Mean CT Attenuation Research Articles

Histogram analysis based on unenhanced CT for identifying thymoma and lymphoma among prevascular mediastinal incidentalomas

ObjectiveTo determine whether histogram analysis based on unenhanced CT can play a role in the differential diagnosis of thymoma and lymphoma from thymic hyperplasia and cyst (mean CT attenuation > 10 HU).Materials and methodsThis retrospective study included consecutive asymptomatic participants who have prevascular mediastinal lesions incidentally detected by unenhanced CT between December 2013 and August 2020, and with definitive diagnosis by pathology or additional radiologic work-ups. A total of thirteen histogram parameters on enhanced CT were calculated for each lesion, then were compared between tumor (thymoma + lymphoma) and non-tumor (hyperplasia + cyst). Receiver operating characteristic analysis was conducted to investigate the performance of histogram parameter for identifying tumor.ResultsThe study population included 192 patients (106 men and 86 women) with a mean age of 50.5 years at the time of CT examination. Of them, 94 patients have tumor (87 thymomas and 7 lymphoma) and 98 have non-tumor (48 thymic hyperplasia and 50 cysts). Nine of the thirteen histogram parameters revealed significant difference between the two groups, including median, minimum, range, 10th percentile, 90th percentile, kurtosis, skewness, uniformity and entropy. No significant difference was observed in the mean CT attenuation between groups. Higher median was found to be independent predictors for distinguishing tumor from non-tumor, and can achieve an area under the curve (AUC) of 0.785 (95% confidence interval [95% IC], 0.720–0.841).ConclusionsHistogram analysis based on unenhanced CT may be able to provide some help in the differential diagnosis of incidental lesions in prevascular mediastinal.Grand supportThis study was sponsored by Natural Science Foundation of Shanghai (No. 21ZR1459700).

Improved Visualization and Quantification of Net Water Uptake in Recent Small Subcortical Infarcts in the Thalamus Using Computed Tomography.

Diagnosing recent small subcortical infarcts (RSSIs) via early computed tomography (CT) remains challenging. This study aimed to assess CT attenuation values (Hounsfield Units (HU)) and net water uptake (NWU) in RSSI and explore a postprocessing algorithm's potential to enhance thalamic RSSI detection. We examined non-contrast CT (NCCT) data from patients with confirmed thalamic RSSI on diffusion-weighted magnetic resonance imaging (DW-MRI) between January 2010 and October 2017. Co-registered DW-MRI and NCCT images enabled HU and NWU quantification in the infarct area compared to unaffected contralateral tissue. Results were categorized based on symptom onset to NCCT timing. Postprocessing using window optimization and frequency-selective non-linear blending (FSNLB) was applied, with interpretations by three blinded Neuroradiologists. The study included 34 patients (median age 70 years [IQR 63-76], 14 women). RSSI exhibited significantly reduced mean CT attenuation compared to unaffected thalamus (29.6 HU (±3.1) vs. 33.3 HU (±2.6); p < 0.01). Mean NWU in the infarct area increased from 6.4% (±7.2) at 0-6 h to 16.6% (±8.7) at 24-36 h post-symptom onset. Postprocessed NCCT using these HU values improved sensitivity for RSSI detection from 32% in unprocessed CT to 41% in FSNLB-optimized CT, with specificities ranging from 86% to 95%. In conclusion, CT attenuation values and NWU are discernible in thalamic RSSI up to 36 h post-symptom onset. Postprocessing techniques, particularly window optimization and FSNLB, moderately enhance RSSI detection.

Greater Severity of Steatosis Is Associated with a Higher Risk of Incident Diabetes: A Retrospective Longitudinal Study.

