Online ISSN: 2515-8260

Keywords : Lung cancer


The Role of Pharmacists in Optimizing Molecular Testing with Evolving Biomarkers and Treatment for Non-Small Cell Lung Cancer

Ashique Ahmed; Alakesh Bharali; Arindam Dutta; Abhinab Chetia; Arzoo Newar; Bedanta Bhattacharjee; Bhargab Deka; Bonti Sonowal; Bharjil Bingari; Dhunusmita Barman; Dibyojyoti Sarmah; Farida Pegu; Farak Ali; Gargi Das; Himangshu Sarma; Nikita Dey; Nayanika Neog; Pinkan Sadhukan; Rofiqul Islam; Richa Sonowal; Shahnaz Alom; Shamima Nasreen Ahmed; Taslima Akhter Rohman

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 2240-2259
DOI: 10.31838/ejmcm.08.03.203

Molecular testing and the development of targeted therapies have revolutionized the treatment of non-small cell lung cancer (NSCLC). Despite the advantages of molecular testing in patients with NSCLC and guideline recommendations, there is no specific standard testing method, resulting in variable testing practices based on institution protocol and access. Pharmacists can help to improve coordination of care around appropriate testing as results are important in determining the most appropriate targeted treatment course. The majority of patients with NSCLC are tested for PD-L1, EGFR, ALK, ROS1, and BRAF mutations. These biomarkers and their corresponding targeted therapies are more understood than the remaining biomarkers, such as KRAS, RET, MET exon 14 (METex14), and NTRK. Multiple new and emerging therapies target these latter biomarkers, and this article will focus on these lesser-known biomarkers. As the treatment of NSCLC becomes increasingly biomarker-driven and more therapies are added to the armamentarium for the management of NSCLC, pharmacists will be called upon to assist the oncology care team to optimize NSCLC treatment to improve patient outcomes.

DEEP LEARNING TECHNIQUES FOR LUNG CANCER SEGMENTATION USING MULTIPLE NEURAL NETWORKS

K. Priya; S. Ranjana; R. Manimegala

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1507-1514
DOI: 10.31838/ejmcm.07.09.162

Deep learning in pattern recognition and classification is considered a common and potent tool. There are, be that as it may, very few profoundly composed frameworks utilized in the field of clinical imaging conclusion, since there isn't constantly a wide store accessible for clinical pictures. In this investigation we tried the possibility of utilizing the Lung Image Database Consortium (LIDC) database cases to utilize profound learning calculations for lung disease conclusion. The knobs were portioned by the imprints given by the radiologists on each figured tomography cut. In the wake of inspecting and turning down we gained 174412 examples with 52 by 52 pixels each and the relating records of truth. Three profound learning calculations, including the Convolutionary Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Auto encoder (SDAE), have been created and executed. We built up a plan with 28 picture highlights and bolster vector machine to analyze the presentation of profound learning calculations with traditional PC helped determination (CADx) system. CNN, DBNs, and SDAE correctness’s are individually 0.7976, 0.8119, and 0.7929; our fabricated regular CADx precision is 0.7940 which is possibly lower than CNN and DBNs.

