Histologic Findings of Sinusoidal Dilatation and Congestion in Liver Grafts Do Not Correlate with Hepatic Venous Anastomotic Gradients.

PURPOSE: Hepatic venous transplant anastomotic pressure gradient measurement and transjugular liver biopsy are commonly used in clinical decision-making in patients with suspected anastomotic hepatic venous outflow obstruction. This investigation aimed to determine if sinusoidal dilatation and congestion on histology are predictive of hepatic venous anastomotic outflow obstruction, and if it can help select patients for hepatic vein anastomosis stenting. MATERIALS AND METHODS: This is a single-center retrospective study of 166 transjugular liver biopsies in 139 patients obtained concurrently with transplant venous anastomotic pressure gradient measurement. Demographic characteristics, laboratory parameters, procedure and clinical data, and histology of time-zero allograft biopsies were analyzed. RESULTS: No relationship was found between transplant venous anastomotic pressure gradient and sinusoidal dilatation and congestion (P = 0.92). Logistic regression analysis for sinusoidal dilatation and congestion confirmed a significant relationship with reperfusion/preservation injury and/or necrosis of the allograft at time-zero biopsy (OR 6.6 [1.3-33.1], P = 0.02). CONCLUSION: There is no relationship between histologic sinusoidal dilatation and congestion and liver transplant hepatic vein anastomotic gradient. In this study group, sinusoidal dilatation and congestion is a nonspecific histopathologic finding that is not a reliable criterion to select patients for venous anastomosis stenting.

Can we screen opportunistically for low BMD using CT scans of the shoulder and artificial intelligence.

OBJECTIVE: To evaluate whether the computed tomography (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. MATERIALS AND METHODS: 194 patients aged 50 years or greater (69.2 +/- 9.1 years; 170 females) who had unenhanced shoulder CT scans and dual-energy x-ray absorptiometry (DXA) within 1 year of each other between January 1st, 2010 and December 31st, 2021 were evaluated. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs were obtained using 3D-Slicer. Support vector machines (SVM) and k-nearest neighbors (kNN) were used to predict low BMD. DeLong's test was used to compare the area under the curve (AUCs). RESULTS: A CT attenuation of 195.4 Hounsfield Units (HU) 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 = 0.003), but not better than the kNN model (P = 0.098). 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.

Radiation Segmentectomy for the Treatment of Hepatocellular Carcinoma: A Practical Review of Evidence.

Radiation segmentectomy is a versatile, safe, and effective ablative therapy for early-stage hepatocellular carcinoma. Advances in radiation segmentectomy patient selection, procedural technique, and dosimetry have positioned this modality as a curative-intent and guideline-supported treatment for patients with solitary HCC. This review describes key radiation segmentectomy concepts and summarizes the existing literary knowledgebase.

Analysis of lncRNAs in Lupinus mutabilis (Tarwi) and Their Potential Role in Drought Response.

Lupinus mutabilis is a legume with high agronomic potential and available transcriptomic data for which lncRNAs have not been studied. Therefore, our objective was to identify, characterize, and validate the drought-responsive lncRNAs in L. mutabilis. To achieve this, we used a multilevel approach based on lncRNA prediction, annotation, subcellular location, thermodynamic characterization, structural conservation, and validation. Thus, 590 lncRNAs were identified by at least two algorithms of lncRNA identification. Annotation with the PLncDB database showed 571 lncRNAs unique to tarwi and 19 lncRNAs with homology in 28 botanical families including Solanaceae (19), Fabaceae (17), Brassicaceae (17), Rutaceae (17), Rosaceae (16), and Malvaceae (16), among others. In total, 12 lncRNAs had homology in more than 40 species. A total of 67% of lncRNAs were located in the cytoplasm and 33% in exosomes. Thermodynamic characterization of S03 showed a stable secondary structure with -105.67 kcal/mol. This structure included three regions, with a multibranch loop containing a hairpin with a SECIS-like element. Evaluation of the structural conservation by CROSSalign revealed partial similarities between L. mutabilis (S03) and S. lycopersicum (Solyc04r022210.1). RT-PCR validation demonstrated that S03 was upregulated in a drought-tolerant accession of L. mutabilis. Finally, these results highlighted the importance of lncRNAs in tarwi improvement under drought conditions.

[Basilar dolichoectasia and fusiform vertebral artery aneurysm: ischemic stroke of dual cause] / Dolicoectasia basilar y aneurisma fusiforme de arteria vertebral: accidente cerebrovascular isquémico de causa dual.