Fatty liver is associated with increased risk of developing type 2 diabetes. We aimed to evaluate whether the severity of hepatic steatosis is associated with incident diabetes. We conducted a longitudinal analysis using data from 1,798 participants who underwent a comprehensive health checkup and abdominal computed tomography (CT). We assessed the association between baseline liver attenuation value on non-contrast CT images and risk of incident diabetes. All the participants were categorized into three groups based on the baseline liver attenuation value on non-contrast CT images: without hepatic steatosis (>57 Hounsfield unit [HU]), mild hepatic steatosis (41-57 HU), and moderate to severe hepatic steatosis (≤40 HU). During a median follow-up period of 5 years, 6.0% of the study participants progressed to diabetes. The incidence of diabetes was 17.3% in the moderate to severe hepatic steatosis group, 9.0% in the mild steatosis group, and 2.9% in those without hepatic steatosis. In a multivariate adjustment model, as compared with participants without hepatic steatosis, those with moderate to severe steatosis had a hazard ratio (HR) of 3.24 (95% confidence interval [CI], 1.64 to 4.2) for the development of diabetes, and those in the mild steatosis group had a HR of 2.33 (95% CI, 1.42 to 3.80). One standard deviation decrease in mean CT attenuation values of the liver was associated with a 40% increase in the development of diabetes (multivariate adjusted HR, 1.40; 95% CI, 1.2 to 1.63). We found a positive association between severity of hepatic steatosis and risk of incident diabetes. Greater severity of steatosis was associated with a higher risk of incident diabetes.

Association between hypertension and myosteatosis evaluated by abdominal computed tomography.

Few studies have examined the relationship between myosteatosis and hypertension, and no studies have enrolled an Asian population. Existing studies also found discordant results, possibly due to the use of conventional myosteatosis indices that are not sufficiently reliable and representative. Therefore, we investigated the association between myosteatosis and hypertension in Asian individuals using novel, objective computed tomography (CT) markers. The total abdominal muscle area (TAMA) was determined from abdominal CT scans taken at the L3 level. Based on the mean CT attenuation, the TAMA was divided into intramuscular adipose tissue and skeletal muscle area (SMA), which was further segmented into normal attenuation muscle area (NAMA) and low attenuation muscle area (LAMA). Among SMA/body mass index (BMI), NAMA/BMI, LAMA/BMI, and the NAMA/TAMA index, NAMA/BMI was chosen through receiver operating characteristic curves as the best predictive marker for hypertension. The hypertension risk for each quartile of NAMA/BMI was calculated by logistic regression analysis. Among the 19,766 participants, 40.3% of men and 23.8% of women had hypertension. People with hypertension showed unhealthier myosteatosis profiles than normotensive controls. Similarly, a lower NAMA/BMI was significantly associated with a greater hypertension risk. The lowest quartile group of NAMA/BMI exhibited 2.3- and 2.6-fold higher risks of hypertension than the highest quartile in men and women, respectively. In conclusion, advanced myosteatosis assessed by abdominal CT was significantly correlated with a higher risk of hypertension. Improving myosteatosis may be a new approach for preventing cardiovascular diseases, including hypertension. Advanced myosteatosis measured by abdominal CT taken at the L3 level was significantly correlated with a higher risk of hypertension even after adjusting for health behaviors, intake of lipid-lowering drugs, plasma lipid levels, and other ectopic fat distribution.

Natural progression of persistent pure ground-glass nodules 10mm or smaller: long-term observation and risk factor assessment.

Semi-automatic segmentation was used to investigate the natural progression of pure ground-glass nodules (pGGNs) of 5-10mm in long-term follow-up and to analyze independent risk factors for subsequent growth. A total of 154 pGGNs of 5-10mm from 132 patients with 698 follow-up CT scans were retrospectively identified. Subsequently, enrolled pGGNs were semiautomatically segmented on initial and follow-up CT to obtain diameter, density and volume, thus calculating mass, volume doubling time (VDT), and mass doubling time (MDT). Kaplan‒Meier analysis and multivariate Cox proportional risk regression were performed to explore independent predictors of pGGN growth. We analyzed growth differences among different pathological results of pGGNs confirmed by surgery. The prognosis was analyzed using the total diameter or solid size of the nodules on the last preoperative CT. Among the 85 (55.2%) pGGNs with growth, 5.9%, 51.8%, and 80.0% showed growth within 1, 3, and 5years, respectively. The median VDT and MDT were 1206.4 (range 349.8-5134.4) days and 1161.3 (range 339.4-6630.4) days, respectively. The multivariate Cox risk regression analysis showed that mean CT attenuation (m-CTA) [hazard ratio (HR) = 2.098, p = 0.010] and roundness index (HR = 1.892, p = 0.021) were independent risk factors for pGGN growth. In total, 67.6% of surgically resected and growing pGGNs were invasive non-mucinous adenocarcinoma (IA), including 2 cases of endpoint events, showing a PSN with solid components of 5.6mm and a solid nodule with a diameter of 19.9mm. pGGNs of 5-10mm showed an indolent clinical course. Follow-up CT imaging of pGGNs in the latter half of the first two years should be a rational management strategy. Small pGGNs with a larger overall m-CTA and roundness index on baseline CT are more likely to grow.

