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Advanced Treatment Response Prediction using clinical parameters and advanced unsupervised machine learning: the contribution scattergram.
Case ID:
C12027
Report of Invention:
5/16/2012
Web Published:
3/17/2017
The early diagnosis of cancer is necessary to facilitate the clinical management of patients, and machine learning (ML) algorithms can identify and model the progression and treatment of cancerous conditions. As a result, there is a growing trend in the application of ML methods in cancer research, and considerable activity in the integration of different types of data for the treatment of breast cancer (Kourou et al.). However, there are few computer-aided diagnostic systems that integrate MRI and other imaging modalities to visualize the data. Therefore, researchers have developed ML systems, with varying degrees of supervision and data labeling, to capture relationships across different ontologies (images, clinical data, etc.). JHU inventors have developed a novel incremental nonlinear dimensionality reduction algorithm that maps each image to a point in a 2-D space to crease a scattergram. In a trial, the system was able to discern treatment response across multiple imaging modalities.
Patent Information:
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Advanced Treatment Response Prediction using clinical parameters and advanced unsupervised machine learning: the contribution scattergram.
PCT: Patent Cooperation Treaty
United States
14/909,154
10,388,017
2/1/2016
8/20/2019
7/31/2034
Granted
Direct Link:
https://jhu.technologypublisher.com/technology/24662
Inventors:
Category(s):
Technology Classifications > Computers, Electronics & Software,
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For Information, Contact:
Louis Mari
lmari3@jhu.edu
410-614-0300
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