Oncology/Hematology

Mining Implicit Treatment Concepts for Neural Precision Medicine.

Precision medicine (PM) is regarded as an information retrieval (IR) task in which biomedical articles containing treatment information about specific diseases or genetic variants are retrieved in response to patient record for the purpose of providing medical evidence to the point-of-care. In existing PM approaches, manual keywords, such as "treatment" and "therapy," are considered direct indicat...

A knowledge-based system for brain tumor segmentation using only 3D FLAIR images.

This study aims to develop a semi-automatic system for brain tumor segmentation in 3D MR images. For a given image, noise was corrected using SUSAN algorithm first. A specific region of interest (ROI) that contains tumor was identified and then the intensity non-uniformity in ROI was corrected via the histogram normalization and intensity scaling. Each voxel in ROI was presented using 22 features ...

Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.

PURPOSE: There has been burgeoning interest in applying machine learning methods for predicting radiotherapy outcomes. However, the imbalanced ratio o...

Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children.

Characterization of children exposure to extremely low frequency (ELF) magnetic fields is an important issue because of the possible correlation of le...

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms.

BACKGROUND: Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingl...

Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers.

IMPORTANCE: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can...

Oncologic outcomes in patients treated with endoscopic robot assisted simple enucleation (ERASE) for renal cell carcinoma: Results from a tertiary referral center.

INTRODUCTION: Open Simple Enucleation (OSE) has been demonstrated to be an oncologically safe alternative to standard partial nephrectomy. We assessed...

Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients.

MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast can...

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise asse...

Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adaptive radiation therapy. However, due to severe artif...

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning.

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy d...

Machine Learning Approach to find the relation between Endometriosis, benign breast disease, cystitis and non-toxic goiter.

The exact mechanism of endometriosis is unknown. The recommendation system (RS) based on item similarities of machine learning has never been applied ...

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many com...

Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation.

Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays...

Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer.

Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is esse...

Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module.

Although successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts s...

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocar...

Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology.

Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cance...

Quality assurance of computer-aided detection and diagnosis in colonoscopy.

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-a...

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor deline...