Enhancement of Anemia Detection by Correlating Computed Tomography Findings of Abdominal Aorta and Inferior Vena Cava With Laboratory Investigations.

Anemia affects approximately a quarter of the global population, and improved detection may reduce the associated morbidity and mortality. This study investigated correlations between the results of laboratory hematological determinations of hemoglobin levels and attenuation values measured in the lumina of the abdominal aorta and inferior vena cava (IVC) via unenhanced computed tomography (CT) with the aim of expanding diagnostic options for anemia. The data of 423 patients who underwent abdominal unenhanced CT examinations and laboratory examinations at a tertiary hospital were retrospectively evaluated. CT data were collected using a standard abdominal protocol without contrast. The 151 patients who met the inclusion criteria were categorized by hemoglobin values as follows: <8 (severe anemia), 8-10.9 (moderate anemia), 10.9-12 (mild anemia in females), 10.9-13 (mild anemia in males), and >13 g/dL (non-anemic). The mean CT attenuation values in the aorta and IVC were 37.7 and 36.1 Hounsfield units (HU), respectively. A regression analysis performed to evaluate the correlation and predictability of hemoglobin-based aortic and IVC density yielded a coefficient of determination, R2: 0.42 (F ratio: 149.23, p < 0.0001). The highest contribution in the dependent variable (hemoglobin) was reported to IVC density, showing a significant positive correlation between hemoglobin and IVC density. Our study results demonstrate significant correlations between the densities of the aorta, IVC, and hemoglobin value. Accordingly, radiologists and clinicians can use these readily available values to facilitate diagnosis and patient care.

Establishment and validation a prediction model for discrimination of invasive adenocarcinomas for patients with peripheral pulmonary subsolid nodules.

The optimal management of patients with subsolid pulmonary nodules is of growing clinical concern. This study sought to develop and validate a more precise predictive model to evaluate the pathological invasiveness of patients with lung peripheral subsolid nodules (SSNs). The data of 1,140 patients with peripheral SSNs who underwent surgical resection at Shanghai Renji Hospital from January 2014 to December 2018 were retrospectively analyzed. The patients were randomly assigned to a training and validation cohort (at a ratio of 2 to 1). Clinical parameters and imaging features were collected to estimate the independent predictors of pathological invasiveness of SSNs. A nomogram model was developed and applied to the validation cohort. The predictive performance of the nomogram model was evaluated by a calibration curve analysis, an area under the receiver operating characteristic curve (AUC) analysis, and a decision curve analysis (DCA), which was also compared with other diagnostic models. In the multivariate analysis, the nodule diameter (P<0.001), solid component size (P<0.001), mean CT attenuation (P=0.001), spiculation (P<0.001), and pleura indentation (P=0.011) were identified as independent predictors of the pathological invasiveness of SSNs. A nomogram model based on the results of the multivariate analysis was developed and showed a robust discrimination in the validation cohort, with an AUC of [0.890; 95% confidence interval (CI), 0873-0.907], which was higher than another two reported models. The calibration curve showed optimal agreement between the pathological invasive probability as predicted by the nomogram and the actual probability. We developed and validated a nomogram model to evaluate the risk of the pathological invasiveness for patients with lung SSNs. The AUC of this nomogram model was higher than another two reported models. Our nomogram model may help clinicians to make individualized treatment more precisely.

Opportunistic screening for osteoporosis and osteopenia from CT scans of the abdomen and pelvis using machine learning.

