Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Data Augmentation is a crucial tool in the Machine Learning (ML) toolbox because it can extract novel, useful training images from an existing dataset, thereby improving accuracy and reducing overfitting in a Deep Neural Network (DNNs). However, clinical dermatology images often contain irrelevant background information,such as furniture and objects in the frame. DNNs make use of that information ...
Personalized medicine in cancer treatment aims to treat each individual's cancer tumor uniquely based on the genetic sequence of the cancer patient and is a much more effective approach compared to traditional methods which involve treating each type of cancer in the same, generic manner. However, personalized treatment requires the classification of cancer-related genes once profiled, which is a ...
While AI is widely used in biomedical research and medical practice, its use is constrained to few specific practical areas, e.g., radiomics. Particip...
BACKGROUND: Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymph...
OBJECTIVE: This study aimed to investigate whether lymphovascular invasion (LVI) was associated with oncological outcomes in patients with prostate ca...
Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of antican...
BACKGROUND: Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment ou...
BACKGROUND: The International Collaboration on Cancer Reporting proposes histological tumour type, lymphovascular invasion, tumour grade, perineural i...
Cancer vaccines have been increasingly studied and developed to prevent or treat various types of cancers. To systematically survey and analyze differ...
This study focused on a novel strategy that combines deep learning and radiomics to predict epidermal growth factor receptor (EGFR) mutations in patie...
Although novel techniques for avoiding incontinence during robot-assisted radical prostatectomy have been developed, long-term oncological outcomes ar...
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identi...
BACKGROUND: Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incide...
PURPOSE: No multi-center radiomics models have been built to predict delayed remission (DR) after transsphenoidal surgery (TSS) in Cushing's disease (...
It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for t...
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastati...
BACKGROUND: Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI...
OBJECTIVES: Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected to have better reproducibility than manual delineat...
BACKGROUND: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into trea...
INTRODUCTION: Medullary thyroid carcinoma (MTC) is a rare thyroid malignancy originating from parafollicular C cells. It accounts for 5%-10% of all th...