Lung Cancer

Integrative spatiotemporal transcriptomics identifies a liver metastasis-related initial cell population associated with the SEMA3A-NRP1 axis in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in which liver metastasis represents the principal determinant of poor prognosis. Although metastatic dissemination is thought to be driven by highly plastic tumor cells, the transcriptional features of liver metastasis-related initial cell (LMIC) and its spatial crosstalk with the metastatic microenvironment during ...

[Construction and validation of a prognostic prediction model for children-onset initial steroid-resistant nephrotic syndrome].

Objective: To analyze the risk factors for poor prognosis in children with steroid-resistant nephrotic syndrome (SRNS) and to construct and validate a prognostic model. Methods: A retrospective cohort study was conducted. Clinical data of 456 children with SRNS who were initially diagnosed and hospitalized at the Children's Hospital of Chongqing Medical University from January 2009 to December 202...

Ensembling Unets for Rare Chromosomal Aberration Detection in Metaphase Images, Uncertainty Quantification, and Ionizing Radiation Dose Estimation.

In biological dosimetry a radiation dose is estimated using the average number of chromosomal aberrations per peripheral blood lymphocytes. This analy...

Systematic Multi-Target QSAR Benchmarking: Machine Learning Algorithms, Molecular Descriptors, and Validation.

Quantitative structure-activity relationship (QSAR) modeling underpins computational drug discovery, yet the factors governing model generalizability ...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.

The purpose of the study is to investigate the potential of artificial intelligence (AI)-driven analysis of preoperative chest radiograph (CXR) for pr...

Abdominal Dual-Energy CT with Deep Learning Reconstruction: Preserving Image Quality and Hepatic Lesion Conspicuity with Reduced Radiation and Contrast.

The objective was to evaluate the image quality and hepatic lesion conspicuity in a dual-low-dose (radiation and contrast volume) upper abdominal dual...

The potential utility of in-silico approach in identifying phytochemicals against various targets for the management of lung cancer.

Research on novel treatment approaches is crucial for lung cancer, because it is one of the most common and aggressive malignancies with high mortalit...

Causal multi-fidelity surrogate forward and inverse models for ICF implosions.

Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input param...

Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.

This study aimed to identify essential genes driving lung adenocarcinoma (LUAD) progression by integrating CRISPR-Cas9 dependency data from the Cancer...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.

OBJECTIVES: To evaluate the potential of spectral detector computed tomography (SDCT) combined with intratumoral and peritumoral radiomics for noninva...

Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer.

The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need f...

DrugDL: Dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.

MOTIVATION: The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the det...

Multi-omics body-fluid biomarkers in immunoglobulin a nephropathy: Analytical Validation, standardization, and clinical chemistry translation of diagnostic and prognostic Analytes.

IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis worldwide. Although optimized supportive therapy is administered to these pati...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.

Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, ...

Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.

Accurate characterization of thoracic malignancies on computed tomography (CT) remains challenging because histological subtype differentiation and no...

Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.

Accurate prognostic models are essential for optimizing treatment strategies in gallbladder adenocarcinoma (GBAC). We preliminarily constructed and va...

Decoding Spectral Perturbations from a SERS Artificial Nose in Serum for Early Lung Cancer Detection.

Serum biomarkers for early cancer detection often suffer from limited sensitivity and specificity due to the biochemical complexity of blood. Here, we...