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Predict histological grade by CT image auto-reviewing

Principal Investigator
Name
Edwin Wang
Degrees
PhD
Institution
University of Calgary
Position Title
Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-340
Initial CDAS Request Approval
Aug 16, 2017
Title
Predict histological grade by CT image auto-reviewing
Summary
Low-dose computed tomography (LDCT) is the recommended screening test for lung cancer. LDCT scans the chest and the reviewers evaluate the generated images for nodules or abnormalities present. This process is mainly done manually by radiologists, and has a relatively high false-positive rate along with over-diagnoses. As true-positive LDCT image data in NLST is paired with pathological images, tissue microarrays and clinical descriptions, we’re willing to design a texture analysis method based on machine learning to predict histological grades and improve the diagnosis.
Aims

To reach the overall goal, we have several specific aims:

(1) To generate pix2pix models with bounding box to automatically detect nodules in LDCT images. (eg, to find region of interest(ROI) with segmentation)
(2) To train & evaluate classification models to predict histological grades and survival length of ROIs.
(3) To use the feature extraction headers learned in above models, build natural language generation headers to give rough descriptions of LDCT images.

Collaborators

Xiaowen FENG feng.xiaowen@ucalgary.ca, PhD student