Other Cancers

Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.

Tumorigenesis and treatment are closely associated with various programmed cell death (PCD) patterns. However, the coregulatory role of multiple PCD patterns in colorectal cancer (CRC) remains unknown. In this study, we developed a multiple PCD index (MPCDI) based on 19 PCD patterns using two machine learning algorithms for risk stratification, prognostic prediction, construction of nomograms, imm...

A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.

Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and biased. Since the heavy manual annotation workload, most pathological whole slide image (WSI) datasets are only partially labeled (without region anno...

Quantitative Three-Dimensional Imaging Analysis of HfO Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale r...

Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable pr...

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in...

Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for bett...

Assessing the use of the novel tool Claude 3 in comparison to ChatGPT 4.0 as an artificial intelligence tool in the diagnosis and therapy of primary head and neck cancer cases.

Head and neck squamous cell carcinoma (HNSCC) is a complex malignancy that requires a multidisciplinary tumor board approach for individual treatment ...

Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in...

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets.

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-th...

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM)...

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer.

Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironm...

LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumo...

Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especi...

Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images.

RATIONALE AND OBJECTIVES: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and co...

Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images.

Deep learning shows promise in autosegmentation of head and neck cancer (HNC) primary tumours (GTV-T) and nodal metastases (GTV-N). However, errors su...

Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. ...

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.

BACKGROUND: This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately pre...

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

OBJECTIVES: Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of ...

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images.

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-bas...

Performance of AI for preoperative CT assessment of lung metastases: Retrospective analysis of 167 patients.

OBJECTIVES: To evaluate the performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT.