Oncology/Hematology

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classi...

Predicting Disease-Free Lung Cancer Survival Using Patient Reported Outcome (PRO) Measurements with Comparisons of Five Machine Learning Techniques (MLT).

The study was to develop the lung cancer patients' prediction model for predicting 5-year survival after completion of treatment by using Machine Learning Technology (MLT), adding patient reporting (PRO) measurements of lung cancer survivors to a variety of clinical parameters. Finally, the survival prediction models with the addition of lung cancer survivors' PRO measurements to the well-known cl...

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network.

The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior...

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method.

In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary...

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks.

Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should...

Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting pr...

Validation of a Cyclic Algorithm to Proxy Number of Lines of Systemic Cancer Therapy Using Administrative Data.

PURPOSE: Researchers are automating the process for identifying the number of lines of systemic cancer therapy received by patients. To date, algorith...

Artificial Intelligence Treatment Decision Support For Complex Breast Cancer Among Oncologists With Varying Expertise.

PURPOSE: The aim of the current study was to assess treatment concordance and adherence to National Comprehensive Cancer Network breast cancer treatme...

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations.

Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although ...

A new era: artificial intelligence and machine learning in prostate cancer.

Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolution...

Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging.

OBJECTIVE: This study aimed to evaluate a deep learning protocol to identify neoplasia in intraductal papillary mucinous neoplasia (IPMN) in compariso...

Selection of Radiomics Features based on their Reproducibility.

Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Exi...

Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly gu...

Hybrid Unified Deep Learning Network for Highly Precise Gleason Grading of Prostate Cancer.

Prostate cancer is one of the leading causes of death around the world. The manual Gleason grading of prostate cancer after histological analysis of s...

Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging.

Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and...

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks.

The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to...

Predicting lymphoma outcomes and risk factors in patients with primary Sjögren's Syndrome using gradient boosting tree ensembles.

Primary Sjogren's Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS ...

A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient's Prognosis using Comprehensive Clinical Parameters.

The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. Howeve...

The Feasibility of Arrhythmias Detection from A Capacitive ECG Measurement Using Convolutional Neural Network.

Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between electrodes and skin. This unconstrained measurement is...

Deep multi-modality collaborative learning for distant metastases predication in PET-CT soft-tissue sarcoma studies.

Soft-tissue Sarcomas (STS) are a heterogeneous group of malignant neoplasms with a relatively high mortality rate from distant metastases. Early predi...