To use multivariable machine learning using the computed tomography (CT) attenuation ofeach of the bones in the lumbar spine, pelvis, and sacrum, to predict osteoporosis/osteopenia. This was a retrospective study of 394 patientsaged 50 years or older with CT scans of the abdomen and pelvis and dual-energy x-ray absorptiometry (DXA) scans obtained within 6 months of each other. Volumetricsegmentations were performed for each of the bones from L1-L4 vertebrae, pelvis, and sacrum to obtain the mean CT attenuation of each bone.The data was randomly split into training/validation (n = 274, 70%) and test (n = 120, 30%) datasets. The CT attenuation ofthe L1vertebrae, univariate logistic regression, least absolute shrinkage and selection operator (LASSO), and support vector machines (SVM) with radial basis function (RBF) were used to predict osteoporosis/osteopenia. The performance of using the CT attenuation at L1 to the univariate logistic regression, LASSO, and SVM models were compared using DeLong's test in the test dataset. All CT attenuation measurements were predictive of osteoporosis/osteopenia (p < 0.001 for all). The SVM model (accuracy = 0.892, AUC = 0.886) outperformed the models using the CT attenuation of threshold of 173.9 Hounsfield units (HU) at L1 (accuracy = 0.725, AUC = 0.739, p = 0.010), the univariate logistic regression model (accuracy = 0.767, AUC = 0.533, p < 0.001) and the LASSO model (accuracy = 0.817, AUC = 0.711, p = 0.007) to predict osteoporosis/osteopenia. A SVM model using the CT attenuations of multiple bones within the lumbar spine andpelvisand clinical data has a better ability to predict osteoporosis/osteopenia than using the CT attenuation of L1 or a LASSO model. • Multivariable SVM model using the CT attenuation of multiple bones and clinical/demographic data was more predictive than using the CT attenuation at L1 only.

Support vector machines are superior to principal components analysis for selecting the optimal bones’ CT attenuations for opportunistic screening for osteoporosis using CT scans of the foot or ankle

ObjectivesTo use the computed tomography (CT) attenuation of the foot and ankle bones for opportunistic screening for osteoporosis. MethodsRetrospective study of 163 consecutive patients from a tertiary care academic center who underwent CT scans of the foot or ankle and dual-energy X-ray absorptiometry (DXA) within 1 year of each other. Volumetric segmentation of each bone of the foot and ankle was done in 3D Slicer to obtain the mean CT attenuation. Pearson's correlations were used to correlate the CT attenuations with each other and with DXA measurements. Support vector machines (SVM) with various kernels and principal components analysis (PCA) were used to predict osteoporosis and osteopenia/osteoporosis in training/validation and test datasets. ResultsCT attenuation measurements at the talus, calcaneus, navicular, cuboid, and cuneiforms were correlated with each other and positively correlated with BMD T-scores at the L1-4 lumbar spine, hip, and femoral neck; however, there was no significant correlation with the L1-4 trabecular bone scores. A CT attenuation threshold of 143.2 Hounsfield units (HU) of the calcaneus was best for detection of osteoporosis in the training/validation dataset. SVMs with radial basis function (RBF) kernels were significantly better than the PCA model and the calcaneus for predicting osteoporosis in the test dataset. ConclusionsOpportunistic screening for osteoporosis is possible using the CT attenuation of the foot and ankle bones. SVMs with RBF using all bones is more accurate than the CT attenuation of the calcaneus.

A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma.

ObjectiveThe morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD.MethodsA total of 301 surgical LUAD patients with HRCT-confirmed GGN were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 171 invasive and 130 non-invasive LUAD patients).ResultsGGN features including shorter distance from pleura, larger diameter, area and mean CT attenuation, more heterogeneous uniformity of density, irregular shape, coarse margin, not defined nodule-lung interface, spiculation, pleural indentation, air bronchogram, vacuole sign, vessel changes, lobulation were observed in invasive LUAD patients compared with non-invasive LUAD patients. After adjustment by multivariate logistic regression model, GGN diameter (OR = 1.490, 95% CI, 1.326–1.674), mean CT attenuation (OR = 1.007, 95% CI, 1.004–1.011) and heterogeneous uniformity of density (OR = 3.009, 95% CI, 1.485–6.094) were independent risk factors for invasive LUAD. In addition, a predictive model integrating these three independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.919, 95% CI, 0.889–0.949).ConclusionsILAG predictive model integrating GGN diameter, mean CT attenuation and heterogeneous uniformity of density via HRCT shows great potential for early estimation of LUAD invasiveness.

