Usefulness of Positron Emission Tomography in the Examination of Hilar and Mediastinal Lymphadenopathies in Patients with Suspicion of Lung Cancer

Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.

Connection between PD-L1 expression and standardized uptake value in NSCLC: an early prognostic treatment combination

Objectives: Lung cancer is still diagnosed at advanced stage and early treatment initiation is needed. Therefore, we need biomarkers or clusters of information that can provide early treatment prognosis.Methods: Biopsies were acquired from 471 patients-lung masses with CT-guided biopsy, convex probe transthorasic biopsy, and EBUS-TBNA convex probe with 18 G needles and 19 G needles.Results: Standardized uptake value (SUV) measurement is associated with female, smoking status, hepatic metastasis, adenocarcinoma and programmed death-ligand 1 (PD-L1). In specific we expect that SUV ≥ 7 is associated with PD-L1 ≥ 50.Conclusions: Lung masses indifferent of size that have SUV ≥ 7 will also have PD-L1 expression ≥ 50. Also, it is likely that these patients will be female with intense smoking habit and hepar or multiple metastasis.