Oncology/Hematology

Deep learning-based detection and segmentation-assisted management of brain metastases.

BACKGROUND: Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance.

[Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image].

OBJECTIVE: Feature extraction of breast tumors is very important in the breast tumor detection (benign and malignant) in ultrasound image. The traditional quantitative description of breast tumors has some shortcomings, such as inaccuracy. A simple and accurate feature extraction method has been studied.

Radiomics and Deep Learning: Hepatic Applications.

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the p...

Use of Artificial Intelligence to Reduce Radiation Exposure at Fluoroscopy-Guided Endoscopic Procedures.

OBJECTIVES: Exposure to ionizing radiation remains a hazard for patients and healthcare providers. We evaluated the utility of an artificial intellige...

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

BACKGROUND: PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for pa...

[Prostate cancer pathologic features in men ≤55 years treated with robot assisted radical prostatectomy.].

OBJECTIVE: Among western males, prostate cancer is the most frequent oncological disease. Since the widespread of PSA, diagnoses in younger adults is ...

[Efficacy of Neoadjuvant Endocrine Therapy for Prostate Ductal Carcinoma with Large Multiple Cysts Prior to Robot-Assisted Laparoscopic Radical Prostatectomy].

A 71-year-old man with gross hematuria and urinary retention showed a 7×8 cm polycystic mass compressing the prostate on the right ventral side on pel...

Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.

Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied exte...

Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures.

Uncovering the heterogeneity of cellular populations and multicellular constructs is a long-standing goal in fields ranging from antimicrobial resista...

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

BACKGROUNDCeramides are sphingolipids that play causative roles in diabetes and heart disease, with their serum levels measured clinically as biomarke...

MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variabili...

Application of Convolutional Neural Networks for Detection of Superficial Nonampullary Duodenal Epithelial Tumors in Esophagogastroduodenoscopic Images.

OBJECTIVES: A superficial nonampullary duodenal epithelial tumor (SNADET) is defined as a mucosal or submucosal sporadic tumor of the duodenum that do...

Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer.

PURPOSE: For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models r...

Computer-assisted Diagnosis of Breast Cancer by Cell Network Matrix Extraction and Multilayer Perceptron Analysis.

OBJECTIVE: Diagnosis of breast cancer is based on identification of various morphologic features by histopathologic examination of the specimen. Ancil...

Power and perils of prediction in palliative care.

[Prediction of gene mutation in lung cancer based on deep learning and histomorphology analysis].

Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advan...

[Application of Radiomics in Classification and Prediction of Benign and Malignant Lung Tumors].

Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based...

Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive ...

Adaptive back-stepping cancer control using Legendre polynomials.

Here, a model-free controller for cancer treatment is presented. The treatment objective is to find a proper drug dosage that can reduce the populatio...

Identification of prognostic immune-related signature predicting the overall survival for colorectal cancer.

OBJECTIVE: The morbidity and mortality of patients with colorectal cancer, one of the most common malignant tumors worldwide, is steadily increasing. ...