Machine learning for the prediction of osteopenia/osteoporosis using the CT attenuation of multiple osseous sites from chest CT

PurposeTo use machine learning and the CT attenuation of all bones visible on chest CT scans to predict osteopenia/osteoporosis. MethodWe retrospectively evaluated 364 patients with CT scans of the chest, and Dual-energy X-ray absorptiometry (DXA) scans within 6 months of each other. Studies were performed between 01/01/2015 and 08/01/2021. Volumetric segmentation of the ribs, thoracic vertebrae, sternum, and clavicle was performed using 3D Slicer to obtain the mean CT attenuation of each bone. The study sample was randomly split into training/validation (80 %, n = 291 patients) and test (20 %, n = 73 patients) datasets. Univariate analyses were used to identify the optimal CT attenuation thresholds to diagnose osteopenia/osteoporosis. We used penalized multivariable logistic regression models including Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Ridge regression, and Support Vector Machines (SVM) with radial basis functions (RBF) to predict osteopenia/osteoporosis and compared these results to the CT attenuation threshold at T12. ResultsThere were positive correlations between the CT attenuation between all bones (r > 0.6, P < 0.001 for all). There were positive correlations between CT attenuation of the bones and the L1-L4 BMD T-score, total hip T-score, and femoral neck T-scores (r > 0.4, P < 0.001 for all). A CT attenuation threshold of 170.2 Hounsfield units (HU) at T12 had an AUC of 0.702, while a threshold of 192.1 HU at T4 had an AUC of 0.757. The SVM with RBF had the highest AUC (AUC = 0.864) and was better than the LASSO (P = 0.011), Elastic Net (P = 0.011), Ridge regression (P = 0.011) but was not better than using the CT attenuation at T12 (P = 0.060). ConclusionsThe CT attenuation of the ribs, thoracic vertebra, sternum, and clavicle can be used individually and collectively to predict BMD and to predict osteopenia/osteoporosis.

Diagnostic accuracy of CT Hounsfield unit in distinguishing Exudative and Transudative Pleural Effusion

Background: Differentiation of exudative and transudative types of pleural effusion is crucial for management of patients. Currently, this differentiation is done with the help of biochemical analysis of the aspirated fluid after thoracentesis, which is an invasive procedure. Objective: To evaluate the utility of CT attenuation values in pleural effusion and define a threshold value to differentiate between exudative and transudative pleural effusion. Materials &amp; Methods: A cross sectional study was done on 130 patients showing pleural effusion on CT thorax, mean attenuation was calculated using nine regions of interest on three slices. Within 48 hours of imaging, biochemical assessment was done to decide exudative or transudative nature of fluid as per Light’s criteria. A Receiver operating characteristic curve was drawn to assess the cut off CT attenuation value for distinguishing between exudative and transudative effusion and evaluate its accuracy. Result: Mean CT attenuation value of 57 exudative effusion cases was 5.9 + 6.48 HU, which was higher than 73 cases with transudative pleural effusion (2.97 + 2.69 HU). The difference was statistically significant (p value &lt; 0.0001). The ROC curve revealed a cut off value of 7.5 HU. When the mean CT attenuation value was &gt; 7.5 HU, the specificity and sensitivity of CT scan for diagnosing exudative effusion was 98.6 % and 59.6% respectively. Area under the curve was 0.79, which revealed moderate accuracy of this threshold value. Conclusion: CT attenuation values have moderate accuracy in differentiating between exudative and transudative pleural effusion.

Prognostic value of pericoronary adipose tissue attenuation in patients with non-alcoholic fatty liver disease with suspected coronary artery disease

