AIMC Topic: Lung Neoplasms
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Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.
Nature communications
Jun 11, 2024
Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerfu...
Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning.
Omics : a journal of integrative biology
Jun 10, 2024
The traditional way of thinking about human diseases across clinical and narrow phenomics silos often masks the underlying shared molecular substrates across human diseases. One Health and planetary health fields particularly address such complexitie...
Artificial intelligence-assisted quantitative CT analysis of airway changes following SABR for central lung tumors.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jun 8, 2024
INTRODUCTION: Use of stereotactic ablative radiotherapy (SABR) for central lung tumors can result in up to a 35% incidence of late pulmonary toxicity. We evaluated an automated scoring method to quantify post-SABR bronchial changes by using artificia...
Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.
Scientific reports
Jun 7, 2024
Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncR...
Predicting Lymphovascular Invasion in Non-small Cell Lung Cancer Using Deep Convolutional Neural Networks on Preoperative Chest CT.
Academic radiology
Jun 6, 2024
RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images w...
A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.
BMC cancer
Jun 5, 2024
BACKGROUND: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associati...
Applying machine learning to construct an association model for lung cancer and environmental hormone high-risk factors and nursing assessment reconstruction.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
Jun 4, 2024
INTRODUCTION: To utilize machine learning techniques to develop an association model linking lung cancer and environmental hormones to enhance the understanding of potential lung cancer risk factors and refine current nursing assessments for lung can...
New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer.
Computers in biology and medicine
Jun 4, 2024
BACKGROUND: Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy. Since the advent of whole slide images (WSIs), the transition from traditional histopathology to di...
Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.
Genome medicine
Jun 4, 2024
BACKGROUND: Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic da...
Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.
Cancer immunology, immunotherapy : CII
Jun 4, 2024
BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunother...
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