Value of Spinal Infection Treatment Evaluation Score, Pola Classification, and Brighton Spondylodiscitis Score from Decision to Surgery in Patients with Spondylodiscitis: A Receiver-Operating Characteristic Curve Analysis.

STUDY DESIGN: This was a retrospective study. PURPOSE: This study aimed to assess the value of the Spinal Infection Treatment Evaluation (SITE) score, Brighton Spondylodiscitis Score (BSDS), and Pola classification to predict the need for surgical intervention in patients with spondylodiscitis. OVERVIEW OF LITERATURE: Spondylodiscitis is a rare disease, and the prediction of its outcome is crucial in the decision-making process. METHODS: All case records were assessed to extract information on the American Spinal Injury Association (ASIA), Visual Analog Scale (VAS), and Japanese Orthopedic Association Back Pain Evaluation Questionnaire (JOABPEQ) scores before and after surgery. The SITE score, Pola classification, and BSDS were recorded. The receiver-operating characteristic (ROC) curve analysis and the area under the curve (AUC) were applied to estimate the predictive ability of the scoring systems. Patients' satisfaction with surgery outcomes was evaluated using the VAS, ASIA, JOABPEQ, and Likert scale for quality-of-life evaluation. RESULTS: In all 148 patients, case records were reviewed. The mean±standard deviation age of the patients was 54.6±14.7 years. Of these, 112 patients underwent surgery. The AUC scores were 0.86, 0.81, and 0.73 for the SITE score, BSDS, and Pola classification, respectively. In the comparison of the AUC of ROC curves, SITE score vs. BSDS showed a significantly greater AUC, 0.13 (Z =2.1, p =0.037); SITE score vs. Pola classification, 0.05 (Z =0.82, p =0.412); and Pola classification vs. BSDS, 0.08 (Z =1.22, p =0.219). The optimal cutoff score was 8.5 (sensitivity, 80.6%; specificity, 81.2%) for the SITE score and 9.5 (sensitivity, 52.8%; specificity, 83.0%) for the BSDS in the decision to surgery. VAS back pain and JOABPEQ subscales showed a significant difference when compared with preoperative scores. According to ASIA grading, none of the patients experienced neurological deterioration. Overall, patients' satisfaction was observed. CONCLUSIONS: The findings suggest that the SITE score is a useful measure and helps clinicians make clinically sound decisions in patients with spondylodiscitis.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Investigation of gene expressions in differentiated cell derived bone marrow stem cells during bone morphogenetic protein-4 treatments with Fourier transform infrared spectroscopy.

A model was set up to predict the differentiation patterns based on the data extracted from FTIR spectroscopy. For this reason, bone marrow stem cells (BMSCs) were differentiated to primordial germ cells (PGCs). Changes in cellular macromolecules in the time of 0, 24, 48, 72, and 96h of differentiation, as different steps of the differentiation procedure were investigated by using FTIR spectroscopy. Also, the expression of pluripotency (Oct-4, Nanog and c-Myc) and specific genes (Mvh, Stella and Fragilis) were investigated by real-time PCR. However, the expression of genes in five steps of differentiation was predicted by FTIR spectroscopy. FTIR spectra showed changes in the template of band intensities at different differentiation steps. There are increasing changes in the stepwise differentiation procedure for the ratio area of CH2, which is symmetric to CH2 asymmetric stretching. An ensemble of expert methods, including regression tree (RT), boosting algorithm (BA), and generalized regression neural network (GRNN), was the best method to predict the gene expression by FTIR spectroscopy. In conclusion, the model was able to distinguish the pattern of different steps from cell differentiation by using some useful features extracted from FTIR spectra.

Supervised segmentation of MRI brain images using combination of multiple classifiers.

Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of combination of multiple classifiers (CMCs) methods is proposed. The proposed method uses dynamic local training sets instead of a full statics one and also it change the classifier rank criterion properly for brain tissue classification. Selection policy for combining the different decisions is implemented here and the K-nearest neighbor algorithm is used to find the best local classifier. Experimental results show that the proposed method can classify the real datasets of the internet brain segmentation repository better than all single classifiers in ensemble and produces significantly improvement on other CMCs methods.

Effectiveness of lumbar drain versus hyperventilation to facilitate transsphenoidal pituitary (suprasellar) adenoma resection.

BACKGROUND: Developing controlled hypercarbia is a known scheme of lowering the suprasellar part of the adenoma in order to assist the surgeon, which acts through raising the ICP and therefore the CSF pressure. OBJECTIVES: The purpose of this study is to compare the effect of introducing a lumbar drain with that of controlled hypercapnia on the quality of transsphenoidal pituitary tumor resection and CSF leak. PATIENTS AND METHODS: Fifty two patients with pituitary adenoma who underwent transsphenoidal hypophysectomy by the same surgeon were included. They were randomly divided into two groups. A lumbar drain catheter introduced into the L3-L4 subarachnoid space under local anesthesia in all patients. The same anesthesia was performed in both groups. In the study group, we used a saline injection into the subarachnoid space versus hypoventilation in the control group in order to increase the ICP according to the surgeon's request. The surgeon's satisfaction during the tumor resection and the resection time were assessed during the surgery. The CSF catheter was closed and sent with the patient for CSF drainage. If there was no CSF leak, the catheter removed 24 hours later. With evidence of a CSF leak, we used the catheter as a lumbar drain. The time taken for the leakage control was assessed. RESULTS: The satisfaction came from 21 (87.5%) and 2 (9.1%) for surgeon in the first and the second group respectively (P = 0.0001). CSF leakage time in the first and the second group was 1.6 ± 0.24 and 5 ± 0.50 respectively. It revealed a significant difference between the two groups (P = 0.001). The mean resection time was 13.54 ± 0.66 minutes in the study group; and 30.91 ± 0.98 minutes in the control group. CONCLUSIONS: In summary, the method described here for ICP manipulation is an effective procedure for a better visualization of the pituitary tumor during transphenoidal resection by surgeon and beneficial in managing the CSF leak following surgery.