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Do you have a TCIA-related presentation at RSNA that's not listed below?  Contact the helpdesk to request it be added!

• Sunday 10:55-11:05 AM | SSA12-02 | Room:
  • FalcoNet-GMC: A 3D Convolutional Neural Network Module for Instance Segmentation and Quantification of Distant Recurrence from Gynecological Cancers 
    • A multifunctional web-based auxiliary system for distant recurrence from gynecologic cancer will enhance the early detection for salvage treatment, with better segmentation by compartment weight maps.
• Sunday 11:45-11:55 AM | SSA12-07 | Room:

  • Constructing a Platform Based on Deep Learning Model to Mimic the Self-Organization Process of CT Images Order for Automatically Recognizing Human Anatomy

    • To demonstrate the ability of a deep learning application to automatically identify computed tomography (CT) slice regions by major Human anatomy. This application will be deployed in National Health Insurance of Taiwan (NHI) to classify the around 458 million CT images in 2018.
• Sunday 1:00-1:30 PM | IN006-EB-SUB | Room:

  • Reproducibility of Quantitative Features in Prostate mpMRI

    • Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality in prostate mpMRI has shown to be predictive of clinically significant cancer defined by Gleason grade groups. In this study we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. We have shown that some quantitative imaging features are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. A validated set of reproducible image features in mpMRI will allow us to develop a clinically reliable malignance risk stratification score. This will enable the possibility of using imaging as a surrogate to invasive biopsies
• Monday 10:30-10:40 AM | SSC03-01 | Room:
  • Impact of Interobserver Variability in Manual Segmentation of Non-small Cell Lung Cancer (NSCLC) on Computed Tomography
    • Discovery of predictive and prognostic radiomic features in cancer is currently of great interest to the radiologic community. Since there is no reliable automated means of segmenting lung cancer, tumor labeling is typically performed by imaging analysts, physician trainees and attending physicians. Here we examine the impact of level of specialty training on interobserver variability in manual segmentation of non-small cell lung cancer (NSCLC).
• Monday 10:50-11:00 AM | SSC03-03 | Room:

  • Correlation-Incorporated Hierarchical Clustering of High-Dimensional Radiomic Features for Prognostic Phenotype Identification of EGFR-mutated Non-Small Cell Lung Cancer

    • We propose a correlation-incorporated unsupervised hierarchical clustering algorithm and evaluate it in identifying computed tomography (CT) radiomic phenotypes of EGFR-mutated non-small cell lung cancer (NSCLC) in association with patient overall survival. CHCA effectively reduces the high dimensionality of radiomic features while allowing for robust identification of CT-based phenotypes of EGFR-mutated NSCLC that are associated with patient survival.
• Tuesday 9:20-9:30 AM |RC305-04 | Room:
  • A Radiomics Nomogram Based on Multiregional Features Might Predict MGMT Promoter Methylation of Glioblastoma Patients
    • To investigate multiregional features from multimodal MRI in reflecting O6-methylguanine methyltransferase (MGMT) promoter methylation status, and to establish visualized nomogram for MGMT methylation prediction of glioblastomas (GBM) patients. The radiomics nomogram based on multiregional features from multimodal MRI was proposed in our study, and could individually and visually predict MGMT status of GBM patients. In addition, the rEA and rNec areas of GBM play an important role in the prediction of MGMT methylation.
• Friday 8:30-10:00 AM | RC825 | Room: 

  • Radiomics: From Image to Radiomics

    • 1) Learn about the role of image annotations in radiology and their relevance to enabling interoperability and for communicating results and value for machine learning and decision support. 2) Become acquainted with important standards and tools that support the creation, management, and use of image annotations. 3) See case examples of image annotations in practice to enable developing applications that help the practice of radiology. 4) Understand the categories of, and the specific radiomic image features that can be computed from images. 5) Understand the effect and implications of image acquisition and reconstruction on radiomic image features. 6) Learn about workflows that drive the creation of predictive models from radiomic image features. 7) Understand the methods for and the potential value of correlating radiological images with genomic data for research and clinical care. 8) Learn how to access genomic and imaging data from databases such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases, respectively. 9) Learn about methods and tools for annotating regions within images and link them with semantic and computational features.10) Learn about methods and tools for analyzing molecular data, generating molecular features and associating them with imaging features. 11) Learn how deep learning can revolutionize interpretation of medical images.

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