Pericoronary adipose tissue (PCAT) attenuation on coronary computed tomography angiography (CTA) has been emerged as a marker of pericoronary inflammation. We aimed to investigate the prognostic value of PCAT attenuation in patients with non-alcoholic fatty liver disease (NAFLD). We enrolled 232 NAFLD patients with suspected coronary artery disease and underwent coronary CTA. NAFLD was defined by abdominal CT as the ratio of hepatic attenuation to spleen attenuation less than 1.0. PCAT attenuation values were assessed by the crude analysis of mean CT attenuation value of the left anterior descending artery (LAD) and right coronary artery (RCA). As coronary CTA findings, luminal stenosis and high-risk plaque features were examined. Primary outcome was the composite of cardiovascular (CV) death, nonfatal acute coronary syndrome, and hospitalization for heart failure. During a median follow-up of 4.9years, 17 patients had CV events. LAD-PCAT attenuation in patients with CV events was higher than that without CV events (-66.9 ± 7.0 versus -70.5 ± 6.6; p = 0.032), while RCA-PCAT attenuation was not. LAD-PCAT attenuation and high-risk plaque features were independent predictors of CV events. The addition of LAD-PCAT attenuation to high-risk plaque features increased the C-statistics and global chi-square from 0.66 to 0.75 (p = 0.042) and 6.8 to 12.7 (p = 0.015), respectively. The net reclassification achieved by adding LAD-PCAT attenuation to high-risk plaque features was 0.494 (p = 0.041). High-LAD-PCAT attenuation was an independent predictor of CV events in NAFLD patients, regardless of CTA-verified high-risk plaque features. In addition, LAD-PCAT attenuation had an incremental prognostic value over high-risk plaque features.

Differentiation of Benign Solid Renal Lesions from Malignant Lesions Using Dual-Energy CT

Objective: To evaluate the quantitative measurement of effective atomic number (Zeff) and iodine density reconstructed from dual-energy computed tomography (CT) in the discrimination of malignant renal lesions from benign renal lesions. Materials and Methods: The present study was a retrospective study that included patients with renal lesions who underwent dual-energy CT between January 2017 and June 2020. All subjects had a pathological diagnosis or follow-up imaging of at least 24 months. Two observers independently measured the Zeff, iodine density, and conventional CT attenuation of the renal lesions. Associations between the different tumor types and the quantitative CT measurements reconstructed from dual-energy CT were assessed, and interobserver agreement was evaluated. Results: Sixty-one renal lesions from 48 patients were included in the present study. No statistical differences were found in mean Zeff, iodine density, or CT attenuation between the benign renal lesions and the malignant renal lesions. However, significant differences were observed in Zeff, iodine density, and CT attenuation between the renal cell carcinoma (RCC) and the other malignant tumors, and between the clear cell RCC and the papillary RCC. The optimal thresholds for Zeff, iodine density, and CT attenuation for distinguishing RCC from other malignant tumors were 8.22, 1.71 mg/mL, and 79.50 HU, respectively, which provided sensitivity of 76%, 76%, and 80%, respectively. Conclusion: Benign solid renal lesions cannot be differentiated from malignant renal tumors by Zeff and iodine density measurements alone. However, dual-energy Zeff and iodine density have potential for the non-invasive differentiation of RCC from the other malignant renal tumors, and for the differentiation of RCC subtypes. Keywords: Dual-energy CT; Spectral CT; Renal cell carcinoma; Renal mass; Iodine density; Effective atomic number

Comparison of L1 CT-attenuation and cortical thickness in predicting osteoporosis by opportunistic CT.

In vertebrae, the amount of cortical bone has been estimated at 30-60%, but 45-75% of axial load on a vertebral body is borne by cortical bone. To compare the role of L1 CT-attenuation and cortical thickness in predicting osteoporosis by opportunistic CT and explore cortical thickness value in osteoporosis. We collected data of 94 patients who underwent DXA and thoracic and/or abdominal CT to demonstrate an entire L1 for other indications in routine practice. Patients were divided into three groups according to T-score: osteoporosis, osteopenia, or normal. CT-attenuation value and cortical thickness of L1 were measured. ANOVA analysis was utilized to analyze CT-attenuation and cortical thickness among the three groups. Sensitivity, specificity, and area under the curve (AUC) predicting low BMD were determined using ROC. Pearson correlations were employed to describe relationship between L1 BMD and CT-attenuation value, BMD, as well as cortical thickness. The mean cortical thickness was 0.83±0.11, 0.72±0.10, and 0.64±0.09 mm for normal, osteopenia, and osteoporotic subgroups, respectively. A statistically significant difference was observed in cortical thickness and CT-attenuation value among these three subgroups. A mean CT-attenuation value threshold of > 148.7 yielded 73.0% sensitivity and 86.0% specificity for distinguishing low BMD from normal with an AUC = 0.83. Pearson correlation analysis indicated that BMD was positively correlated with CT-attenuation (r = 0.666, P < 0.001) and cortical thickness (r = 0.604, P < 0.001). L1 CT-attenuation and cortical thickness measured on opportunistic CT can help predict osteoporosis. Compared with cortical thickness, CT-attenuation is a more sensitive and accurate index for distinguishing low BMD from normal.