A 76-year-old woman arrived at the emergency department due to an ischemic stroke that affected the territory of the left anterior choroidal and basilar arteries. Imaging studies showed dolichoectasia basilar associated with a fusiform aneurysm with thrombi inside it in the left vertebral artery. Both anatomic abnormalities are associated with ischemic stroke.

Mujer de 76 años que acudió al servicio de urgencias por un accidente cerebro vascular isquémico que afectó el territorio de arteria coroidea anterior izquierda y basilar. Los estudios de imagen mostraron dolicoectasia basilar asociada a un aneurisma fusiforme con trombos en su interior en la arteria vertebral izquierda. Las dos anomalías anatómicas están asociadas a accidente cerebrovascular isquémico.

Outcomes of Radiation Segmentectomy for Hepatocellular Carcinoma in Patients with Non-Alcoholic Fatty Liver Disease versus Chronic Viral Hepatitis.

Purpose: To compare the outcomes of radiation segmentectomy for early-stage hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD) versus hepatitis C virus (HCV). Materials and Methods: A retrospective analysis of consecutive patients with NAFLD- or HCV-related HCC treated with radiation segmentectomy from 01/2017-06/2022 was performed. Eligibility criteria included solitary tumor ≤8 cm or up to 3 HCC ≤3 cm, ECOG 0-1, and absence of vascular invasion or extrahepatic spread. Imaging best response was assessed per modified Response Evaluation Criteria in Solid Tumors. Target tumor and overall progression, time-to-progression (TTP), and overall survival (OS) were calculated. All outcomes were censored for liver transplantation (LT). Complete pathologic response (CPN) was assessed in patients who underwent LT. Results: Of 142 patients included (NAFLD: 61; HCV: 81), most had cirrhosis (NAFLD: 87%; HCV: 86%) and small tumors (median size NAFLD: 2.3 cm; HCV: 2.5 cm). Patients with NAFLD had higher BMI (p<0.001) and worse ALBI scores (p=0.003). Patients with HCV were younger (p<0.001) and had higher AFP levels (p=0.034). Median radiation dose (NAFLD: 508 Gy; HCV: 452 Gy) and specific activity (NAFLD: 700 Bq; HCV: 698 Bq) were similar between cohorts. Objective response was 100% and 97% in the NAFLD and HCV cohorts, respectively. Target tumor progression occurred in 1 (2%) NAFLD and 8 (10%) HCV patients. Target tumor TTP was not met for either cohort. Overall progression occurred in 23 (38%) NAFLD and 39 (48%) HCV patients. Overall TTP was 17.4 months (95% CI 13.5-22.2) in NAFLD and 13.5 months (95% CI 0.4-26.6) in HCV patients (p=0.86). LT was performed in 27 (44%) NAFLD and 33 (41%) HCV patients, with a CPN rate of 63% and 54%, respectively. OS was not met in the NAFLD cohort and was 53.9 months (95% CI 32.1-75.7) in the HCV cohort (p=0.15). Conclusion: Although NAFLD and HCV are associated with different mechanisms of liver injury, patients with early-stage HCC treated with radiation segmentectomy achieve comparable outcomes.

Effect of receiving a customizable brochure on breast cancer patients' knowledge about their diagnosis and treatment: A randomized clinical trial.

BACKGROUND: Patients' lack of knowledge about their own disease may function as a barrier to shared decision-making and well-being. This study aimed to evaluate the impact of written educational materials on breast cancer patients. METHODS: This multicenter, parallel, unblinded, randomized trial included Latin American women aged ≥18 years with a recent breast cancer diagnosis yet to start systemic therapy. Participants underwent randomization in a 1:1 ratio to receive a customizable or standard educational brochure. The primary objective was accurate identification of molecular subtype. Secondary objectives included identification of clinical stage, treatment options, participation in decision-making, perceived quality of information received, and illness uncertainty. Follow-up occurred at 7-21 and 30-51 days post-randomization. CLINICALTRIALS: gov identifier: NCT05798312. RESULTS: One hundred sixty-five breast cancer patients with a median age of 53 years and 61 days from diagnosis were included (customizable: 82; standard: 83). At first available assessment, 52%, 48%, and 30% identified their molecular subtype, disease stage, and guideline-endorsed systemic treatment strategy, respectively. Accurate molecular subtype and stage identification were similar between groups. Per multivariate analysis, customizable brochure recipients were more likely to identify their guideline-recommended treatment modalities (OR: 4.20,p = 0.001). There were no differences between groups in the perceived quality of information received or illness uncertainty. Customizable brochure recipients reported increased participation in decision-making (p = 0.042). CONCLUSIONS: Over one third of recently diagnosed breast cancer patients are incognizant of their disease characteristics and treatment options. This study demonstrates a need to improve patient education and shows that customizable educational materials increase patients' understanding of recommended systemic therapies according to individual breast cancer characteristics.

