Oncology/Hematology

Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a common cancer associated with elevated mortality rates. Exosomes, diminutive extracellular vesicles, significantly contribute to tumour development, immunological evasion, and treatment resistance. Identifying exosome-associated biomarkers in HNSCC may improve early diagnosis, treatment targeting, and patient classification.

XLLC-Net: A lightweight and explainable CNN for accurate lung cancer classification using histopathological images.

Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the accuracy and efficiency of disease classification. This study introduces the Explainable and Lightweight Lung Cancer Net (XLLC-Net), a streamlined convolutional neural network designed for classifying lung cancer from histopathological images. Using th...

Deep learning reconstruction of free-breathing, diffusion-weighted imaging of the liver: A comparison with conventional free-breathing acquisition.

This study aimed to compare image quality and solid focal liver lesion (FLL) assessments between free-breathing, diffusion-weighted imaging using deep...

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of ...

Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.

BACKGROUND: Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based ...

Deep learning approaches for quantitative and qualitative assessment of cervical vertebral maturation staging systems.

To investigate the potential of artificial intelligence (AI) in Cervical Vertebral Maturation (CVM) staging, we developed and compared AI-based qualit...

Breast cancer pathology image recognition based on convolutional neural network.

This study presents a convolutional neural network (CNN)-based method for the classification and recognition of breast cancer pathology images. It aim...

An efficient leukemia prediction method using machine learning and deep learning with selected features.

Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by ...

Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.

BACKGROUND: Prostate cancer is a common malignancy in men, and accurately distinguishing between benign and malignant nodules at an early stage is cru...

OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks.

OrthoNets use the Gram-Schmidt process to achieve orthogonality among filters but do not impose constraints on the internal orthogonality of individua...

Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10-Year Retrospective Study.

BACKGROUND: Bovine leukemia virus (BLV) infection in beef cattle has received less attention than in dairy herds, despite its potential impact on the ...

Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning.

BACKGROUND: Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variabil...

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-...

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicentre study.

OBJECTIVES: Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cel...

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

INTRODUCTION: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may...

Deep Learning Applied to Diffusion-weighted Imaging for Differentiating Malignant from Benign Breast Tumors without Lesion Segmentation.

Purpose To evaluate and compare the performance of different artificial intelligence (AI) models in differentiating between benign and malignant breas...

Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma Machine-learning Algorithm.

BACKGROUND: Necroptosis, a recently identified mechanism of programmed cell death, exerts significant influence on various aspects of cancer biology, ...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer imm...

Enhancing Privacy-Preserving Cancer Classification with Convolutional Neural Networks.

Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating prim...

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based s...