Machine Learning for Opportunistic Screening for Osteoporosis from CT Scans of the Wrist and Forearm.

Background: We investigated whether opportunistic screening for osteoporosis can be done from computed tomography (CT) scans of the wrist/forearm using machine learning. Methods: A retrospective study of 196 patients aged 50 years or greater who underwent CT scans of the wrist/forearm and dual-energy X-ray absorptiometry (DEXA) scans within 12 months of each other was performed. Volumetric segmentation of the forearm, carpal, and metacarpal bones was performed to obtain the mean CT attenuation of each bone. The correlations of the CT attenuations of each of the wrist/forearm bones and their correlations to the DEXA measurements were calculated. The study was divided into training/validation (n = 96) and test (n = 100) datasets. The performance of multivariable support vector machines (SVMs) was evaluated in the test dataset and compared to the CT attenuation of the distal third of the radial shaft (radius 33%). Results: There were positive correlations between each of the CT attenuations of the wrist/forearm bones, and with DEXA measurements. A threshold hamate CT attenuation of 170.2 Hounsfield units had a sensitivity of 69.2% and a specificity of 77.1% for identifying patients with osteoporosis. The radial-basis-function (RBF) kernel SVM (AUC = 0.818) was the best for predicting osteoporosis with a higher AUC than other models and better than the radius 33% (AUC = 0.576) (p = 0.020). Conclusions: Opportunistic screening for osteoporosis could be performed using CT scans of the wrist/forearm. Multivariable machine learning techniques, such as SVM with RBF kernels, that use data from multiple bones were more accurate than using the CT attenuation of a single bone.

Association of near-infrared spectroscopy-defined lipid rich plaque with lesion morphology and peri-coronary inflammation on computed tomography angiography

Background and aimsRecent studies have reported that the lipid-rich plaque (LRP) detected by near-infrared spectroscopy (NIRS) and the pericoronary adipose tissue attenuation (PCATA) evaluated by coronary computed tomography angiography (CCTA) are associated with worse outcomes. We sought to investigate the relationship between NIRS-defined LRP and PCATA or the incremental ability of PCATA for the prediction of the presence of NIRS-defined LRP when added to the CCTA-derived morphometric findings. MethodsA total of 101 de novo lesions of 101 patients with chronic coronary syndromes (CCS), who underwent pre-procedural CCTA and NIRS during percutaneous coronary intervention (PCI), were retrospectively studied. PCATA was assessed by the crude analysis of the mean CT attenuation value of the culprit vessel. NIRS-defined LRP was defined as a maximum lipid core burden index in 4 mm ≥ 400. Univariate and multivariate logistic regression analyses were performed to determine the predictors of NIRS-defined LRP. ResultsNIRS-defined LRP was observed in 37 patients and median PCATA was −72.71. A significant relationship was observed between LCBI and PCATA (r = 0.24, p = 0.001). PCATA (OR: 4.99; 95% CI: 1.48–16.82; p = 0.010) and CCTA-derived positive remodeling (OR: 12.53; 95% CI: 3.56–44.07; p < 0.001) were independent predictors of NIRS-defined LRP. Net reclassification and integrated discrimination improvement indices were both significantly improved when PCATA was added to the reference model including clinical characteristics and CCTA-derived morphometric findings. ConclusionsComprehensive assessment of CCTA including PCATA may provide reliable information to identify the presence of NIRS-defined LRP potentially leading to future adverse events after PCI.

Comparative study of true and virtual non-contrast imaging generated from dual-layer spectral CT in patients with upper aerodigestive tract cancer.

