A PROSPECTIVE STUDY OF COMMONLY UTILIZED REGIMENS OF VITAMIN D REPLACEMENT AND MAINTENANCE THERAPY IN ADULTS.

OBJECTIVE: To determine which vitamin D dose, formulation, and schedule most effectively and safely achieves a 25-hydroxyvitamin D (25[OH]D) level of >30 ng/mL (75 nmol/L). METHODS: In this prospective study, 100 subjects from the NY Harbor HCS Brooklyn Campus, ages 25 to 85 years, with 25(OH)D <30 ng/mL (<75 nmol/L), were randomized into four groups: cholecalciferol (D3) 2,000 international units (IU) daily; D3 3,000 IU daily; ergocalciferol (D2) 50,000 IU weekly; and D2 50,000 IU twice weekly. All were supplemented with 500 mg calcium carbonate daily. 25(OH)D, parathyroid hormone (PTH), urinary calcium, urinary creatinine, and other variables were measured during 7 visits over 12 months. RESULTS: All groups achieved a mean vitamin D level >30 ng/mL (>75 nmol/L) by visit 4 (5 months). Those receiving 50,000 IU D2 twice weekly displayed the most rapid and robust response, with 25(OH)D reaching >30 ng/mL (>75 nmol/L) after only 1 month and plateauing at 60 ng/mL (150 nmol/L) by 7 months. Although no statistically significant difference was seen in mean 25(OH)D levels between groups 1 through 3, subjects on 50,000 IU D2 weekly more consistently showed higher mean levels than either groups 1 or 2. No episodes of significant hypercalcemia occurred. There was a negative correlation in mean PTH levels and mean vitamin D levels in group 4 and all groups combined. CONCLUSION: All four schedules of vitamin D replacement were effective in safely achieving and maintaining 25(OH)D >30 ng/mL (>75 nmol/L). D2 50,000 IU twice weekly provided the most rapid attainment and highest mean levels of vitamin D. ABBREVIATIONS: 25(OH)D = 25-hydroxyvitamin D; BMI = body mass index; BUN = blood urea nitrogen; Ca/Cr = calcium/creatinine; D2 = ergocalciferol; D3 = cholecalciferol; IU = international units; PTH = parathyroid hormone.

Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.

Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis. Specifically, as a small, soft, and flexible abdominal organ, the pancreas demonstrates very high inter-patient anatomical variability in both its shape and volume. This inhibits traditional automated segmentation methods from achieving high accuracies, especially compared to the performance obtained for other organs, such as the liver, heart or kidneys. To fill this gap, we present an automated system from 3D computed tomography (CT) volumes that is based on a two-stage cascaded approach-pancreas localization and pancreas segmentation. For the first step, we localize the pancreas from the entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step. We introduce a fully deep-learning approach, based on an efficient application of holistically-nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views. The resulting HNN per-pixel probability maps are then fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall. We show that our introduced localizer compares favorably to both a conventional non-deep-learning method and a recent hybrid approach based on spatial aggregation of superpixels using random forest classification. The second, segmentation, phase operates within the computed bounding box and integrates semantic mid-level cues of deeply-learned organ interior and boundary maps, obtained by two additional and separate realizations of HNNs. By integrating these two mid-level cues, our method is capable of generating boundary-preserving pixel-wise class label maps that result in the final pancreas segmentation. Quantitative evaluation is performed on a publicly available dataset of 82 patient CT scans using 4-fold cross-validation (CV). We achieve a (mean  ±â€¯ std. dev.) Dice similarity coefficient (DSC) of 81.27 ±â€¯6.27% in validation, which significantly outperforms both a previous state-of-the art method and a preliminary version of this work that report DSCs of 71.80 ±â€¯10.70% and 78.01 ±â€¯8.20%, respectively, using the same dataset.

Feature fusion for lung nodule classification.

PURPOSE: This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning. METHODS: Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest. Support vector machines (SVMs) and k-nearest neighbor (kNN) classifiers in serial and two-tier cascade frameworks are optimized and analyzed for optimal classification results of nodules. RESULTS: A total of 1191 nodule and non-nodule samples from the Lung Image Data Consortium database is used for analysis. Classification using SVM and kNN classifiers is examined. The classification results from the two-tier cascade SVM using Gabor features showed overall better results for identifying non-nodules, malignant and benign nodules with average area under the receiver operating characteristics (AUC-ROC) curves of 0.99 and average f1-score of 0.975 over the two tiers. CONCLUSION: In the results, higher overall AUCs and f1-scores were obtained for the non-nodules cases using any of the three features, showing the greatest distinguishability over nodules (benign/malignant). SVM and kNN classifiers were used for benign, malignant and non-nodule classification, where Gabor proved to be the most effective of the features for classification. The cascaded framework showed the greatest distinguishability between benign and malignant nodules.