Detecting whether L1 or other lumbar levels would be excluded from DXA bone mineral density analysis during opportunistic CT screening for osteoporosis using machine learning.

PURPOSE: One or more vertebrae are sometimes excluded from dual-energy X-ray absorptiometry (DXA) analysis if the bone mineral density (BMD) T-score estimates are not consistent with the other lumbar vertebrae BMD T-score estimates. The goal of this study was to build a machine learning framework to identify which vertebrae would be excluded from DXA analysis based on the computed tomography (CT) attenuation of the vertebrae. METHODS: Retrospective review of 995 patients (69.0% female) aged 50 years or greater with CT scans of the abdomen/pelvis and DXA within 1 year of each other. Volumetric semi-automated segmentation of each vertebral body was performed using 3D-Slicer to obtain the CT attenuation of each vertebra. Radiomic features based on the CT attenuation of the lumbar vertebrae were created. The data were randomly split into training/validation (90%) and test datasets (10%). We used two multivariate machine learning models: a support vector machine (SVM) and a neural net (NN) to predict which vertebra(e) were excluded from DXA analysis. RESULTS: L1, L2, L3, and L4 were excluded from DXA in 8.7% (87/995), 9.9% (99/995), 32.3% (321/995), and 42.6% (424/995) patients, respectively. The SVM had a higher area under the curve (AUC = 0.803) than the NN (AUC = 0.589) for predicting whether L1 would be excluded from DXA analysis (P = 0.015) in the test dataset. The SVM was better than the NN for predicting whether L2 (AUC = 0.757 compared to AUC = 0.478), L3 (AUC = 0.699 compared to AUC = 0.555), or L4 (AUC = 0.751 compared to AUC = 0.639) were excluded from DXA analysis. CONCLUSIONS: Machine learning algorithms could be used to identify which lumbar vertebrae would be excluded from DXA analysis and should not be used for opportunistic CT screening analysis. The SVM was better than the NN for identifying which lumbar vertebra should not be used for opportunistic CT screening analysis.

Statistically based nomograms for the minimal needle length required to achieve intra-articular fluoroscopic-guided injections of the shoulder, hip, and knee.

BACKGROUND: Image-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. OBJECTIVE: To 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 DESIGN: Cross-sectional study. SETTING: Tertiary care academic center. PARTICIPANTS: 600 consecutive patients with available magnetic resonance imaging (MRI) of the hips, knees, and shoulders (100 males and 100 females for each joint). METHODS: The 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. RESULTS: The 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. CONCLUSIONS: Patient 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.

Utilizing machine learning for opportunistic screening for low BMD using CT scans of the cervical spine.

BACKGROUND: Computed Tomography (CT) scans of the cervical spine are often performed to evaluate patients for trauma and degenerative changes of the cervical spine. We hypothesized that the CT attenuation of the cervical vertebrae can be used to identify patients who should be screened for osteoporosis. METHODS: A retrospective study of 253 patients (177 training/validation and 76 test) with unenhanced CT scans of the cervical spine and Dual-energy x-ray Absorbtiometry (DXA) studies within 12 months of each other was performed. Volumetric segmentation of C1-T1, clivus, and first ribs was performed to obtain the CT attenuation of each bone. The correlations of the CT attenuations between the bones and with DXA measurements were evaluated. Univariate receiver operator characteristic (ROC) analyses, and multivariate classifiers (Random Forest (RF), XGBoost, Naïve Bayes (NB), and Support Vector Machines (SVM)) analyzing the CT attenuation of all bones, were utilized to predict patients with osteopenia/osteoporosis and femoral neck bone mineral density (BMD) T-scores <-1. RESULTS: There were positive correlations between the CT attenuation of each bone, and with the DXA measurements. A CT attenuation threshold of 305.2 Hounsfield Units (HU) at C3 had the highest accuracy (0.763, AUC=0.814) to detect femoral neck BMD T-scores ≤-1 and a CT attenuation threshold of 323.6 HU at C3 had the highest accuracy (0.774, AUC=0.843) to detect osteopenia/osteoporosis. The SVM classifier (AUC=0.756) had higher AUC than the RF (AUC=0.692, P=0.224), XGBoost (AUC=0.736; P=0.814), NB (AUC=0.622, P=0.133) and CT threshold of 305.2 HU at C3 (AUC=0.704, P=0.531) classifiers to identify patients with femoral neck BMD T-scores <-1. The SVM classifier (accuracy=0.816) was more accurate than using the CT threshold of 305.2 HU at C3 (accuracy=0.671) (McNemar's χ12=7.55, P=0.006). CONCLUSION: Opportunistic screening for low BMD can be done using cervical spine CT scans. A SVM classifier was more accurate than using the CT threshold of 305.2 HU at C3.