Dual-layer spectral computed tomography (DLSCT) is a novel CT platform of dual-energy CT. Virtual non-contrast (VNC) imaging theoretically resembles true non-contrast (TNC) imaging by subtracting iodine attenuation from post-contrast data. We aimed to compare qualitative and quantitative datasets between TNC and VNC in patients with upper aerodigestive tract cancer (UATC) and to evaluate the potential radiation dose reduction obtained by omitting the TNC phase. The study included 61 patients with UATC who underwent DLSCT. The CT protocol included TNC and post-contrast phases. The VNC images were reconstructed from the post-contrast phase. The differences of mean CT attenuation values, imaging noise, and image quality for TNC and VNC images were compared. The effective radiation doses of a biphasic TNC and post-contrast CT protocol were compared with a single-phase protocol (post-contrast CT with VNC reconstruction). There were a total of 732 ROIs from TNC and VNC. There was no statistical difference in the mean CT attenuation values between TNC and VNC images for all tissue types (p = 0.09-0.44), except for the buccal fat pad. Overall, 85.3% of cases revealed a difference of less than 10 HU. There was no significant difference in mean imaging noise (p = 0.5455) and image quality (p = 0.3214) between 2 acquisitions. All VNC images had acceptable quality for diagnostic purposes. The potential dose reduction by omitting the TNC was 49.5 ± 3.5%. VNC could replace TNC images in patients with UATC, with good image quality and the advantage of radiation dose reduction.

Clinical Significance of Increased Computed Tomography Attenuation of Periaortic Adipose Tissue in Patients With Abdominal Aortic Aneurysms.

Recent imaging studies reported an association between vascular inflammation and progression of abdominal aortic aneurysm (AAA). This study investigated the clinical significance of periaortic adipose tissue inflammation derived from multidetector computed tomography angiography (MDCTA).Methods and Results:Patients with asymptomatic AAA (n=77) who underwent an index and >6 months follow-up MDCTA examinations were retrospectively investigated. MDCTA analysis included AAA diameter and the periaortic adipose tissue attenuation index (PAAI). The PAAI was defined as the mean CT attenuation value within a predefined range from -190 to -30 Hounsfield units of adipose tissue surrounding the AAA. The growth rate of the AAA was calculated as the change in diameter. AAA progression (AP) was defined as an AAA growth rate ≥5 mm/year. Univariate and multivariate logistic regression analysis were performed to determine the predictors of AP. AP was observed in 19 patients (24.7%), the median baseline AAA diameter was 38.9 mm (interquartile range [IQR] 32.7-42.9 mm), and the median growth rate was 3.1 mm/year (IQR 1.5-4.9 mm/year). Baseline AAA diameter (odds ratio [OR] 1.16; 95% confidence interval [CI] 1.05-1.28; P=0.001) and PAAI (OR 1.12; 95% CI 1.05-1.20; P=0.004) were independent predictors of AP. PAAI was an independent and significant predictor of AP, supporting the notion that local adipose tissue inflammation may contribute to aortic remodeling.

Prognostic significance of imaging features of peritumoral adipose tissue in FDG PET/CT of patients with colorectal cancer

PurposeThis study investigated the relationship of imaging features of primary tumor and peritumoral VAT on PET/CT with histopathological findings of peritumoral VAT and recurrence-free survival (RFS) in patients with colorectal cancer. MethodsWe retrospectively reviewed 133 patients diagnosed with colorectal cancer who underwent staging FDG PET/CT and received curative surgery. Histogram-based imaging features of primary tumor and peritumoral VAT were extracted from PET/CT images. Based on histopathological analysis of peritumoral VAT, the degree of CD4, CD8, and CD163 cell infiltration and the expression of matrix metalloproteinase-11 and interleukin 6 (IL-6) were graded. Differences in imaging parameters based on the histopathological results and the relationships between imaging features and RFS were assessed. ResultsMean CT-attenuation and SUV of peritumoral VAT showed significant positive correlation with CD163 cell infiltration and IL-6 expression of peritumoral VAT. Univariable survival analysis revealed significant correlation between RFS and the mean CT-attenuation, mean SUV, and first-order SUV entropy of peritumoral VAT (p < 0.05). Multivariable analysis indicated that mean SUV and SUV entropy of peritumoral VAT remained significant predictors of RFS after adjustment for age, sex, and T stage (p < 0.05). ConclusionFDG uptake of peritumoral VAT was significantly associated with inflammatory response in peritumoral VAT and was an independent predictor of RFS in colorectal cancer patients.