AI Medical Compendium Journal:
JCO clinical cancer informatics

Showing 141 to 150 of 163 articles

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

JCO clinical cancer informatics
PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the applicati...

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

JCO clinical cancer informatics
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 ...

Validity of Natural Language Processing for Ascertainment of and Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

JCO clinical cancer informatics
PURPOSE: SEER registries do not report results of epidermal growth factor receptor () and anaplastic lymphoma kinase () mutation tests. To facilitate population-based research in molecularly defined subgroups of non-small-cell lung cancer (NSCLC), we...

Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.

JCO clinical cancer informatics
PURPOSE: Although the bulk of patient-generated health data are increasing exponentially, their use is impeded because most data come in unstructured format, namely as free-text clinical reports. A variety of natural language processing (NLP) methods...

A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic.

JCO clinical cancer informatics
PURPOSE: Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model ...

Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

JCO clinical cancer informatics
PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and vali...

Prediction of Atypical Ductal Hyperplasia Upgrades Through a Machine Learning Approach to Reduce Unnecessary Surgical Excisions.

JCO clinical cancer informatics
PURPOSE: Surgical excision is currently recommended for all occurrences of atypical ductal hyperplasia (ADH) found on core needle biopsies for malignancy diagnoses and treatment of lesions. The excision of all ADH lesions may lead to overtreatment, w...

Deep Learning for Natural Language Processing in Urology: State-of-the-Art Automated Extraction of Detailed Pathologic Prostate Cancer Data From Narratively Written Electronic Health Records.

JCO clinical cancer informatics
PURPOSE: Entering all information from narrative documentation for clinical research into databases is time consuming, costly, and nearly impossible. Even high-volume databases do not cover all patient characteristics and drawn results may be limited...

Leveraging Human Genetics to Guide Cancer Drug Development.

JCO clinical cancer informatics
PURPOSE: The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development ...

Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review.

JCO clinical cancer informatics
PURPOSE: Clear and trustworthy information is essential for people who are ill. People with cancer, in particular, are targeted with vast quantities of patient education material, but of variable quality. Machine learning technologies are popular acr...