Lung Cancer

Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.

BACKGROUND: Deep learning methods have made great progress in the automatic segmentation of nasopharyngeal carcinoma, but challenges remain. PURPOSE: Computer-aided automatic segmentation of nasopharyngeal cancer primary area is of great significance for automatic outlining of nasopharyngeal cancer target areas and accurate prediction of responsiveness and prognosis of metastatic lymph nodes in th...

Dual-band graphene-based THz MIMO antenna for 6G-enabled biomedical application using supervised regression machine learning validation.

The exponential increase in wireless data traffic and the growing demand for biomedical sensing have driven the advancement of sophisticated antenna technologies, particularly within the terahertz (THz) frequency range. This research presents an innovative graphene-based microstrip patch antenna featuring a slotted design and MIMO configuration, specifically designed for the high-speed needs of 6G...

Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications.

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite rec...

A transformer-based patient-agnostic tumor motion tracking model (TransTracking): A feasibility study in radiotherapy for lung cancer.

BACKGROUND: Accurate and real-time localization of thoracic tumor targets is essential for effective radiation therapy. Recently, Transformer architec...

Cross-attention multimodal fusion for EGFR mutation prediction: A predictive model using CT and whole-slide images.

BACKGROUND: The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy....

Deep Learning-Based Augmented Contrast-Enhancement and Denoising for Reduced-Iodine and Low-Radiation 70-kVp Cerebral CT Angiography: A Prospective Study.

OBJECTIVE: To evaluate the feasibility of cerebral computed tomography angiography (CTA) obtained with reduced iodine and low radiation at 70 kVp and ...

A deep learning lung cancer segmentation pipeline to facilitate CT-based radiomics.

AIM: CT-based radio-biomarkers could provide non-invasive insights into tumour biology to risk-stratify patients. One of the limitations is the labori...

CBFI: A multi-layer gene network key node identification algorithm integrating structural-biological features and dynamic regulatory weights.

OBJECTIVE: Identifying key nodes within multi-layer GRNs is crucial for uncovering potential biomarkers and therapeutic targets. Key nodes exhibit bot...

Machine learning-enhanced plasma proteomics discriminates pancreatic cancer-associated diabetes from type 2 diabetes mellitus.

Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type...

Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.

Lung cancer persists as the predominant oncological cause of mortality globally, underscoring an imperative public health issue that demands effective...

Early ctDNA stratifies survival in locally advanced and oligometastatic lung cancer treated with radiotherapy.

PURPOSE: Circulating tumor fraction estimate (ctFE) is a machine learning-derived composite metric of circulating tumor DNA (ctDNA) burden. We hypothe...

Deep-learning based quantitative evaluation of postoperative atelectasis following right upper lobectomy.

Existing methods of grading atelectasis are typically subjective and not scalable. We aimed to develop an automated, deep learning-based framework to ...

Peripheral blood mononuclear cell DNA methylation signatures guide surgical decision-making in indeterminate pulmonary nodules.

BACKGROUND: Distinguishing malignant from benign pulmonary nodules remained a significant clinical challenge. Given the involvement of DNA methylation...

Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy.

BACKGROUND: Macrophage polarization and endoplasmic reticulum (ER) stress play critical yet incompletely understood roles in cancer progression and th...

Integration of Single-Cell RNA Sequencing and Machine Learning to Identify and Validate Prognostic Genes With Lymph Node Metastasis and Immune Cell Signatures in Lung Adenocarcinoma.

The poor prognosis of lung adenocarcinoma (LUAD) remains unimproved. This study aimed to identify lymph node metastasis (LNM)-related and cellular imm...

Predicting survival of Hodgkin lymphoma using machine learning-an analysis based on the SEER database.

OBJECTIVES: This study aimed to develop an effective model for predicting Hodgkin lymphoma (HL) prognosis as to assist clinicians in making optimal cl...

Revolutionizing nanosatellites' data integrity with SEEnet: A real-time ensemble learning approach for Single-Event Effect (SEE) prediction.

As nanosatellites make access to space more affordable and widespread, protecting onboard data from radiation-related damage has become a major challe...

In-hospital electronic monitoring system approaches to epidemiologic investigation and predictive modeling of contrast-induced acute kidney injury.

Contrast-induced acute kidney injury (CI-AKI), the third most common cause of hospital-acquired kidney injury, is associated with poor clinical outcom...

Artificial intelligence-assisted risk prediction of postoperative pulmonary complications in non-small cell lung cancer surgery.

BACKGROUND: Pulmonary complications are the most frequent adverse events following surgery for non-small cell lung cancer (NSCLC), influencing both sh...

Multimodal AI approach combining deep learning imaging and clinical machine learning for pancreatic cancer detection.

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of...