Oncology/Hematology

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T -weighted, postcontrast T -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-...

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high...

Multi-Responsive Bottlebrush-Like Unimolecules Self-Assembled Nano-Riceball for Synergistic Sono-Chemotherapy.

Improved drug loading content, bioavailability, and controlled release in targeted tissue have been major bottlenecks in the design of precision nanom...

ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells.

A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Employing ...

Use of artificial intelligence in computed tomography dose optimisation.

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT...

L-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification.

With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, d...

Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

BACKGROUND: Lung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligib...

Time-range based sequential mining for survival prediction in prostate cancer.

BACKGROUND AND OBJECTIVE: Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival o...

Mammographic image classification with deep fusion learning.

To better address the recognition of abnormalities among mammographic images, in this study we apply the deep fusion learning approach based on Pre-tr...

Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosomy 3 and mutation are strong prognostic factors pr...

Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity.

To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop methods to predict drug sensitivity ...

Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy.

Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (...

Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this mod...

Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses.

BACKGROUND AND AIMS: Detection and characterization of focal liver lesions (FLLs) is key for optimizing treatment for patients who may have a primary ...

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

BACKGROUND: Cell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-deriv...

Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater t...

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.

Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic reso...

Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the s...

Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.

PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive ...

A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM).

An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, nu...