Oncology/Hematology

Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network.

Colorectal cancer (CRC) is a major global health concern. Its early diagnosis is extremely important, as it determines treatment options and strongly influences the length of survival. Histologic diagnosis can be made by pathologists based on images of tissues obtained from a colonoscopic biopsy. Convolutional neural networks (CNNs)-i.e., deep neural networks (DNNs) specifically adapted to image d...

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

PURPOSE: The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients.

Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer.

BACKGROUND: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis ...

[Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].

OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.

Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.

Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the...

A Data Mining Approach to Diagnose Cancer for Therapeutic Decision Making.

BACKGROUND: With the increase in population, there is a rise in number of cancer cases starting from young children to old people. The uncommon cancer...

Radiomics with artificial intelligence for precision medicine in radiation therapy.

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it...

Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data.

Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine ...

A Cancer Paradox: Machine-Learning Backed Propensity-Score Analysis of Coronary Angiography Findings in Cardio-Oncology.

OBJECTIVES: Cancer has been proposed as a cardiovascular risk factor. We aimed to assess the cardiovascular risk profile and coronary angiography (CA)...

Constructing a Risk Prediction Model for Lung Cancer Recurrence by Using Gene Function Clustering and Machine Learning.

OBJECTIVE: A significant proportion of patients with early non-small cell lung cancer (NSCLC) can be cured by surgery. The distant metastasis of tumor...

Cancer Phenotype Development: A Literature Review.

EHR-based, computable phenotypes can be leveraged by healthcare organizations and researchers to improve the cohort identification process. The abilit...

[Impact of DVH Outliers Registered in Knowledge-based Planning on Volumetric Modulated Arc Therapy Treatment Planning for Prostate Cancer].

RapidPlan, a knowledge-based planning software, uses a model library containing the dose-volume histogram (DVH) of previous treatment plans, and it au...

Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network.

Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempted to determine a set of critical proteins that were ...

Breast mass detection and diagnosis using fused features with density.

BACKGROUND: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis tec...

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment...

Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.

BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression...

GIDB: a knowledge database for the automated curation and multidimensional analysis of molecular signatures in gastrointestinal cancer.

Gastrointestinal (GI) cancer is common, characterized by high mortality, and includes oesophagus, gastric, liver, bile duct, pancreas, rectal and colo...

Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model.

AIM AND OBJECTIVE: Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid tech...

Iterative image reconstruction for sparse-view CT via total variation regularization and dictionary learning.

Recently, low-dose computed tomography (CT) has become highly desirable due to the increasing attention paid to the potential risks of excessive radia...

A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI.

PURPOSE: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor regio...