System for Quantifying Treatment-induced Image Change, and for Integrating Quantified Change into Cancer Treatment-relat

1. 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

3. IntroductionExample: 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. IntroductionExample: 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. IntroductionExample 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. ImagingThe 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. y X Intensity Value Z PET, MRI, SPECT, volumetric MRSI, etc.Voxels

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

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. 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 DirectionsRelevant to multiple imaging sourcesRelevant 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