1. Edited version of presentation given at RSNA
System for Quantifying Treatment-induced Image Change, and for Integrating Quantified Change into Cancer Treatment-related Decision-making and Diagnosis
Timothy Sawyer, MD (presenter)
ImQuant, Inc.
Richard Robb, Ph.D.
Mayo Clinic Biomedical Imaging Resource
Robert Foote, M.D.
Vice Chairman of Radiation Oncology, Mayo Clinic
Val Lowe, M.D.
Director, PET Imaging, Mayo Clinic
Shigeru Yokoyama, Ph.D.
Radiation Physics, Idaho Quantitative Oncology Consortium / Saint Alphonsus Regional Medical Center, Boise, ID
Edited version of presentation given at RSNA
November 30, 2005

2. Introduction
Cancer treatment is not tailored to individual patients -- less than optimal outcome, greater than necessary toxicity and expense
Traditional advanced medical images are of limited value to the present-day practice of oncology
Hypothesis:
A novel, quantitative imaging format that preserves spatial integrity -- in combination with the right software-based processes -- can facilitate tailoring of cancer treatment to individual patients
This research is about the fact that cancer treatment is not tailored to individual patients – and the hypothesis that treatment tailoring can be facilitated through the novel technique to quantify imaging data, while preserving spatial integrity.

3. Introduction Example: Adjuvant chemotherapy
Adjuvant chemotherapy (breast, colon, non-small cell
lung, and other cancers):
Set agents given for a set number of
cycles, based on empiric evidence
from randomized trials
10 % improvement in long-term survival:
100 patients are treated to benefit 10.

4. Introduction Example: Adjuvant chemotherapy
Statements that are probably true:
A -- Of the 10 patients that benefited, some did not need all of the (toxic and expensive) cycles administered
B -- Of the 90 patients who did not benefit:
Some would have been cured even without chemo
Some will die, despite receiving it
C -- Of those that died despite receiving chemo:
Some would have lived with additional cycles of the same chemo
Some would have lived with different chemo
All received toxic, expensive treatment, without cure

5. Introduction Example 2: Prostate cancer irradiation
Cancer in anterior prostate
Cancer in posterior prostate
Cancer very radiosensitive
Cancer very radioresistant
81 Gy to entire prostate, + margin, including anterior rectum and posterior bladder

6. Imaging The ultimate in vivo system for measuring anatomy, physiology and function, and molecular concentration

7. Ideal image quantification / treatment tailoring system
Applicable to all major imaging sources -- PET, SPECT, MRI, MRSI, DCE-MRI, diffusion MRI, perfusion MRI, etc.
Applicable to all major cancer applications -- systemic therapy, radiation therapy, surgery, etc.
Considers each voxel, yet in quantifying response and making predictions, maintains spatial integrity of entire tumor, and spatial relationships between voxels
Detects and quantifies changes in sub-regions of the tumor that are physiologically or molecularly heterogenous, or that respond to therapy at different rates or to different degrees
Compares changes in sub-regions of the tumor to other sub-regions, and to a data bank of sub-regional changes for which the outcome is known. Goals: a) to gain early warning signs from a particular sub-volumetric region, even when an overall tumor appears to be responding well to therapy, and b) to facilitate differential radiation therapy dosing to different regions of the tumor

8. Ideal image quantification / treatment tailoring system (continued)
Does not rely on simple registration / fusion and digital subtraction, since the pre-therapy versus the mid-therapy tumor is of a different shape and size
Results not only in change quantification, prediction, and treatment recommendations, but also in automatic mid-therapy display of key sub-regional contours, for dynamic (mid-therapy) changes in intra-tumor radiation dose-painting (delivery of higher dose, each fraction, to the sub-regions responding less, and / or less likely to continue to respond)
Sensitive and specific enough to detect and quantify changes very early in treatment, so that early changes in treatment can be made (resulting in better outcomes, less toxicity, and less expense)
Simple to use

9. Methods
Tools and Code from AVW library Developed by Mayo Clinic Biomedical Imaging Resource
New code with customized graphical user interface from Mayo BIR and ImQuant

10. PET, MRI, SPECT, volumetric MRSI, etc. Voxels
Intensity Value X y Z

11. Methods
Voxels of like or similar intensity value (physiology, molecular concentration, etc.) “connected” to form isonumeric contours
Collections of isonumeric contours, each representing a different intensity value, form 3D “functional, physiologic, or molecular profiles”
3D molecular / functional profiles analyzed for quantifiable features
Images, and image changes, represented as numbers, sets of numbers, graphs, or equations
Series of processes developed, to integrate quantified change into clinical decision-making

12. Potential Methods for Isonumeric Contour Determination
Uniform Intensity Spacing
Histogram Distribution
Multispectral Classification
Watershed (Level Sets)
Distance From Center/Edge
Brightness-Area Thresholding

13. Results Intensity Elevation Map in ROI ROI
Isonumeric contours in ROI based on intensity gradients

14. Results Thin Sagittal cuts through ROI
Sequence of sagittal PET images with overlaid isocontours.

15. “Functional Topography” (3D + alpha)
Partial list of quantifiable features measurable by software-based tools
Volume of each contour
Surface area of each contour
Shape characteristics
Median, peak intensity values for voxels within a contour-defined volume
Distance of contours from each other
Distance of contours from a point
Volumes of “elevations” (analogy from conventional topography)
Volumes of “depressions”
Max or min intensity level within an elevation or depression
Numbers, or locations, of elevations / depressions

16. Images represented as numbers, sets of numbers, graphs, or equations
Results
Images represented as numbers, sets of numbers, graphs, or equations
Treatment-induced image change represented as numbers, sets of numbers, graphs, equations

17. Results
Approximately 60 processes developed, to integrate change into clinical decision-making in medical oncology, radiation oncology, diagnosis, surgery, and other interventions
Example: Dynamic Chemotherapy
Image
Administer systemic therapy
Re-image
Compare images or imaging data
Express volumetric change as numbers, sets of numbers, graphs, or equations
Express sub-volumetric changes as numbers, sets of numbers, graphs, or equations
Compare volumetric change to volumetric data bank of changes for which outcome is known
Compare sub-volumetric changes to sub-volumetric data bank of changes for which outcome is known
Express relative volumetric change
Express relative sub-volumetric change
Predict ultimate likelihood of favorable volumetric change, or other clinical endpoints, assuming no change in plan
Rules engine-based recommendation for next cycle (change interval, dose, agents)

18. Conclusions and Future Directions Relevant to multiple imaging sources Relevant to multiple applications
Display
Diagnosis
Chemotherapy tailoring
Tailoring radiation therapy dose
Tailoring radiation therapy targeting and intra-tumor dose-painting
Dynamic and change-based targeting
Surgical planning
Surgical targeting
MRI
Perfusion MRI
Diffusion MRI
DCE-MRI
MR Spectroscopy
PET
SPECT
Others