Oncology/Hematology

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 challenge for modern low-cost missions. These small satellites often rely on commercial off-the-shelf (COTS) electronic components, which are particularly vulnerable to radiation-induced Single-Event Effects (SEEs) that can disrupt system operation and com...

A dynamic proteomic signature for the prediction of lung Cancer: a longitudinal analysis in the UK Biobank cohort.

BACKGROUND: To understand the molecularly obscure pre-diagnostic phase of lung cancer, we mapped the temporal evolution of the plasma proteome for new biological insights and improved risk prediction. METHODS: Leveraging the UK Biobank prospective cohort, we analyzed 2,921 plasma proteins from 37,759 participants, including 342 incident lung cancer cases identified over a median follow-up of 11.7 ...

Genetically engineered human cell-based microrobots for selective cancer cell death.

Medical microrobots have strong potential for targeted therapeutic delivery; however, current systems achieve only physical targeting, and once at the...

Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Microsatellite instability (MSI) has emerged as a key predictive biomarker for chemotherapy and immunotherapy response, and as a prognostic...

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...

Data-driven design of deterministic lateral displacement microfluidic devices for CTC separation.

Circulating tumor cells (CTCs) are established biomarkers for cancer diagnosis and therapeutic monitoring, yet their extreme rarity in peripheral bloo...

The Negative Impact of a Non-Therapeutic Laparotomy in Patients With Inoperable Colorectal Peritoneal Metastases.

BACKGROUND: The peritoneum is the third most prevalent location for metastases of colorectal cancer. In patients with resectable disease, cytoreductiv...

Evaluating the interpretation of neck CT/MRI scans by ChatGPT-4V for detecting primary oropharyngeal squamous cell carcinoma: An exploratory study.

OBJECTIVES: Early and accurate detection of head and neck squamous cell carcinoma and the subset of oropharyngeal squamous cell carcinoma (OPSCC) is e...

Deep Learning for Pretreatment Imaging-Based Tumor and Nodal Classification in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Network Meta-Analysis.

BACKGROUND: Accurate pretreatment assessment of the extent of tumor invasion and status of cervical lymph node metastasis is essential for staging and...

Application of a machine learning-based PANoptosis-immune-related gene risk score model in prognostic stratification and immunotherapy benefit prediction for glioblastoma.

This study developed a risk score model using PANoptosis and immune-related genes to predict glioblastoma (GBM) prognosis. Utilizing TCGA data and 66 ...

Investigating the toxicological impacts of thiabendazole exposure on uterine corpus endometrial carcinoma utilizing network toxicology, machine learning, and molecular docking.

Uterine corpus endometrial carcinomas (UCEC) are common cancers of the female reproductive system linked to environmental chemicals. Thiabendazole (TB...

Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas.

Pediatric brain tumors are rare and still represent the most common solid tumors in children and the leading cause of cancer-related mortality in the ...

Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial.

OBJECTIVES: Transarterial chemoembolization (TACE) is a promising locoregional therapy for unresectable colorectal liver metastases, but patient selec...

[Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].

BACKGROUND: Artificial intelligence (AI) is increasingly being implemented in digital pathology to support the tissue classification, cell detection, ...

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...

Clinical validation and implementation of mSTOP, a machine learning-based, longitudinal prediction model for the early identification of non-small cell lung cancer patients who not benefit from immune checkpoint inhibitor treatment.

OBJECTIVES: There has been a lot of interest in the field of laboratory medicine regarding the use of machine learning (ML)-based prediction models. T...

Random Survival Forest Versus Elastic-Net Regularized Cox Regression for Survival Prediction in Acute Myeloid Leukemia at Distinct Treatment Time Points: Model Performance Comparison Study.

BACKGROUND: Risk group stratification based on the prediction of survival of patients with acute myeloid leukemia (AML) is complex. Despite common ris...

Detection of Interpretable and Fine-Grained Brain Tumor Magnetic Resonance Imaging Based on Progressive Pruning: Machine Learning Model Development and Validation Study.

BACKGROUND: Brain tumor is one of the most malignant diseases of the central nervous system, and early accurate detection is of great significance for...

An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.

Early and accurate identification of brain tumors from magnetic resonance imaging (MRI) is essential for timely clinical intervention; however, manual...

An agentic framework for autonomous scientific discovery in cancer pathology.

Artificial intelligence has advanced cancer pathology, but many systems still depend on hand-crafted features, are hard to explain and rely on fragmen...