A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling.

Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative imaging analysis, pathology detection, and surgical assistance. For organs with high anatomical variability (e.g., the pancreas), previous segmentation approaches report low accuracies, compared with well-studied organs, such as the liver or heart. We present an automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method generates a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels. The system contains four steps: 1) decomposition of CT slice images into a set of disjoint boundary-preserving superpixels; 2) computation of pancreas class probability maps via dense patch labeling; 3) superpixel classification by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and 4) simple connectivity based post-processing. Dense image patch labeling is conducted using two methods: efficient random forest classification on image histogram, location and texture features; and more expensive (but more accurate) deep convolutional neural network classification, on larger image windows (i.e., with more spatial contexts). Over-segmented 2-D CT slices by the simple linear iterative clustering approach are adopted through model/parameter calibration and labeled at the superpixel level for positive (pancreas) or negative (non-pancreas or background) classes. The proposed method is evaluated on a data set of 80 manually segmented CT volumes, using six-fold cross-validation. Its performance equals or surpasses other state-of-the-art methods (evaluated by "leave-one-patient-out"), with a dice coefficient of 70.7% and Jaccard index of 57.9%. In addition, the computational efficiency has improved significantly, requiring a mere 6 ~ 8 min per testing case, versus ≥ 10 h for other methods. The segmentation framework using deep patch labeling confidences is also more numerically stable, as reflected in the smaller performance metric standard deviations. Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same data set. Under six-fold cross-validation, our bottom-up segmentation method significantly outperforms its MALF counterpart: 70.7±13.0% versus 52.51±20.84% in dice coefficients.

A novel approach for lung nodules segmentation in chest CT using level sets.

A new variational level set approach is proposed for lung nodule segmentation in lung CT scans. A general lung nodule shape model is proposed using implicit spaces as a signed distance function. The shape model is fused with the image intensity statistical information in a variational segmentation framework. The nodule shape model is mapped to the image domain by a global transformation that includes inhomogeneous scales, rotation, and translation parameters. A matching criteria between the shape model and the image implicit representations is employed to handle the alignment process. Transformation parameters evolve through gradient descent optimization to handle the shape alignment process and hence mark the boundaries of the nodule "head." The embedding process takes into consideration the image intensity as well as prior shape information. A nonparametric density estimation approach is employed to handle the statistical intensity representation of the nodule and background regions. The proposed technique does not depend on nodule type or location. Exhaustive experimental and validation results are demonstrated on 742 nodules obtained from four different CT lung databases, illustrating the robustness of the approach.

Shape Representation and Registration in Vector Implicit Spaces: Adopting a Closed-Form Solution in the Optimization Process.

In this paper, a novel method to solve the shape registration problem covering both global and local deformations is proposed. The vector distance function (VDF) is used to represent source and target shapes. The problem is formulated as an energy optimization process by matching the VDFs of the source and target shapes. The minimization process results in estimating the transformation parameters for the global and local deformation cases. Gradient descent optimization handles the computation of scaling, rotation, and translation matrices used to minimize the global differences between source and target shapes. Nonrigid deformations require a large number of parameters which make the use of the gradient descent minimization a very time-consuming process. We propose to compute the local deformation parameters using a closed-form solution as a linear system of equations derived from approximating an objective function. Extensive experimental validations and comparisons performed on generalized 2D shape data demonstrate the robustness and effectiveness of the method.

Hypopituitarism in the elderly.

Pituitary dysfunction in elderly can represent a true diagnostic and therapeutic challenge to clinicians caring for these patients. Symptoms associated with partial or total hypopituitarism, such as fatigue, lower muscle strength and decreased libido, are nonspecific and can be often attributed to normal aging. Gold standard pituitary diagnostic testing carries higher risks in elderly and is classically replaced by alternative testing. Furthermore, the benefits and safety of selective pituitary hormonal replacement, specifically sexual and growth hormone replacement, remain subject of controversy in this group of patients. Recognizing and appropriately treating hypopituitarism in elderly is crucial for the survival and well being of the older patients with this disease.

Modeling of the lung nodules for detection in LDCT scans.

A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.

Toward precise pulmonary nodule descriptors for nodule type classification.

A framework for nodule feature-based extraction is presented to classify lung nodules in low-dose CT slices (LDCT) into four categories: juxta, well-circumscribed, vascularized and pleural-tail, based on the extracted information. The Scale Invariant Feature Transform (SIFT) and an adaptation to Daugman's Iris Recognition algorithm are used for analysis. The SIFT descriptor results are projected to lower-dimensional subspaces using PCA and LDA. Complex Gabor wavelet nodule response obtained from an adopted Daugman Iris Recognition algorithm revealed improvements from the original Daugman binary iris code. This showed that binarized nodule responses (codes) are inadequate for classification since nodules lack texture concentration as seen in the iris, while the SIFT algorithm projected using PCA showed robustness and precision in classification.

