Lung Cancer

Optimizing material composition determination in dual-energy computed tomography: a comparative study of a linear model and a fully connected neural network.

Accurate elemental decomposition in dual-energy computed tomography (DECT) is crucial for precision in radiation therapy planning. We present a comparative study of linear regression and fully connected neural networks (FCNNs) for voxel-wise prediction of tissue elemental composition, using synthetic datasets that incorporate realistic intra- and inter-patient variability. Both models performed we...

Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.

To assess how computed tomography (CT) image reconstruction techniques affect perceived diagnostic image quality at varying radiation dose levels in chest imaging. A PBU-50 anthropomorphic phantom (small adult-sized model) and an air-dried human lung specimen were scanned on the same CT system (Revolution Apex™, GE Healthcare) at six dose levels (CTDIvol) from 0.07 to 2.19 mGy for the smallest pha...

Loop of N2-polarized neutrophils and exhausted CD8 + T cells induces immunotherapy resistance in NSCLC.

While neutrophils represent a prominent myeloid component in non-small cell lung cancer (NSCLC), the specific immunosuppressive functions of N2-polari...

Functional-based multi-omics early prediction of radiation pneumonitis in NSCLC using AI-generated perfusion and ventilation from planning CT.

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and...

Lung cancer as a global health challenge: Multidimensional biomarker research and therapeutic advances.

Lung cancer, the leading cause of global cancer-related mortality, is categorized into small-cell and non-small-cell subtypes. The heterogeneous non-s...

Hulled Rice or husk? Synchrotron radiation XRF and deep learning approach for the determination of the geographical origin of Chinese rice samples.

Accurate authentication of rice geographical origin is crucial for food safety and fraud prevention. Synchrotron radiation X-ray fluorescence (SR-XRF)...

MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.

PURPOSE: We present MuTriM, a multimodal deep learning model integrating DCE-MRI and whole-slide pathology to predict survival and radiation benefit i...

Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a common type of lung cancer with poor prognosis and high mortality in advanced stages. Although sec...

AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.

Translating tumor epigenetic subtyping into methylome network-based prognostic models in early-stage NSCLC: results from the prospective MOBIT study.

Detection of Lung and Colon Cancer Using XRx-Net With Histopathological Images.

Lung and Colon Cancer (LCC) is a prevalent type of cancer influenced by factors such as genetics, smoking, second-hand smoke exposure, and alcohol con...

Downregulated lysyl oxidase in plasma extracellular vesicles: a biomarker linked to brain metastasis risk in lung adenocarcinoma.

BACKGROUND: Brain metastasis (BrM) is a leading cause of mortality in patients with lung adenocarcinoma (LUAD). Extracellular vesicles (EVs), which ca...

RAS signaling in lung adenocarcinoma is defined by lineage context and DUSP4 loss.

BACKGROUND: The molecular landscape of lung adenocarcinoma (LUAD) is often illustrated as a driver-oncogene "pie chart," but identical mutations exhib...

Multi-omics investigation of benzo[a]pyrene in gastric cancer: comprehensive network toxicology, machine learning and molecular docking approaches.

Gastric cancer (GC) risk is shaped by environmental exposures such as benzo[a]pyrene (BaP). Here, we systematically identified BaP-toxicological targe...

Mathematical fidelity vs. perceptual realism: A multi-center study of super-resolution radiomics for lung adenocarcinoma invasiveness.

OBJECTIVE: To identify the optimal super-resolution (SR) architecture for radiomics by comparing three models (Residual Channel Attention Network (RCA...

TRIM36 and CAMK2N2 regulate ferroptosis and antigen presentation in small cell lung cancer.

Small cell lung cancer (SCLC) is a highly aggressive tumor with poor prognosis. Ferroptosis is closely linked to tumor antigen presentation: it affect...

Assessment Tools of Radiation Skin Injury in Head and Neck Cancer: A Narrative Review of Preclinical and Clinical Studies.

Radiation skin injury (RSI) is an unavoidable side effect of radiotherapy that delays the treatment process and affects patients' quality of life. How...

A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.

Lung cancer is a leading cause of cancer-related mortality worldwide, and its early and accurate detection is critical for improving patient outcomes....

Pathology Public Datasets for Artificial Intelligence: A Systematic Review.

Histopathology plays a crucial role in the diagnosis of many diseases, especially cancers, for which the correct classification of tissue samples sign...

CT radiomics of adenocarcinoma of the esophagogastric junction: machine learning-based prediction of perineural invasion status.