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

OBJECTIVES: To use multivariable machine learning using the computed tomography (CT) attenuation of each of the bones in the lumbar spine, pelvis, and sacrum, to predict osteoporosis/osteopenia. METHODS: This was a retrospective study of 394 patients aged 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. Volumetric segmentations 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 of the L1 vertebrae, 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. RESULTS: 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. CONCLUSION: A SVM model using the CT attenuations of multiple bones within the lumbar spine and pelvis and clinical data has a better ability to predict osteoporosis/osteopenia than using the CT attenuation of L1 or a LASSO model. KEY POINTS: • 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.

Nononcologic Embolization.

The scope of conditions managed by embolization, which was initially used for the treatment of hemorrhage and vascular malformations, is constantly expanding. Apart from oncologic indications, embolization is used to treat a wide range of benign pathology, including uterine fibroids and benign prostatic hyperplasia. While various particulate embolic agents are successfully used for benign embolization, there is growing evidence that unique properties of these may result in different outcomes. This article reviews available evidence comparing various particles used for uterine fibroid embolization and prostate artery embolization. In addition, we provide an overview of periprocedural pharmacology and protocols facilitating same-day discharge for these interventions.

Correction: Sebro, R.; De la Garza-Ramos, C. Machine Learning for Opportunistic Screening for Osteoporosis from CT Scans of the Wrist and Forearm. Diagnostics 2022, 12, 691.

In the original publication [...].

Association of intracranial abnormalities with the development of epilepsy and drug-resistant epilepsy in patients with Parry-Romberg syndrome.

BACKGROUND: Epilepsy represents an essential component of Parry Romberg syndrome (PRS). This study aimed to identify clinical factors that influence the development of epilepsy and drug-resistant epilepsy (DRE) in PRS. METHODS: We retrospectively reviewed the medical records of eighty patients with PRS. Data including the age of onset for PRS, history of seizures, use and timing of immunotherapy, antiseizure medication use, and EEG and brain imaging findings were reviewed. For comparison with the patients with epilepsy (PRSe+) group, we selected 18 age and sex-matched controls from the patient without epilepsy (PRSe-) cohort using propensity score matching. RESULTS: Eighteen (22.5%) had epilepsy: 12 were female, and the median age was 14.5 years (range = 6-48 years). Eleven patients developed DRE. The median latency between the onset of cutaneous manifestations and diagnosis and timing and use of immunotherapy was similar between the PRSe + and PRSe- groups. Intracranial abnormalities were commonly seen in the PRSe + group (16 vs. 2, p < 0.01). White matter disease and ipsilateral atrophy were common among the PRSe + group. Timing and use of immunotherapy, epileptiform discharges, and brain imaging abnormalities did not differ between those with DRE and without. CONCLUSIONS: The presence and degree of severity of ipsilateral brain abnormalities are risk factors for the development of epilepsy in PRS but not factors in predicting drug resistance. The timing of immunotherapy did not influence the development of PRSe + or DRE. Prospective studies are needed to identify biomarkers for epilepsy and assess the role of immunotherapy on seizure outcomes in PRSe + .

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.

Objectives: To use the computed tomography (CT) attenuation of the foot and ankle bones for opportunistic screening for osteoporosis. Methods: Retrospective 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. Results: CT 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. Conclusions: Opportunistic 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.

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

PURPOSE: To use machine learning and the CT attenuation of all bones visible on chest CT scans to predict osteopenia/osteoporosis. METHOD: We 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. RESULTS: There 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). CONCLUSIONS: The CT attenuation of the ribs, thoracic vertebra, sternum, and clavicle can be used individually and collectively to predict BMD and to predict osteopenia/osteoporosis.