Oncology/Hematology

Artificial intelligence based malignant lymphoma type prediction using enhanced super resolution image and hybrid feature extraction algorithm.

In the medical field, the most common and frequent type of blood cancer is lymphoma. Accurately predicting and early response to lymphoma treatment will be useful for initiating treatment plans to achieve a greater rate of cure or reduced risk of treatment-related morbidity and death. Numerous research studies have revealed several pre-treatment predictive variables, including age, lactate dehydro...

Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans.

Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumeral (GH) joint functionality, requiring a targeted treatment. Various options are available to restore joint functions, ranging from conservative manag...

LUNETR: Language-Infused UNETR for precise pancreatic tumor segmentation in 3D medical image.

The identification of early micro-lesions and adjacent blood vessels in CT scans plays a pivotal role in the clinical diagnosis of pancreatic cancer, ...

Machine learning explainability for survival outcome in head and neck squamous cell carcinoma.

BACKGROUND: Diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC) induces psychological variables and treatment-related toxicity in...

From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction.

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therap...

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the dise...

RAPTOR-AI: An open-source AI powered radiation protection toolkit for radioisotopes.

Artificial intelligence (AI) has gained significant attention in various scientific fields due to its ability to process large datasets. In nuclear ra...

Extracellular vesicles as nature's nano carriers in cancer therapy: Insights toward preclinical studies and clinical applications.

Extracellular vesicles (EVs), which are secreted by various cell types, hold significant potential for cancer therapy. However, there are several chal...

Computationally Enabled Polychromatic Polarized Imaging Enables Mapping of Matrix Architectures that Promote Pancreatic Ductal Adenocarcinoma Dissemination.

Pancreatic ductal adenocarcinoma (PDA) is a highly metastatic and lethal disease. In PDA, extracellular matrix (ECM) architectures, known as tumor-ass...

Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However...

Hierarchical Characterization of Brain Dynamics via State Space-based Vector Quantization

Understanding brain dynamics through functional Magnetic Resonance Imaging (fMRI) remains a fundamental challenge in neuroscience, particularly in c...

Five-Gene Expression Formula Accurately Detects Hepatocellular Carcinoma Tumors

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Several diagnostic methods, such as imaging modaliti...

HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification

We propose HQCM-EBTC, a hybrid quantum-classical model for automated brain tumor classification using MRI images. Trained on a dataset of 7,576 scan...

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume

This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer dru...

Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes.

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and acc...

Large Language Model Agent for Modular Task Execution in Drug Discovery

We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational...

Devising a solution to the problems of Cancer awareness in Telangana

According to the data, the percent of women who underwent screening for cervical cancer, breast and oral cancer in Telangana in the year 2020 was 3....

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models

This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomal...

Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive Learning

Complete removal of cancer tumors with a negative specimen margin during lumpectomy is essential in reducing breast cancer recurrence. However, 2D s...

Quantum Variational Transformer Model for Enhanced Cancer Classification

Accurate prediction of cancer type and primary tumor site is critical for effective diagnosis, personalized treatment, and improved outcomes. Tradit...