caBIG Tools Supporting(Radiation Oncology)

Research at OHSU

Jayashree Kalpathy-Cramer, PhD

Oregon Health & Science University, Portland, OR

Introduction

• Instructor at Oregon Health & Science University (Medical Informatics)• K99-R00 Pathway to Independence grant through NLM• Clinical Image Retrieval: User needs assessment,

toolbox development & evaluation• combine content-based image retrieval (CBIR) with

textual retrieval• use relevance feedback/pairwise comparisons to

improve distance metrics• what is meant by “similar”

Introduction to caBIG

• First submission of grant

• reviews noted significant overlap in proposed work with activities in caBIG

• Knight Cancer Institute at OHSU deploying caBIG tools

• attended F2F

• Collaboration with faculty in Radiation Oncology at OHSU and UTHSCSA

• Revised grant leveraging caBIG activities and tools

• Funded in 2009

Project I - Use of LIDC Imagesfrom NBIA

• Goal: Organize a nodule detection challenge at ImageCLEF 2009, an international image retrieval challenge (classification as a precursor to retrieval)

• caBIG tools: LIDC images in NBIA, (pMAP)

• Current Status: complete, interest but limited participation

• Plans: continued interest in such a challenge, looking for optimal venue (in conjunction with major computer vision/ medical imaging conference). In addition to detection, do imaging features correspond to manual annotations?

Project II: Evaluation variabilityin target volume delineation

• Background: Target volume delineation is a critical step in radiation therapy planning. Significant variability exists between clinicians in this critical step

• Goal: Characterize variability using volumetric overlap, surface distances and ultimately, resulting dose distribution (DVH)

• (possible) caBIG tools: XIP, AVT

• Current Status: created web-based learning tool for target volume delineation (TacTICS) created under SIIM grant (using ITK, VTK, R)

• Plans: Use XIP, AVT for characterization

• Roadblocks: extend XIP for DICOM-RT, AVT

Available data

• Rectal cancer

• target volume delineation study in the cooperative group setting

• demonstrated the utility of a consensus guideline based atlas intervention to improve concordance with an expert standard

• Human-Computer Interface Differentials in Target volume Delineation: A Multi-Institutional Study”

• >500 structure sets from a total of 160 contouring sessions from 27 distinct users at 7 institutes over an 18-month period

Example Data

• SWOG S0713 T3N0M0 adenocarcinoma of the rectum

• Largest and smallest CTVA

Target Contour Testing/Instructional Computer Software (TaCTICS)

Information about TaCTICS:

• TaCTICS is an instructional software for radiation therapy target volume delineation. We have obtained contours (RT-structures) for different sites including lung, liver, prostate, and Head and Neck from experts. The goal is to anatomical site and download the data. The data is available as zipped DICOM match the expert’s contour as closely as possible.

Instructions:

1) Register at the registration page. You will be mailed a password to download the data

2) Select the images

3) Contour the structures using your favorite treatment planning system

4) Export the RTSTRUCT file

5) Upload the RTSTRUCT file at the submission page. Select the correct anatomical site and expert that you want to compare against (expert 1, expert 2, probability map from all users)

6) You should get an e-mail with your report within a few minutes

TaCTICS

• The flowchart displays how the TaCTICS software works.

• DICOM-RT, CT slices, and RTSTRUCT (structures) can be used in:

• MRIconvert

• Which then leads to ITK (mod IJ alg)

• Set of binary (multiple label structures)

• Conformance metrics (volumetric, spatial, SD)

• Statistical analysis (R)

• Final Summary

• ITK (mod IJ alg)

• Set of binary (multiple label structures)

• Conformance metrics (volumetric, spatial, SD)

• Statistical analysis (R)

• Final Summary

• AVT

• Which could possible lead to a final summary

Project IV - Image Retrieval forRadiation Oncology

• Goal: Create a system to store and retrieve Ctbased treatment plans

• (possible) caBIG tools: XIP, AIM, AVT, EVS

• Current Status: Gathering requirements from radiation oncologists, medical physicists, dositmetrists, residents, fellow

• Plans: Compile list of concepts not currently in AIM, determine which ones would lend themsevles to AIM-like structure

What are some characteristics of “interesting cases”?

• Almost everyone has a personal list of “interesting cases”• often on stickies, in personal computer, notebook etc• topics include: site, technique used for treatment

(“RapidArc and prostate”), shape of tumor (heterogenous, multifocal), characteristics of treatment plan (how many isocenters, distance of separation, MLC configuration), immobilization used, image guidance

Defining similarity

• What is meant by “similar”• Anatomical Location• Visual appearance

• Shape, volume, edges• Histopathology• Gene expression• Context of patient

• Recruit Clinicians: Use pairwise comparison to determine weights (distance metric learning)

Scenarios

• Use Case #1: Trainee, Dr. X is working with attending physician, Dr. Y. Dr. X is starting to create a new treatment plan for her patient. Dr. X would like to be able to find the last five ‘similar’ cases and the corresponding treatment plans created by her attending Dr. Y.

• Use Case #2: Dr. A is preparing a talk for pediatric grand rounds. He would like to pull up the most recent 5-10 cases of patients with Wilm’s tumor.

• Use Case #3: Dr. B has been funded for a retrospective research study on the efficacy of radiation therapy. She would like to be able to retrieve all cases of node-negative lung cancer treated with definitive RT alone that were treated over the past 10 years.

• Use Case #4: Dr. F has a research study that seeks to correlate treatment fields or modalities with patient outcomes. She would like to retrieve treatment plans for all patients with laryngeal cancer for the past 10 years.

• Use Case #5: Dr. W is comparing toxicity from conventional and Stereotactic Body Radiation (SBRT) [99] treatments. For the study on the effect of dose on toxicity, she would like to compare the Dose Volume Histograms.

• Use Case #6: Dr. Z would like to know if the proximity of the tumor to the spinal cord affects the failure rate of treatment (RTOG study). He would like to retrieval all cases where the tumor is <Y cm from the spinal cord.

• Use Case #7: Dr. F would be able to compare H&N contours done by Expert X vs. Expert Y for a study. [120]

• Use Case 8: Dr. T wants to create guidelines for treatment margins depending on the location of the tumor in the lung based on expected motion during breathing. He would like to find lung tumors by lobe or by location (e.g., peripheral versus central).

Project IV: U01 for shutterspeed model of DCE-MR

• Background: Imaging scientists at OHSU have developed novel analysis technique for DCE-MR images that is sensitive to vascular changes. They have identified a potential imaging biomarker that appears to be a good predictor of response

• Goal: Clinical response, surgical pathology results and biochemical markers will be used as endpoints for correlation with imaging results and statistical analyses

• (possible) caBIG tools: XIP, AVT, AIM

• Current Status: submitted U01 proposal in Feb.

• Plans: Pending grant funding

Immediate Needs

• Little hand holding with:

• XIP with DICOM-RT support

• AVT (cross platform)

• Extending AIM for RadOnc

Acknowledgments

• William Hersh, MD, Chair, Dept. Medical Informatics & Clinical Epidemiology, OHSU

• Shannon McWeeney, PhD, Director, OHSU Cancer Institute Informatics

• K99-R00 grant from the National Library of Medicine 1K99LM009889-01A1