also predispose to cerebral changes, probably through shared mechanisms. We examined the association of MetS with cerebral changes in patients with manifest arterial disease. RESEARCH DESIGN AND METHODS: Cross-sectional data on MetS and brain MRI were available in 1,232 participants with manifest arterial disease (age 58.6 ± 10.1 years; 37% MetS). Volumes of brain tissue, ventricles, and white matter hyperintensities (WMH) were obtained by automated segmentation and expressed relative to intracranial volume. Infarcts were distinguished into lacunar and nonlacunar infarcts. RESULTS: The presence of MetS (n = 451) was associated with smaller brain tissue volume (B -0.72% [95% CI -0.97, -0.47]), even in the subgroup of patients without diabetes (B -0.42% [95% CI -0.71, -0.13]). MetS was not associated with an increased occurrence of WMH or cerebral infarcts. Impaired glucose metabolism, abdominal obesity, and elevated triglycerides were individual components associated with smaller brain volume. Obesity and hypertriglyceridemia remained associated with smaller brain volume when patients with diabetes were excluded. Hypertension was associated with an increased occurrence of WMH and infarcts. CONCLUSIONS: In patients with manifest arterial disease, presence of MetS is associated with smaller brain volume, even in patients without diabetes. Screening for MetS and treatment of its individual components, in particular, hyperglycemia, hypertriglyceridemia, and obesity, may prevent progression of cognitive aging in patients with MetS, even in a prediabetic stage.

Bone suppressed images improve radiologists' detection performance for pulmonary nodules in chest radiographs.

OBJECTIVES: To assess the effect of bone suppression imaging on observer performance in detecting lung nodules in chest radiographs. MATERIALS AND METHODS: Posteroanterior (PA) and lateral digital chest radiographs of 111 (average age 65) patients with a CT proven solitary nodule (median diameter 15 mm), and 189 (average age 63) controls were read by 5 radiologists and 3 residents. Conspicuity of nodules on the radiographs was classified in obvious (n = 32), moderate (n = 32), subtle (n = 29) and very subtle (n = 18). Observers read the PA and lateral chest radiographs without and with an additional PA bone suppressed image (BSI) (ClearRead Bone Suppression 2.4, Riverain Technologies, Ohio) within one reading session. Multi reader multi case (MRMC) receiver operating characteristics (ROC) were used for statistical analysis. RESULTS: ROC analysis showed improved detection with use of BSI compared to chest radiographs alone (AUC = 0.883 versus 0.855; p = 0.004). Performance also increased at high specificities exceeding 80% (pAUC = 0.136 versus 0.124; p = 0.0007). Operating at a specificity of 90%, sensitivity increased with BSI from 66% to 71% (p = 0.0004). Increase of detection performance was highest for nodules with moderate and subtle conspicuity (p = 0.02; p = 0.03). CONCLUSION: Bone suppressed images improve radiologists' detection performance for pulmonary nodules, especially for those of moderate and subtle conspicuity.

Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes.

OBJECTIVE: Type 2 diabetes is associated with a moderate degree of cerebral atrophy and a higher white matter hyperintensity (WMH) volume. How these brain-imaging abnormalities evolve over time is unknown. The present study aims to quantify cerebral atrophy and WMH progression over 4 years in type 2 diabetes. RESEARCH DESIGN AND METHODS: A total of 55 patients with type 2 diabetes and 28 age-, sex-, and IQ-matched control participants had two 1.5T magnetic resonance imaging scans with a 4-year interval. Volumetric measurements of total brain, peripheral cerebrospinal fluid (CSF), lateral ventricles, and WMH were performed with k-nearest neighbor-based probabilistic segmentation. All volumes were expressed as percentage of intracranial volume. Linear regression analyses, adjusted for age and sex, were performed to compare brain volumes between the groups and to identify determinants of volumetric change within the type 2 diabetic group. RESULTS: At baseline, patients with type 2 diabetes had a significantly smaller total brain volume and larger peripheral CSF volume than control participants. In both groups, all volumes showed a significant change over time. Patients with type 2 diabetes had a greater increase in lateral ventricular volume than control participants (mean adjusted between-group difference in change over time [95% CI]: 0.11% in 4 years [0.00 to 0.22], P = 0.047). CONCLUSIONS: The greater increase in lateral ventricular volume over time in patients with type 2 diabetes compared with control participants shows that type 2 diabetes is associated with a slow increase of cerebral atrophy over the course of years.