Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis.

BACKGROUND: The obesity epidemic has prompted the need to better understand the impact of adipose tissue on human pathophysiology. However, accurate, efficient, and replicable models of quantifying adiposity have yet to be developed and clinically implemented. We propose a novel semiautomated radiologic method of measuring the visceral fat area (VFA) using computed tomography scan analysis. MATERIALS AND METHODS: We obtained a cohort of 100 patients with rectal adenocarcinoma, with a median age of 60.9 y (age range: 35-87 y) and an average body mass index of 28.8 kg/m2 ± 6.56 kg/m2. The semiautomated quantification method of adiposity was developed using a commercial imaging suite. The method was compared to two manual delineations performed using two different picture archiving communication systems. We quantified VFA, subcutaneous fat area (SFA), total fat area (TFA), and visceral-to-subcutaneous fat ratio (V/S ratio) on computed tomography axial slices that were at the L4-L5 intervertebral level. RESULTS: The semiautomated method was comparable to manual measurements for TFA, VFA, and SFA with intraclass correlation (ICC) of 0.99, 0.97, and 0.96, respectively. However, the ICC for the V/S ratio was only 0.44, which led to the identification of technical outliers that were identified using robust regression. After removal of these outliers, the ICC improved to 0.99 for TFA, VFA, and SFA and 0.97 for the V/S ratio. Measurements from the manual methodology highly correlated between the two picture archiving communication system platforms, with ICC of 0.98 for TFA, 0.98 for VFA, 0.96 for SFA, and 0.95 for the V/S ratio. CONCLUSIONS: This semiautomated method is able to generate precise and reproducible results. In the future, this method may be applied on a larger scale to facilitate risk stratification of patients using measures of abdominal adiposity.

Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested Case-Control Study.

Purpose To extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer risk by using data and images from the National Lung Screening Trial (NLST). Materials and Methods Radiologic features in SPNs in baseline low-dose computed tomography (CT) screening studies that did not meet NLST criteria to be considered a positive screening examination were extracted. SPNs were identified for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk. All statistical tests were two sided. Results Nine features were significantly different between case patients and control subjects. Backward elimination followed by bootstrap resampling identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932 (95% confidence interval [CI]: 0.88, 0.96), a specificity of 92.38% (95% CI: 52.22%, 84.91%), and a sensitivity of 76.55% (95% CI: 87.50%, 95.35%) that included total emphysema score (odds ratio [OR] = 1.71; 95% CI: 1.39, 2.01), attachment to vessel (OR = 2.41; 95% CI: 0.99, 5.81), nodule location (OR = 3.25; 95% CI: 1.09, 8.55), border definition (OR = 7.56; 95% CI: 1.89, 30.8), and concavity (OR = 2.58; 95% CI: 0.89, 5.64). Conclusion A set of clinically relevant radiologic features were identified that that can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs. © RSNA, 2017 Online supplemental material is available for this article.

Multi-window CT based Radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study.

BACKGROUND: We retrospectively evaluated the capability of radiomic features to predict tumor growth in lung cancer screening and compared the performance of multi-window radiomic features and single window radiomic features. METHODS: One hundred fifty lung nodules among 114 screen-detected, incident lung cancer patients from the National Lung Screening Trial (NLST) were investigated. Volume double time (VDT) was calculated as the difference between continuous two scans and used to define indolent and aggressive lung cancers. Lung nodules were semi-automatically segmented using lung and mediastinal windows separately, and subtracting the mediastinal window region from the lung window region generated the difference region. 364 radiomic features were separately exacted from nodules using the lung window, the mediastinal window and the difference region. Multivariable models were conducted to identify the most predictive features in predicting tumor growth. Clinical information was also obtained from the database. RESULTS: Based on our definition, 26% of the cases were indolent lung cancer. The tumor growth pattern could be predicted by radiomic models constructed using features obtained in the lung window, the difference region, and by combining features obtained in both the lung window and difference regions with areas under the receiver operator characteristic (AUROCs) of 0.799, 0.819, and 0.846, respectively. The multi-window feature model showed better performance compared to single window features (P < 0.001). Incorporating clinical factors into the multi-window feature models showed improvement, yielding an accuracy of 84.67% and AUROC of 0.855 for distinguishing indolent from aggressive disease. CONCLUSIONS: Multi-window CT based radiomics features are valuable predictors of indolent lung cancers and out performed single CT window setting. Combining clinical information improved predicting performance.

Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas.

