Value Of Sentinel Lymph Node Biopsy In Papillary Thyroid Cancer: Initial Results Of A Prospective Trial.

The objectives of the study were to evaluate the performance of sentinel lymph node biopsy (SLNB) in detecting occult metastases in papillary thyroid carcinoma (PTC) and to correlate their presence to tumor and patient characteristics. Twenty-three clinically node-negative PTC patients (21 females, mean age 48.4 years) were prospectively enrolled. Patients were submitted to sentinel lymph node (SLN) lymphoscintigraphy prior to total thyroidectomy. Ultrasound-guided peritumoral injections of (99m)Tc-phytate (7.4 MBq) were performed. Cervical single-photon emission computed tomography and computed tomography (SPECT/CT) images were acquired 15 min after radiotracer injection and 2 h prior to surgery. Intra-operatively, SLNs were located with a gamma probe and removed along with non-SLNs located in the same neck compartment. Papillary thyroid carcinoma, SLNs and non-SLNs were submitted to histopathology analysis. Sentinel lymph nodes were located in levels: II in 34.7 % of patients; III in 26 %; IV in 30.4 %; V in 4.3 %; VI in 82.6 % and VII in 4.3 %. Metastases in the SLN were noted in seven patients (30.4 %), in non-SLN in three patients (13.1 %), and in the lateral compartments in 20 % of patients. There were significant associations between lymph node (LN) metastases and the presence of angio-lymphatic invasion (p = 0.04), extra-thyroid extension (p = 0.03) and tumor size (p = 0.003). No correlations were noted among LN metastases and patient age, gender, stimulated thyroglobulin levels, positive surgical margins, aggressive histology and multifocal lesions. Sentinel lymph node biopsy can detect occult metastases in PTC. The risk of a metastatic SLN was associated with extra-thyroid extension, larger tumors and angio-lymphatic invasion. This may help guide future neck dissection, patient surveillance and radioiodine therapy doses.

The Geometric Curvature Of The Spine Of Runners During Maximal Incremental Effort Test.

This study sought to analyse the behaviour of the average spinal posture using a novel investigative procedure in a maximal incremental effort test performed on a treadmill. Spine motion was collected via stereo-photogrammetric analysis in thirteen amateur athletes. At each time percentage of the gait cycle, the reconstructed spine points were projected onto the sagittal and frontal planes of the trunk. On each plane, a polynomial was fitted to the data, and the two-dimensional geometric curvature along the longitudinal axis of the trunk was calculated to quantify the geometric shape of the spine. The average posture presented at the gait cycle defined the spine Neutral Curve. This method enabled the lateral deviations, lordosis, and kyphosis of the spine to be quantified noninvasively and in detail. The similarity between each two volunteers was a maximum of 19% on the sagittal plane and 13% on the frontal (p<0.01). The data collected in this study can be considered preliminary evidence that there are subject-specific characteristics in spinal curvatures during running. Changes induced by increases in speed were not sufficient for the Neutral Curve to lose its individual characteristics, instead behaving like a postural signature. The data showed the descriptive capability of a new method to analyse spinal postures during locomotion; however, additional studies, and with larger sample sizes, are necessary for extracting more general information from this novel methodology.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Study Of The Homogeneity Of Drug Loaded In Polymeric Films Using Near-infrared Chemical Imaging And Split-plot Design.

Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.