Oncology/Hematology

Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI-Assisted vs Standard Methods in 3 Oncology Trials.

BACKGROUND: Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a means of overcoming bottlenecks and potential biases associated with standard patient prescreening processes in an oncology setting.

A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.

BACKGROUND: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, since high-risk patients need active intervention, while low-risk ones simply need to be follow-up. This study aimed at developing a personalized comp...

Machine and deep learning approaches for cancer drug repurposing.

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled ...

Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatm...

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.

OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocell...

Artificial Intelligence in Cancer Staging: Limitless Potential or Passing Fad?

FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms.

Early identification of metastatic or recurrent colorectal cancer (CRC) patients who will be sensitive to FOLFOX (5-FU, leucovorin and oxaliplatin) th...

UPLC-ESI-MS/MS based identification and antioxidant, antibacterial, cytotoxic activities of aqueous extracts from storey onion (Allium cepa L. var. proliferum Regel).

Storey onion (Allium cepa L. var. proliferum Regel) is a variety of onion commonly grown in northern China that has not been researched in detail. Thi...

Breast tumor classification through learning from noisy labeled ultrasound images.

PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samp...

Cardiac substructure segmentation with deep learning for improved cardiac sparing.

PURPOSE: Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation...

Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

BACKGROUND: The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual trea...

Predicting breast cancer risk using personal health data and machine learning models.

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works f...

Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review.

Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the ...

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

PURPOSE: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tra...

A mitochondria-targeting nitroreductase fluorescent probe with large Stokes shift and long-wavelength emission for imaging hypoxic status in tumor cells.

Development of a mitochondria-targeting fluorescent probe with large Stokes shift and long-wavelength emission was benefit for accurate detection of h...

Green synthesis and characterization of gold nanoparticles using endophytic fungi and its in-vitro anticancer and biomedical applications.

The present study aimed to explore the anticancer potentials of the gold nanoparticles (NPs) obtained by green synthesis method using an endophytic st...

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient res...

One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radi...

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detect...

Single-stage posterior total en bloc spondylectomy in the treatment of lumbar spinal metastases.

OBJECTIVE: To report the clinical and radiographic outcomes of single-stage posterior total en bloc spondylectomy (TES) of lumbar spinal metastases.