BLENDED KERNEL FUZZY LOCAL INFORMATION C-MEANS (BKFLICM) CLUSTERING BASED EDGE DETECTION FOR LUNG IMAGES

P. Dhanalakshmi; Dr. G. Satyavathy

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1920-1937

The medical diagnosis and clinical practice greatly demands medical image classification, an emerging area of research which includes modern medical imaging technology. Recently, Fuzzy Bat Algorithm (FBA) with Mean Weight Convolution Neural Network (MWCNN) algorithm was proposed for Region of Interest (RoI) area detection in the lung images in order to increase the classification accuracy. The image processing system outcomes are influenced by edge detection e.g. region segmentation, objects detection. Edge detection is done through Blended Kernel Based Fuzzy Local Information C-Means (BKFLICM) technique and construction of gradients in the scale is achieved by clustering of all image pixels in a feature space. The image segmentation mainly relies on the pixel intensity which is used for assessing resemblance amidst pixels. The edge detection using BKFLICM is performed by formation of new kernel range which is obtained by merging hyperbolic tangent kernel and Gaussian kernel. The special feature of BKFLICM is the fuzzy local (gray level) similarity measure through the kernel function. This does the edge detection perfectly while preserving the image details following which FBA and MWCNN classifier are utilized for segmentation and classification respectively. The training of lung image classification deprived of severe over-fitting is mainly done through MWCNN with sufficient labelled images and improved accuracy is also obtained for (LIDC-IDRI) database. The performance metrics such as accuracy, precision, recall, and F-measure values are also enhanced using the proposed algorithm which is validated by the experimental outcomes.

The Description Of Pre-Clinical Students’ Knowledge And Attitudes About The Dangers Of Smoking Against Lung Cancer

Moskwadina Gultom; B.R.Hertaty Siahaan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 4771-4782

This study discusses the description of pre-clinical students’ knowledge and attitudes about the dangers of smoking against lung cancer. It was done at Medical Faculty of Universitas Kristen Indonesia (UKI). It was a quantitative study with survey design. The respondents of this study were 83 students who were chosen using quota sampling. The instrument of this study were two sets of questionnaires with closed-ended questions. Based on the results of the study, it can be concluded that: a) of 83 respondents, 71 (85.5%) of respondents knew that smoking was harmful to health, the remaining 12 (14.5%) of respondents did not know that smoking was harmful to health. It can be concluded that most of the Pre-clinical Students Class 2016-2018 at the Medical Faculty of UKI have good knowledge about the dangers of smoking against lung cancer.

COMPARISON ON AUGUMENTED DIAGNOSTIC METHODS FOR EARLY LUNG TUMOR

J. , Vijayaraj; D. Loganathan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3417-3440

Lung cancer is also the most serious illness of the day for smokers. Small cell lung cancer
(SCLC) is the fatal type of lung cancer. This days, tumour detection is getting difficult. It is only
in the final stage that this form of lung cancer can be detected. Computer assisted identification
(CAD) and diagnosis mechanisms for lung cancer are an essential indicator of lung
segmentation, as the execution of those mechanisms is based on the execution of computed
tomography (CT) lung segmentation images. Image recognition system is commonly used for
early identification and treatment. Lung cancer prediction, hereditary cell identification and
environmental factors are essential factors in the development of lung cancer prevention
strategies. By predicting the movement of tumour cell it will be easier to control the tumour
spreading. This can be achieved to decrease the growth of the tumour cells using the motion
prediction model. So this paper contrasts the methods used at the earlier level to identify lung
tumors.

Zhengyuan capsule alleviates chemotherapy-related fatigue in nude mice with human lung adenocarcinoma A549 xenografts

Jieshan Guan; Lizhu Lin; Mingzi Ouyang

European Journal of Molecular & Clinical Medicine, 2019, Volume 6, Issue 1, Pages 3-11

Aim: We aimed to investigate the action mechanism of Zhengyuan capsule (a registered proprietary Chinese medicine) against chemotherapy-related fatigue (CRF). Methods: BALB/c-nu nude mice model with human lung adenocarcinoma A549 xenografts was constructed by injection of A549 cell suspension. The xenografted mice were randomly divided into model, cisplatin and cisplatin+Zhengyuan groups (n = 20 each). The cisplatin group was given an intraperitoneal injection of 5 mg/kg cisplatin every 3 days for 21 days. The cisplatin+Zhengyuan group was given an intragastric administration of cisplatin and 25 mg/kg Zhengyuan capsule each day for 21 days. Normal control and model groups were administrated with equal amount of saline. Forced swimming assay, tail suspension test, open field test, hepatic glycogen assay, blood analysis, and bone marrow smear was performed. Results: The cisplatin group developed CRF after receiving chemotherapy. When compared with cisplatin group, the cisplatin+Zhengyuan group exhibited longer exhaustive swimming time (p