Oncology/Hematology

Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations.

Electronic health records (EHRs) contain rich documentation regarding disease symptoms and progressi...

Impact of obesity on surgical and oncologic outcomes in patients with endometrial cancer treated with a robotic approach.

AIM: The surgical treatment of endometrial cancer (EC) can be more complicated in obese patients. Ro...

Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams.

Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. I...

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.

An effective feature extraction method is key to improving the accuracy of a prediction model. From ...

A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

Deregulated splicing machinery components have shown to be associated with the development of severa...

Digital health innovation to integrate palliative care during the COVID-19 pandemic.

Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report.

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpr...

Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study.

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist betwee...

Deep computational pathology in breast cancer.

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world d...

Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study.

OBJECTIVE: Artificial intelligence (AI) could automatedly detect abnormalities in digital cytologica...

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography.

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain,...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model.

Machine learning (ML) algorithms permit the integration of different features into a model to perfor...

MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas.

Twelve to 66% of patients with clinically non-functioning pituitary adenoma (NFPA) experience tumor ...

Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.

Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resist...

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Path...

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is par...

The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.

PURPOSE: This study aimed to investigate the deep learning model (DLM) combining computed tomography...

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imagin...

Deep learning-based survival analysis for brain metastasis patients with the national cancer database.

PURPOSE: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used...

New convolutional neural network model for screening and diagnosis of mammograms.

Breast cancer is the most common cancer in women and poses a great threat to women's life and health...

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage.

BACKGROUND: Cancer is the second leading cause of death in the United States. Cancer screenings can ...