Prevention of type 2 diabetes: an update.

With the rising tide of the diabetes epidemic leading to increased morbidity and mortality (primarily from cardiovascular disease), together with failure to control the disease and its associated complications, prevention of diabetes appears to be the logical option for curbing this epidemic. Several trials have been completed, and others ongoing, using various strategies for diabetes prevention. In this review, we provide an update on diabetes prevention strategies, highlighting the rationale behind such interventions, together with an outlook of the ongoing efforts that are likely to provide additional options for patients at risk for diabetes.

Bisphosphonates and osteonecrosis of the jaw: a retrospective study.

OBJECTIVE: To assess the prevalence of osteonecrosis of the jaw (ONJ) in patients receiving bisphosphonate therapy and in those who were bisphosphonate naïve. METHODS: We undertook a retrospective review of medical records of patients at the New York Harbor Health Care System from 1999 through 2004. Charts were selected for review if patients had a Current Procedural Terminology (CPT) code suggestive of ONJ or if they had ever received bisphosphonate therapy. RESULTS: Among 1,951 medical records reviewed, we identified 2 patients with ONJ who had received bisphosphonates and 2 patients with ONJ who were bisphosphonate naïve. Both patients treated with bisphosphonates had multiple myeloma and were receiving monthly infusions. They had initially received pamidronate before treatment was changed to zoledronic acid. In each case, ONJ was precipitated by a routine dental extraction. The prevalence of ONJ in our patient population receiving intravenously administered bisphosphonates was 1 in 71.5. Of the 2 cases of ONJ in bisphosphonate-naïve patients, osteoradionecrosis was clearly incriminated in 1 patient and potentially the causative factor in the other patient as well. No patients receiving orally administered bisphosphonates had ONJ, nor did this complication occur in any patients receiving parenteral bisphosphonate therapy for disorders such as osteoporosis or Paget's disease of bone. CONCLUSION: Bisphosphonates remain an important option for management of metabolic bone disease and complications of malignant disease. The overall prevalence of ONJ in patients receiving bisphosphonates seems to be very low; however, patients receiving intense parenteral therapy for an underlying malignant condition appear to have a uniquely elevated risk for the development of this complication. A causal relationship between bisphosphonates and ONJ remains to be proved and merits further investigation.

Control of blood pressure and other cardiovascular risk factors at different practice settings: outcomes of care provided to diabetic women compared to men.

Cardiovascular disease (CVD) is the major cause of morbidity and mortality in diabetes. To determine the proportion of patients who met the American Diabetes Association guidelines for control of CVD risk factors and to assess the achievement of these guidelines in women compared with men, we conducted a cross-sectional study of 3678 diabetic cohorts followed at seven medical centers, two Veteran Administration hospitals, three urban clinics, and two suburban clinics. Overall, 28% met the target blood pressure of <130/80 mm Hg, 48.8% achieved a goal low-density lipoprotein cholesterol of <100 mg/dL, and 35.8% had hemoglobin A1c of <7%. Gender comparisons of 2788 diabetic patients at urban and suburban centers showed that women had a lower percentage of low-density lipoprotein cholesterol <100 mg/dL (45.8 vs. 51.3, p<0.01) and a lower percentage of screening for retinopathy (54 vs. 60, p<0.01) and nephropathy (37 vs. 49, p<0.01). However, overall there were no gender differences in the percentage of patients who achieved a goal blood pressure <130/80 mm Hg or hemoglobin A1c <7%. Control of blood pressure and other CVD risk factors in diabetic patients was largely suboptimal, especially for diabetic women. These observations underscore the need for better strategies for control of CVD risk in the diabetic population in general, and women in particular.

Calcium antagonists in patients with type 2 diabetes and hypertension.

Hypertension is twice as common in patients with diabetes compared to those without diabetes. It accounts for up to 75% of cardiovascular disease risk leading to the substantial increase in morbidity and mortality. Control of blood pressure in people with diabetes has been shown in randomized controlled trials to decrease cardiovascular risk and improve outcome especially in preventing stroke. A target blood pressure goal of <130/80 mm Hg is currently recommended for patients with diabetes. However, less than 1/3 of these patients achieve such a goal. This is in part due to the inherent difficulty in controlling blood pressure in these patients where hypertension is usually associated with increased salt sensitivity, volume expansion and isolated systolic hypertension. Therefore, patients with diabetes usually require multiple medications for optimal blood pressure control. Calcium channel antagonists have been shown in large clinical trials to be both safe and effective in controlling blood pressure in diabetic patients and will continue to play a major role in the management of hypertension in this population, particularly in the combination therapy that these patients usually require.