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Lung Cancer


Machine Learning

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The following information is listed on the source website. Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. This dataset contains information on peptides (annotated for their one-letter amino acid code) and their anticancer activity on breast and lung cancer cell lines. Two peptide datasets targeting breast and lung cancer cells were assembled and curated manually from CancerPPD. Linear and l-chiral peptides were retained, while cyclic, mixed or d-chiral peptides were discarded.


AI helps to reduce the risk of developing lung and cardiovascular diseases

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Lung cancer is one of the most common cancers worldwide. According to a study published in Nature called "Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography", researchers got to know that with the help of AI (artificial intelligence), lung cancer and cardiovascular health can be screened through the usage of low-dose computed tomography. This can help to reduce the risk of developing lung and cardiovascular diseases. The study was a result of a combined effort by Rensselaer Polytechnic Institute and Massachusetts General Hospital. Dr Colin Jacobs, Ph.D. assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen said "As it does not require manual interpretation of nodule imaging characteristics, the proposed algorithm may reduce the substantial interobserver variability in CT interpretation," .


Artificial Intelligence Detects Lung Cancer Risk

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According to the World Health Organization, there was an estimated 1.8 million deaths in 2020 from lung cancer. It is a leading cause of cancer death among men and women and each year, more people die of lung cancer than of colon, breast, and prostate cancers combined. However, the number of new lung cancer cases continues to decrease, due to fewer people smoking and advances in early detection and treatment. The latest advance in early lung cancer detection involves artificial intelligence (AI). Researchers from Radboud University Medical Center in Nijmegen, the Netherlands, and collaborators reported that an AI program accurately predicted the risk that lung nodules detected on screening CT will become cancerous.


Integrating medical imaging and cancer biology with deep neural networks

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Despite our remarkable advances in medicine and healthcare, the cure to cancer continues to elude us. On the bright side, we have made considerable progress in detecting several cancers in earlier stages, allowing doctors to provide treatments that increase long-term survival. The credit for this is due to "integrated diagnosis," an approach to patient care that combines molecular information and medical imaging data to diagnose the cancer type and, eventually, predict treatment outcomes. There are, however, several intricacies involved. The correlation of molecular patterns, such as gene expression and mutation, with image features (e.g., how a tumor appears in a CT scan), is commonly referred to as "radiogenomics."


Google AI tool can help patients identify skin conditions

BBC News

Dermatology Assist has not yet been given clearance by the Food and Drug Administration (FDA) for use in the US, but a similar machine-learning model built by British firm Optellum was recently approved by the FDA for use as an assistant in the diagnosis of lung cancer.


FDA greenlights Optellum's AI-powered software for early lung cancer diagnosis

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To read the full story, subscribe or sign in. The rapidly expanding field artificial intelligence (AI)-aided image analysis received a boost with the FDA 510(k) clearance for Optellum Ltd.'s Virtual Nodule Clinic, which helps clinicians evaluate small, potentially malignant lung lesions or nodules. The action makes Optellum's system the first cleared radiomic application for early lung cancer, an area of active research for the last five years.


A nomogram based on CT deep learning signature

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Xianyue Quan Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People's Republic of China Tel/Fax 86-2061643114 Email quanxianyue2014@163.com Purpose: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n 231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness.


Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps

arXiv.org Artificial Intelligence

Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based model to identify lung and colon cancer utilizing histopathological images with better augmentation techniques. In this paper, eight distinctive Pre-trained CNN models, VGG16, NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet, and DenseNet169 are trained on LC25000 dataset. The model performances are assessed on precision, recall, f1score, accuracy, and auroc score. The results exhibit that all eight models accomplished noteworthy results ranging from 96% to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture the attention images of Pre-trained CNN models classifying malignant and benign images.


Predicting Lung Cancer Survival With Deep Learning - Cancer Therapy Advisor

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A deep learning model successfully predicted the lung cancer survival period with an accuracy of 71.18%, outperforming previous machine learning models, according to the results of a study published in the International Journal of Medical Informatics. "Early detection and prediction of depth of survivability from cancer can help both patients and healthcare professionals better manage costs, treatment intensity and time spent around medical care," the authors wrote. The aim of this study was to characterize a deep learning approach to predict the survival period of patients with lung cancer. The study used data from the Surveillance, Epidemiology, and End Results (SEER) program. The deep learning models included data preprocessing using categorical and quantitative variables.


Expanding TNM for lung cancer through machine learning - Docwire News

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BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the conventional TNM for stratifying patients with lung cancer and evaluating survival. METHODS: Data were extracted from SEER. A total of 77 953 patients were analyzed using factors including primary tumor (T), regional lymph node (N), distant metastasis (M), age, and histology type. Ensemble algorithm for clustering cancer data (EACCD) and C-index were applied to generate prognostic groups and expand the current staging system.