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Purpose: To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. Materials and methods: The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs—maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. Results: The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. Conclusion: Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque. [ABSTRACT FROM AUTHOR]

Rationale and objectives: Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT. Materials and methods: This retrospective study included 4266 scans from 2030 asymptomatic patients who underwent two or more nonenhanced CT scans for colorectal screening between 2004 and 2016. The DL software identified and measured the volume, location, and attenuation of 883 stones. The corresponding scans were manually evaluated, and patients without follow-up were excluded. At each follow-up, the stones were categorized as new, growing, persistent, or resolved. Stone size (volume and diameter), attenuation, and location were correlated with the outcome and growth rates of the stones. Results: The stone cohort comprised 407 scans from 189 (M: 124, F: 65, median age: 55.4 years) patients. The median number of stones per scan was 1 (IQR: [1, 2]). The median stone volume was 17.1 mm3 (IQR: [7.4, 43.6]) and the median peak attenuation was 308 HU (IQR: [204, 532]. The 189 initial scans contained 291stones; 91 (31.3%) resolved, 142 (48.8%) grew, and 58 (19.9) remained persistent at the first follow-up. At the second follow-up (for 27 patients with 2 follow-ups), 14/44 (31.8%) stones had resolved, 19/44 (43.2%) grew and 11/44 (25%) were persistent. The median growth rate of growing stones was 3.3 mm3/year, IQR: [1.4,7.4]. Size and attenuation had a moderate correlation (Spearman rho 0.53, P <.001 for volume, and 0.50 P <.001 for peak attenuation) with the growth rate. Growing and persistent stones had significantly greater maximum axial diameter (2.7 vs 2.3 mm, P =.047) and peak attenuation (300 vs 258 HU, P =.031) Conclusion: We report a 12.7% prevalence of incidental kidney stones in asymptomatic adults, of which about half grew during follow-up with a median growth rate of about 3.3 mm3/year. [ABSTRACT FROM AUTHOR]

Purpose: Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. Materials and Methods: This retrospective study included 259 scans from 113 symptomatic patients being treated for urolithiasis at a single medical center between 2006 and 2019. These patients underwent a standard low-dose noncontrast CT scan followed by ultra-low-dose CT scans limited to the level of the kidneys. A DL model was used to detect, segment, and measure the volume of all stones in both initial and follow-up scans. The stone burden was characterized by the total volume of all stones in a scan (SV). The absolute and relative change of SV, (SVA and SVR, respectively) over serial scans were computed. The automated assessments were compared with manual assessments using concordance correlation coefficient (CCC), and their agreement was visualized using Bland–Altman and scatter plots. Results: Two hundred twenty-eight out of 233 scans with stones were identified by the automated pipeline; per-scan sensitivity was 97.8% (95% confidence interval [CI]: 96.0–99.7). The per-scan positive predictive value was 96.6% (95% CI: 94.4–98.8). The median SV, SVA, and SVR were 476.5 mm3, −10 mm3, and 0.89, respectively. After removing outliers outside the 5th and 95th percentiles, the CCC measuring agreement on SV, SVA, and SVR were 0.995 (0.992–0.996), 0.980 (0.972–0.986), and 0.915 (0.881–0.939), respectively Conclusions: The automated DL-based measurements showed good agreement with the manual assessments of the stone burden and its interval change on serial CT scans. [ABSTRACT FROM AUTHOR]

PURPOSE: Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS: Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance. RESULTS: A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B. CONCLUSION: A machine learning radiomics model may help differentiate SCLC from other lung lesions. [ABSTRACT FROM AUTHOR]

Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors that have metastatic potential. Management of patients with PPGLs mainly depends on the makeup of their genetic cluster: SDHx, VHL/EPAS1, kinase, and sporadic. CT is the preferred modality for precise localization of PPGLs, such that their metastatic progression can be assessed. However, the variable size, morphology, and appearance of these tumors in different anatomical regions can pose challenges for radiologists. Since radiologists must routinely track changes across patient visits, manual annotation of PPGLs is quite time-consuming and cumbersome to do across all axial slices in a CT volume. As such, PPGLs are only weakly annotated on axial slices by radiologists in the form of RECIST measurements. To ameliorate the manual effort spent by radiologists, we propose a method for the automated detection of PPGLs in CT via a proxy segmentation task. Weak 3D annotations (derived from 2D bounding boxes) were used to train both 2D and 3D nnUNet models to detect PPGLs via segmentation. We evaluated our approaches on an in-house dataset comprised of chest-abdomen-pelvis CTs of 255 patients with confirmed PPGLs. On a test set of 53 CT volumes, our 3D nnUNet model achieved a detection precision of 70% and sensitivity of 64.1%, and outperformed the 2D model that obtained a precision of 52.7% and sensitivity of 27.5% (p < 0.05). SDHx and sporadic genetic clusters achieved the highest precisions of 73.1% and 72.7% respectively. Our state-of-the art findings highlight the promising nature of the challenging task of automated PPGL detection. • We present the first approach to detect Pheochromocytomas and Paragangliomas (PPGLs), which are rare adrenal and extra-adrenal tumors. • Data from 255 patients who underwent CT imaging of the chest, abdomen, and pelvis was collected at the NIH. • 2D measurements of tumors in the volumes were converted into weak 3D bounding box annotations. • Two different nnUNet models were trained to detect PPGLs via a proxy segmentation task. • Our 3D model achieved a detection precision of 70% and sensitivity of 64.1%, and outperformed a 2D model (p < 0.05). [ABSTRACT FROM AUTHOR]