Oncology/Hematology

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer.

Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in 'simulated' environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and evaluated a random forest algorithm for therapeutic curative-intent radiation therapy (RT) treatment planning for prostate cancer in a blinded, head-...

Knowledge-Powered Deep Breast Tumor Classification With Multiple Medical Reports.

Breast tumor classification with multiple medical reports such as B-ultrasound, Mammography (X-ray) and Nuclear Magnetic Resonance Imaging (MRI) is crucial to the intelligent cancer diagnosis system. Unlike the other domain texts, the medical reports have latent hierarchical syntactic structures and have hidden rich semantic information about the entities and relationships, which poses a great cha...

Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors.

Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based...

A Preliminary Study of Deep-Learning Algorithm for Analyzing Multiplex Immunofluorescence Biomarkers in Body Fluid Cytology Specimens.

INTRODUCTION: Multiplex biomarker analysis of cytological body fluid specimens is often used to assist cytologists in distiguishing metastatic cancer ...

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here...

An assessment of contamination pickup on ground robotic vehicles for nuclear surveying application.

Ground robotic vehicles are often deployed to inspect areas where radioactive floor contamination is a prominent risk. However, the accuracy of detect...

Clinical characteristics and oncological outcomes in negative multiparametric MRI patients undergoing robot-assisted radical prostatectomy.

BACKGROUND: Efforts are ongoing to try and find ways to reduce the number of unnecessary prostate biopsies without missing clinically significant pros...

The case for AI-driven cancer clinical trials - The efficacy arm in silico.

Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational metho...

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endange...

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.

PURPOSE: Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the...

The potential of AI in cancer care and research.

Current applications of artificial intelligence (AI), machine learning, and deep learning in cancer research and clinical care are highly diverse-from...

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, whic...

Data-driven approaches to advance research and clinical care for pediatric cancer.

Pediatric cancer is a rare disease with a distinct etiology and mutational landscape compared with adult cancer. Multi-omics profiling of retrospectiv...

Cell morphology-based machine learning models for human cell state classification.

Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively f...

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastas...

Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.

PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentati...

Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine lea...

5FU encapsulated polyglycerol sebacate nanoparticles as anti-cancer drug carriers.

The majority of anti-cancer drugs fail to reach clinical trials due to their low water solubility. A biocompatible drug delivery system that encapsula...

Improvement of Neoantigen Identification Through Convolution Neural Network.

Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer...

A review of AI and Data Science support for cancer management.

INTRODUCTION: Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting...