Oncology/Hematology

Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery.

OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c contr...

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can readily learn complex nonlinear relationships, many methods are criticized as inadequate because of limited interpretability. We propose using unsuper...

Is Watson for Oncology Unreasonably Dangerous?: Making A Case for How to Prove Products Liability Based on a Flawed Artificial Intelligence Design.

Artificial intelligence (AI) machines hold the world's curiosity captive. Futuristic television shows like West World are set in desert lands against ...

A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytog...

[An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].

Lung cancer is the most common and fatal tumor in the world with limited diagnostic and treatment methods. The development of precision medicine has b...

Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image.

Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer b...

Salvage robot-assisted radical prostatectomy following failed local treatments.

Prostate cancer represents the most commonly diagnosed cancer in men and is the second-leading cause of cancer related death in the United States. Pri...

Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.

BACKGROUND: To explore whether radiomics combined with computed tomography (CT) images can be used to establish a model for differentiating high grade...

A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche.

Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastas...

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen re...

Nomogram and Artificial Neural Network for Prognostic Performance on the Albumin-Bilirubin Grade for Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization.

PURPOSE: To construct the albumin-bilirubin (ALBI) grade and the Child-Turcotte-Pugh (CTP) score based on nomograms, as well as to develop an artifici...

Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment b...

MR-based treatment planning in radiation therapy using a deep learning approach.

PURPOSE: To develop and evaluate the feasibility of deep learning approaches for MR-based treatment planning (deepMTP) in brain tumor radiation therap...

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks.

For patients undergoing surgical cancer resection of squamous cell carcinoma (SCCa), cancer-free surgical margins are essential for good prognosis. We...

[Machine Learning Applications in Cancer Genome Medicine].

Practical cancer genome medicine requires large-scale data analysis for many types of biological data such as cancer driver mutations, aberrantly meth...

Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning.

Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging, however, they were limited to just pre...

Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.

In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynt...

GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agon...

Low coherence quantitative phase microscopy with machine learning model and Raman spectroscopy for the study of breast cancer cells and their classification.

Early-stage detection of breast cancer is the primary requirement in modern healthcare as it is the most common cancer among women worldwide. Histopat...

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography.

In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recur...