PURPOSE: The purpose of this study was to investigate the potential of computed tomography (CT) based radiomic features of primary tumors to predict pathological nodal involvement in clinically node-negative (N0) peripheral lung adenocarcinomas. METHODS: A total of 187 patients with clinical N0 peripheral lung adenocarcinomas who underwent preoperative CT scan and subsequently received systematic lymph node dissection were retrospectively reviewed. 219 quantitative 3D radiomic features of primary lung tumor were extracted; meanwhile, nine radiological semantic features were evaluated. Univariate and multivariate logistic regression analysis were used to explore the role of these features in predicting pathological nodal involvement. The areas under the ROC curves (AUCs) were compared between multivariate logistic regression models. RESULTS: A total of 153 patients had pathological N0 status and 34 had pathological lymph node metastasis. On univariate analysis, fissure attachment and 17 radiomic features were significantly associated with pathological nodal involvement. Multivariate analysis revealed that semantic features of pleural retraction (P = 0.048) and fissure attachment (P = 0.023) were significant predictors of pathological nodal involvement (AUC = 0.659); and the radiomic feature F185 (Histogram SD Layer 1) (P = 0.0001) was an independent prognostic factor of pathological nodal involvement (AUC = 0.73). A logistic regression model produced from combining radiomic feature and semantic feature showed the highest AUC of 0.758 (95% CI: 0.685-0.831), and the AUC value computed by fivefold cross-validation method was 0.737 (95% CI: 0.73-0.744). CONCLUSIONS: Features derived on primary lung tumor described by semantic and radiomic could provide information of pathological nodal involvement in clinical N0 peripheral lung adenocarcinomas.

Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.

BACKGROUND: In this study we retrospectively evaluated the capability of computed tomography (CT)-based radiomic features to predict epidermal growth factor receptor (EGFR) mutation status in surgically-resected peripheral lung adenocarcinomas in an Asian cohort of patients. PATIENTS AND METHODS: Two hundred ninety-eight patients with surgically resected peripheral lung adenocarcinomas were investigated in this institutional review board-approved retrospective study with requirement waived to obtain informed consent. Two hundred nineteen quantitative 3-D features were extracted from segmented volumes of each tumor, and 59 of these, which were considered independent features, were included in the analysis. Clinical and pathological information was obtained from the institutional database. RESULTS: Mutant EGFR was significantly associated with female sex (P = .0005); never smoker status (P < .0001), lepidic predominant adenocarcinomas (P = .017), and low or intermediate pathologic grade (P = .0002). Statistically significant differences were found in 11 radiomic features between EGFR mutant and wild type groups in univariate analysis. Mutant EGFR status could be predicted by a set of 5 radiomic features that fell into 3 broad groups: CT attenuation energy, tumor main direction, and texture defined according to wavelets and Laws (area under the curve [AUC], 0.647). A multiple logistic regression model showed that adding radiomic features to a clinical model resulted in a significant improvement of predicting power, because the AUC increased from 0.667 to 0.709 (P < .0001). CONCLUSION: Computed tomography-based radiomic features of peripheral lung adenocarcinomas can capture useful information regarding tumor phenotype, and the model we built can be useful to predict the presence of EGFR mutations in peripheral lung adenocarcinoma in Asian patients when mutational profiling is not available or possible.

The relationship between adult health and childhood maltreatment, as moderated by anger and ethnic background.

Childhood maltreatment, anger, and racial/ethnic background were examined in relation to physical health, psychological well-being, and blood pressure outcomes. This study used data from a diverse sample of African American, Latino, and Caucasian participants (N=198). Results from a series of multiple regressions indicated anger and total childhood maltreatment were robust predictors of poorer health. Although correlational analyses found maltreatment from the mother and father were associated with poorer health outcomes, when considered as part of the regression models, only a relationship between maltreatment from the mother and physical health was found. Greater anger scores were linked with lower blood pressure, particularly systolic blood pressure. Generally, more psychological and physical symptom reporting was found with greater anger scores, and higher levels of total maltreatment also predicted physical symptoms. The pattern of interactions indicated anger was more detrimental for African American participant's (and marginally so for Latino participant's) physical health. Interestingly, interactions also indicated total childhood maltreatment was related to fewer symptoms for Latino participants. Although child maltreatment may be viewed as a moral and/or human rights issue, this study provides evidence that it can also be viewed as a public health issue. Our study demonstrated that known health risk factors such as anger and maltreatment may operate in a different pattern dependent on ethnic/cultural background. The findings suggest health and health disparities research would benefit from greater exploration of the differential impact of certain moderating variables based on racial/ethnic background.