Lung Cancer

Modeling and forecasting oyster norovirus outbreaks of long-range dependence on antecedent marine environmental conditions.

This study presents three artificial intelligence-based models - XGBoost, Random Forest (RF), and Deep Artificial Neural Network (DANN) - with 2-day lead time for forecasting broad-scale oyster norovirus outbreaks. Among them, the XGBoost model performs best and is characterized by unique features: (1) the model was constructed and tested with epidemiological and environmental data collected from ...

CT radiomics machine learning for predicting treatment response in unresectable stage III-IV NSCLC: insights from dual chemo-immunotherapy and immunotherapy cohorts.

PURPOSE: The purpose of this study was to develop a machine learning model based on radiomic features extracted from baseline contrast-enhanced CT scans for predicting early treatment responses in two cohorts of patients with stage III-IV NSCLC treated with distinct therapeutic regimens (first-line chemo-immunotherapy or immunotherapy alone). METHODS: In this retrospective bicentric study includin...

KA-TMoE: A Deep Learning Method Using Time-Series CT Radiomics to Predict Post-Radiotherapy Rib Fractures in NSCLC Patients.

BACKGROUND AND PURPOSE: Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) un...

Prediction models for adverse events from prostate cancer curative radiotherapy: a systematic review of methodological quality.

BACKGROUND: Predicting risks of urinary, bowel, sexual, and other adverse effects following prostate cancer curative radiotherapy (PCa-RT) is essentia...

Interfractional Monitoring of Spacer Hydrogel Morphology in Patients With Prostate Cancer Receiving SBRT Treatment.

PURPOSE: The purpose of this study is to explore whether spacer hydrogel morphology changes during the course of stereotactic body radiation therapy (...

Local recurrence in rectal cancer: from detection to structured reporting.

Despite advances in total mesorectal excision and neoadjuvant therapy, locally recurrent rectal cancer remains a clinically important source of pelvic...

Artificial intelligence-powered evaluation of the pathologic response to preoperative therapy in non-small cell lung cancer.

INTRODUCTION: Quantitative assessment of the pathologic response (pR) in the eyeball approach remains challenging. Few studies have clearly defined vi...

Machine learning-derived clinical phenotypes of ascites in cirrhosis: A multi-center latent class analysis with external validation.

BACKGROUND: Ascites development in cirrhosis reduces five-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comp...

Machine learning-inspired design of a modified spider-shaped MIMO antenna for 5G-advanced wireless communications.

A modified Spider Shaped Four Element Multi Input Multi Output Antenna (SSFEMIMOA) is suggested for the 5G-advanced, and sub-6 GHz advanced wireless c...

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating im...

Radiation dose optimization and dosimetry in pediatric nuclear medicine: Current concepts and future directions.

Pediatric nuclear medicine plays an essential role in the diagnosis and treatment of a wide range of oncologic and non-oncologic diseases while requir...

Online adaptive radiotherapy in pelvic and thoracic cancers - comparing toxicities, clinical outcomes and technical parameters between conventional image-guided radiotherapy and online adaptive radiotherapy - the study protocol for the prospective, registry-based cohort study (PRoART).

BACKGROUND: Conventional image-guided radiotherapy (IGRT) typically relies on a computed tomography (CT)-based treatment planning process (planning CT...

High-performance MXene/CuNWs/MH-TPU piezoresistive strain sensors fabricated via template-assisted phase separation with integrated thermal-EMI dual protection for emergency rescue.

In recent years, complex and extreme fire environments have posed a severe threat to the safety of front-line firefighters, urgently requiring high-pe...

TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.

Immune checkpoint blockade (ICB) can produce durable responses in cancer, but reliable predictors of benefit are still lacking. CD8+ tumor-specific T ...

Spatially resolved tissue architecture and computational pathology in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease characterized by high levels of cellular heterogeneity and pronounced microenvironmental ...

Patient-Level Cross-validated nnU-Net for Multiclass Segmentation of Lung Parenchyma and Solid Adenocarcinoma on Thoracic CT.

Automated segmentation of lung parenchyma and solid lung adenocarcinoma on thoracic computed tomography (CT) is needed for reproducible quantitative i...

Integrative multi-omics reveals a prognostic and therapeutic landscape of synthetic lethality-associated signatures in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) exhibits considerable heterogeneity and therapeutic resistance. Here, we integrated single-cell RNA sequencing, spatial tra...

CHRNA5 orchestrates tumorigenesis and immune invasion through the CaMKII/AKT/NF-κB-CCL20 axis in pancreatic ductal adenocarcinoma.

AIMS: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by poor prognosis, extensive perineural invasion (PNI), ...

Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma.

The heterogeneity and complex tumor microenvironment of lung adenocarcinoma lead to poor prognosis. Autophagy, as a key cellular process, interacts wi...

DAFNet: A multi-modal deep learning model based on whole-lung CT for the automated prediction of the air space spread of lung adenocarcinoma.

Spread through air spaces (STAS) is a characteristic invasive pattern of lung adenocarcinoma (LUAD), which is associated with a high recurrence rate a...