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 RSNANovember 30, 2005

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

IntroductionExample: Adjuvant chemotherapy

Adjuvant chemotherapy (breast, colon, non-small celllung, and other cancers):

Set agents given for a set number ofcycles, based on empiric evidencefrom randomized trials

10 % improvement in long-term survival:

100 patients are treated to benefit 10.

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 chemoSome will die, despite receiving it

C -- Of those that died despite receiving chemo:

Some would have lived with additional cycles of the same chemoSome would have lived with different chemo

All received toxic, expensive treatment, without cure

IntroductionExample 2: Prostate cancer irradiation

Cancer in anterior prostateCancer in posterior prostateCancer very radiosensitiveCancer very radioresistant

81 Gy to entire prostate, + margin, including anterior

rectum and posterior bladder

Imaging

The ultimate in vivo system for measuring anatomy, physiology and function, and

molecular concentration

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

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

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

PET, MRI, SPECT, volumetric MRSI, etc.Voxels

Intensity Value X

y

Z

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

• Uniform Intensity Spacing

• Histogram Distribution

• Multispectral Classification

• Watershed (Level Sets)

• Distance From Center/Edge

• Brightness-Area Thresholding

Potential Methods for Isonumeric Contour Determination

Results

Isonumeric contours in ROI based on intensity gradients

Intensity Elevation Map in ROIROI

Results Thin Sagittal cuts through ROI

•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

““Functional Topography” (3D + Functional Topography” (3D + alpha)alpha)

Partial list of quantifiable features Partial list of quantifiable features measurable by software-based toolsmeasurable by software-based tools

Results

Images represented as numbers, sets of Images represented as numbers, sets of numbers, graphs, or equationsnumbers, graphs, or equations

Treatment-induced image Treatment-induced image changechange represented represented as numbers, sets of numbers, graphs, as numbers, sets of numbers, graphs,

equationsequations

ResultsApproximately 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)

Conclusions and Future DirectionsRelevant 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