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Machine learning-based radiation toxicity mitigation in pediatric brain cancer

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43CA233346-01
Agency Tracking Number: R43CA233346
Amount: $224,726.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 101
Solicitation Number: PA17-302
Timeline
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-14
Award End Date (Contract End Date): 2019-05-13
Small Business Information
3200 S SEPULVEDA BLVD
Los Angeles, CA 90034-4299
United States
DUNS: 079439264
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 SINCHAI TSAO
 (424) 341-3886
 mail@sinchaitsao.com
Business Contact
 SINCHAI TSAO
Phone: (424) 341-3886
Email: sinchai.tsao@voxelhealthcare.com
Research Institution
N/A
Abstract

Project Summary
Radiation therapyRThas a proven record of efficacy in treating many forms of pediatric brain
tumorsHoweverit is associated with long term side effects due to damage to healthy tissueThis is especially important in the developing brainwhere long term deficits can be seen in the
areas of intelligenceattentionmemory and psychomotor processingTo mediate these deficitsthere has been a push away from whole brain irradiation to more targeted treatment by using
dose painting intensity modulated radiation therapyDP IMRTHoweverin order to use these
techniquesmore information about how dosing to organs at riskOARsaffects outcomesincluding volumetric changes in the brainVoxel Healthcare LLCformerly Advanced Medical Systems LLCis the developer of ClickBrainan automatic pediatric MR brain segmentation tool that uses cloud based deep learningGoogle TensorFlowtechnology for radiology clinical decision supportIn Aimawe extend
ClickBrain to ClickBrain RTa system that will combine ClickBrainandapos s pre treatment brain
structure segmentation outputs with radiation planning CTs and MRs to calculate dosing to
OARsClickBrain RT will also segment longitudinal MRIsmonthmonthsyearyearsto track outcomes via volumetric changesWe will use OAR dosingdemographicstumor type
and gradechemotherapy informationOAR and tumor volumetric measurements to predict
tumor and OAR volumetric outcomesWe will adapt our existing version of a multi time point
machine learning technique to do this prediction taskIn Aimba user interface for this cloud
computing based proof of concept system will be built to allow the RT planner to import patient
information and see changes in predicted longitudinal post RT OAR and tumor volumesbased
on adjusting OAR dosages for a particular patientOur initial validationAimwill focus on an
existing database ofgerm cell tumor patients acquired as part of standard of care and
previous studies at Childrenandapos s Hospital Los AngelesGerm cell tumors have relative uniform size
and location and provide an ideal dataset to validate our proof of concept systemOur long term goal for ClickBrain RT is to train the machine learning algorithm to provide
optimized recommended OAR dosage ranges based on patient history and tumor informationOur software will allow radiation oncologists to optimize treatment and vastly improve long term
quality of life in pediatric brain tumor survivors Narrative
Mitigating radiation toxicity due to radiation therapy for the treatment of pediatric brain tumors is
important to avoid long term developmental side effectsOur ClickBrain RT software will use
cloud computing based deep learning technologies to automatically delineate key structures in
the brain and to train a machine learning algorithm to predict volumetric changes due to
treatment related radiation dosing to these key structuresThis will enable physicians to better
avoid unnecessary radiation doses that may cause long term deficits in children with brain
tumors

* Information listed above is at the time of submission. *

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