Oncology/Hematology

A Deep-Learning Framework for Ovarian Cancer Subtype Classification Using Whole Slide Images.

Ovarian cancer, a leading cause of cancer-related deaths among women, comprises distinct subtypes each requiring different treatment approaches. This paper presents a deep-learning framework for classifying ovarian cancer subtypes using Whole Slide Imaging (WSI). Our method contains three stages: image tiling, feature extraction, and multi-instance learning. Our approach is trained and validated o...

A Knowledge Graph for the Exploration of Human RSV Infection.

Despite decades of research and disease burden, RSV remains the most common cause of acute lower respiratory tract infection (ALRTI) in infants <2 years of age. New vaccines and an improved long lasting monoclonal antibody have been approved to protect individuals at increased risk of severe disease. However, treatment of severe RSV-mediated disease remains palliative, and we still cannot identify...

Deep learning MRI-based radiomic models for predicting recurrence in locally advanced nasopharyngeal carcinoma after neoadjuvant chemoradiotherapy: a multi-center study.

Local recurrence and distant metastasis were a common manifestation of locoregionally advanced nasopharyngeal carcinoma (LA-NPC) after neoadjuvant che...

A Deep Learning-Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records.

BACKGROUND: Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world P...

Distribution characteristics of volatile organic compounds and its multidimensional impact on ozone formation in arid regions based on machine learning algorithms.

Volatile Organic Compounds (VOCs) are key components of atmospheric pollution and play a critical role in ozone (O) formation. Understanding their dis...

Machine learning-assisted washing-free detection of extracellular vesicles by target recycling amplification based fluorescent aptasensor for accurate diagnosis of gastric cancer.

Extracellular vesicles (EVs) are promising non-invasive biomarkers for cancer diagnosis. EVs proteins play a critical role in tumor progress and metas...

Automated Detection of Clinical Entities in Lung and Breast Cancer Reports Using NLP Techniques

Research projects, including those focused on cancer, rely on the manual extraction of information from clinical reports. This process is time-consu...

VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction

Objective: Ulcerative colitis (UC), characterized by chronic inflammation with alternating remission-relapse cycles, requires precise histological h...

DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images

Lung cancer is a leading cause of cancer-related deaths globally, where early detection and accurate diagnosis are critical for improving survival r...

Machine-learning based classification of 2D-IR liquid biopsies enables stratification of melanoma relapse risk.

Non-linear laser spectroscopy methods such as two-dimensional infrared (2D-IR) produce large, information-rich datasets, while developments in laser t...

Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into palliative medicine, offering opportunities to improve quality, efficiency, a...

Research on the developments of artificial intelligence in radiomics for oncology over the past decade: a bibliometric and visualized analysis.

OBJECTIVE: To assess the publications' bibliographic features and look into how the advancement of artificial intelligence (AI) and its subfields in r...

Development of a prognostic model for breast cancer patients based on intratumoral tumor-infiltrating lymphocytes using machine learning algorithms.

BACKGROUND: Breast cancer remains a formidable global health challenge, with tumor-infiltrating lymphocytes (TILs) serving as pivotal biomarkers assoc...

Detection of Early-Stage Colorectal Cancer Using Cell-Free oncRNA Biomarkers and Artificial Intelligence.

PURPOSE: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide, and early detection significantly improves treatment ...

CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study.

PURPOSE: To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict patholog...

Prognostic value of circadian rhythm-associated genes in breast cancer.

OBJECTIVE: Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circad...

Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.

Malignant pleural effusions (MPEs) are common in advanced lung cancer patients. Cytological examination of pleural fluid is essential for identifying...

Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.

Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are cr...

SOHO State of the Art Updates and Next Questions | The Evolving Landscape of Prognostic Models in Chronic Lymphocytic Leukemia.

The advent of targeted therapies has profoundly altered the prognostic landscape of chronic lymphocytic leukemia (CLL), demanding a reassessment of es...

Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.

Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperative to focus on strategies that can mitigate its i...