Latest AI and machine learning research in oncology/hematology for healthcare professionals.
BACKGROUND: Artificial intelligence (AI) is a promising tool used in oncology that may be able to facilitate diagnosis, treatment planning, and patient management. Transparency and completeness of protocols of randomized controlled trials (RCT) involving AI interventions is necessary to ensure reproducibility of AI tools across diverse clinical settings. The SPIRIT 2013 and SPIRIT-AI 2020 guidelin...
BACKGROUND: Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy.
Introduction: Operative management of spinal metastatic disease is largely for symptom palliation and revolves around the expectation that postoperati...
BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional...
BACKGROUND: Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovar...
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemother...
PURPOSE: The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast...
This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by ...
OBJECTIVE: To develop and validate machine learning (ML) models for diagnosing salivary gland adenoid cystic carcinoma (ACC) in the salivary glands ba...
Despite significant progress in air quality improvement, heavy fine particulate matter (PM) pollution events persist in China. The pollution character...
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases are crucial for guiding clinical decision-making and optimizing tre...
Early detection of lung cancer is crucial for improving survival rates because it allows for potentially curative treatments; however, most lung cance...
The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The ...
Molecular subtyping of pulmonary large-cell neuroendocrine carcinoma (LCNEC) based on retinoblastoma protein (pRb) expression may influence systemic t...
Recent advancements in digital pathology have enabled comprehensive analysis of Whole-Slide Images (WSI) from tissue samples, leveraging high-resolu...
In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharma...
The study by Huang , published in the , advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict te...
The study by Huang , published in the , advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict te...
The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public heal...
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the gene, typically diagnosed during early childhood and char...