De la Garza-Ramos, Cynthia

Among patients with treatment-naive hepatocellular carcinoma, glass microsphere radiation segmentectomy before liver transplant achieved high rates of complete pathologic necrosis, which was positively associated with the number of optimized treatment parameter thresholds.

Abstract Objective To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD. Methods In this study, we evaluated 194 patients aged 50 years or greater (69.2 ± 9.1 years; 170 females) who underwent unenhanced shoulder CT scans and dual-energy X-ray absorptiometry within 1 year of each other between January 1, 2010, and December 31, 2021. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs was obtained using 3D-Slicer. Support vector machines (SVMs) and k-nearest neighbours (kNN) were used to predict low BMD. DeLong test was used to compare the areas under the curve (AUCs). Results A CT attenuation of 195.4 Hounsfield Units of the clavicle had a sensitivity of 0.577, specificity of 0.781, and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00, and AUC of 0.857, while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = .003) but not better than the kNN model (P = .098). Conclusion The CT attenuation of the clavicle was best for predicting low BMD; however, a multivariable SVM was superior for predicting low BMD. Advances in knowledge SVM utilizing the CT attenuations at many sites was best for predicting low BMD.

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AbstractBackgroundImage‐guided intra‐articular injections are commonly performed to reduce pain in patients with arthritis or other joint‐related pathology. Utilizing a needle length that is too short could lead to increased patient discomfort, increased procedural time, and extra‐articular injections.ObjectiveTo predict the minimum needle length required for fluoroscopic‐guided intra‐articular injections of the hips, knees, and shoulders based on patient age, gender, height and weight, or body mass index (BMI) and to evaluate whether this varies by gender.Study DesignCross‐sectional study.SettingTertiary care academic center.Participants600 consecutive patients with available magnetic resonance imaging (MRI) of the hips, knees, and shoulders (100 males and 100 females for each joint).MethodsThe distance from the skin to the joint (glenohumeral, hip and knee) and the thickness of the subcutaneous fat pad (distance from the skin to the muscle) along the injection path were measured. Multivariable linear ridge regression with 10‐fold cross‐validation was used to predict the distance from the skin to the hip, knee, and glenohumeral joints using age, gender, weight, and height or using age, gender, and BMI.ResultsThe data show that the subcutaneous fat thickness and the distance from the skin to all joints increase with weight (p < .001) and BMI (p < .001). Subcutaneous fat pads around the anterior shoulder (p < .02) and knee (<.001) are thicker in women than in men.ConclusionsPatient habitus, in particular weight and BMI, are strong predictors of the thickness of the subcutaneous fat pads and consequently strong predictors of the distance from the skin to the joint. Subcutaneous fat pad thickness around the shoulders and knees varies by gender. Nomograms showing the minimal needle length required to achieve intra‐articular injections of the hip, knee and glenohumeral joints are presented.

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.

AbstractRadioembolization dosimetry for the treatment of hepatocellular carcinoma has evolved alongside our understanding of best practice for this therapy. At the core of advances in dosimetry are personalized and ablative applications of radioembolization, which have generated paradigm shifts in both safety and efficacy. This review provides a summary of fundamental radioembolization dosimetry concepts and narrates how our approach to treating patients has shifted from conventional to tailored and definitive therapy.