Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients.

Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.

Real-time liver uptake and biodistribution of magnetic nanoparticles determined by AC biosusceptometry.

We describe the development of a joint in vivo/ex vivo protocol to monitor magnetic nanoparticles in animal models. Alternating current biosusceptometry (ACB) enables the assessment of magnetic nanoparticle accumulation, followed by quantitative analysis of concentrations in organs of interest. We present a study of real-time liver accumulation, followed by the assessment of sequential biodistribution using the same technique. For quantification, we validated our results by comparing all of the data with electron spin resonance (ESR). The ACB had viable temporal resolution and accuracy to differentiate temporal parameters of liver accumulation, caused by vasculature extravasation and macrophages action. The biodistribution experiment showed different uptake profiles for different doses and injection protocols. Comparisons with the ESR system indicated a correlation index of 0.993. We present the ACB system as an accessible and versatile tool to monitor magnetic nanoparticles, allowing in vivo and real-time evaluations of distribution and quantitative assessments of particle concentrations.

Quantification of Pulmonary Inflammatory Processes Using Chest Radiography: Tuberculosis as the Motivating Application.

The purpose of this work was to develop a quantitative method for evaluating the pulmonary inflammatory process (PIP) through the computational analysis of chest radiography exams in posteroanterior (PA) and lateral views. The quantification procedure was applied to patients with tuberculosis (TB) as the motivating application.A study of high-resolution computed tomography (HRCT) examinations of patients with TB was developed to establish a relation between the inflammatory process and the signal difference-to-noise ratio (SDNR) measured in the PA projection. A phantom essay was used to validate this relation, which was implemented using an algorithm that is able to estimate the volume of the inflammatory region based solely on SDNR values in the chest radiographs of patients.The PIP volumes that were quantified for 30 patients with TB were used for comparisons with direct HRCT analysis for the same patient. The Bland-Altman statistical analyses showed no significant differences between the 2 quantification methods. The linear regression line had a correlation coefficient of R = 0.97 and P < 0.001, showing a strong association between the volume that was determined by our evaluation method and the results obtained by direct HRCT scan analysis.Since the diagnosis and follow-up of patients with TB is commonly performed using X-rays exams, the method developed herein can be considered an adequate tool for quantifying the PIP with a lower patient radiation dose and lower institutional cost. Although we used patients with TB for the application of the method, this method may be used for other pulmonary diseases characterized by a PIP.

Objective CT-based quantification of lung sequelae in treated patients with paracoccidioidomycosis.

This study presents methodology for objectively quantifying the pulmonary region affected by emphysemic and fibrotic sequelae in treated patients with paracoccidioidomycosis. This methodology may also be applied to any other disease that results in these sequelae in the lungs.Pulmonary high-resolution computed tomography examinations of 30 treated paracoccidioidomycosis patients were used in the study. The distribution of voxel attenuation coefficients was analyzed to determine the percentage of lung volume that consisted of emphysemic, fibrotic, and normal tissue. Algorithm outputs were compared with subjective evaluations by radiologists using a scale that is currently used for clinical diagnosis.Affected regions in the patient images were determined by computational analysis and compared with estimates by radiologists, revealing mean (± standard deviation) differences in the scores for fibrotic and emphysemic regions of 0.1% ±â€Š1.2% and -0.2% ±â€Š1.0%, respectively.The computational results showed a strong correlation with the radiologist estimates, but the computation results were more reproducible, objective, and reliable.