Oncology/Hematology

Convolutional neural network is a good technique for sleep staging based on HRV: A comparative analysis.

The fluctuation of heart rate is regulated by autonomic nervous system. In human sleep, the autonomic nervous system plays a leading role. Therefore, we can use heart-rate variability (HRV) to stage the sleep process. Based on two independent public datasets, we construct three end-to-end automatic sleep staging models: fully connected neural networks (FCN), convolutional neural networks (CNN) and...

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans.

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.

BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (...

Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment.

The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterog...

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learnin...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosi...

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic a...

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treat...

Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.

This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architectu...

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images w...

Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cell...

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy.

BACKGROUND: In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positi...

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer...

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging...

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears.

BACKGROUND: Acute promyelocytic leukemia (APL) is considered a hematologic emergency due to high risk of bleeding and fatal hemorrhages being a major ...

A multipurpose TNM stage ontology for cancer registries.

BACKGROUND: Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work exte...

Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.

Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determini...

A Systematic Review and Meta-Analysis of Comparison of Outcomes of Robot-Assisted versus Open Partial Nephrectomy in Clinical T1 Renal Cell Carcinoma Patients.

INTRODUCTION: Robot-assisted partial nephrectomy (RAPN) has been increasingly used for renal cell carcinoma in recent years. But the advantages of RAR...

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process re...

Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence.

Clinical guidelines integrate latest evidence to support clinical decision-making. As new research findings are published at an increasing rate, it wo...