Presentation on theme: "Edited version of presentation given at RSNA"— Presentation transcript:
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 DiagnosisTimothy Sawyer, MD (presenter)ImQuant, Inc.Richard Robb, Ph.D.Mayo Clinic Biomedical Imaging ResourceRobert Foote, M.D.Vice Chairman of Radiation Oncology, Mayo ClinicVal Lowe, M.D.Director, PET Imaging, Mayo ClinicShigeru Yokoyama, Ph.D.Radiation Physics, Idaho Quantitative Oncology Consortium / Saint Alphonsus Regional Medical Center, Boise, IDEdited version of presentation given at RSNANovember 30, 2005
2 IntroductionCancer treatment is not tailored to individual patients -- less than optimal outcome, greater than necessary toxicity and expenseTraditional advanced medical images are of limited value to the present-day practice of oncologyHypothesis: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 patientsThis 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 celllung, and other cancers):Set agents given for a set number ofcycles, based on empiric evidencefrom randomized trials10 % 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 administeredB -- Of the 90 patients who did not benefit:Some would have been cured even without chemoSome will die, despite receiving itC -- Of those that died despite receiving chemo:Some would have lived with additional cycles of the same chemoSome would have lived with different chemoAll received toxic, expensive treatment, without cure
5 Introduction Example 2: Prostate cancer irradiation Cancer in anterior prostateCancer in posterior prostateCancer very radiosensitiveCancer very radioresistant81 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 voxelsDetects 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 degreesCompares 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 sizeResults 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 MethodsTools and Code from AVW library Developed by Mayo Clinic Biomedical Imaging ResourceNew code with customized graphical user interface from Mayo BIR and ImQuant
10 PET, MRI, SPECT, volumetric MRSI, etc. Voxels Intensity ValueXyZ
11 MethodsVoxels of like or similar intensity value (physiology, molecular concentration, etc.) “connected” to form isonumeric contoursCollections of isonumeric contours, each representing a different intensity value, form 3D “functional, physiologic, or molecular profiles”3D molecular / functional profiles analyzed for quantifiable featuresImages, and image changes, represented as numbers, sets of numbers, graphs, or equationsSeries of processes developed, to integrate quantified change into clinical decision-making
12 Potential Methods for Isonumeric Contour Determination Uniform Intensity SpacingHistogram DistributionMultispectral ClassificationWatershed (Level Sets)Distance From Center/EdgeBrightness-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 toolsVolume of each contourSurface area of each contourShape characteristicsMedian, peak intensity values for voxels within a contour-defined volumeDistance of contours from each otherDistance of contours from a pointVolumes of “elevations” (analogy from conventional topography)Volumes of “depressions”Max or min intensity level within an elevation or depressionNumbers, or locations, of elevations / depressions
16 Images represented as numbers, sets of numbers, graphs, or equations ResultsImages represented as numbers, sets of numbers, graphs, or equationsTreatment-induced image change represented as numbers, sets of numbers, graphs, equations
17 ResultsApproximately 60 processes developed, to integrate change into clinical decision-making in medical oncology, radiation oncology, diagnosis, surgery, and other interventionsExample: Dynamic ChemotherapyImageAdminister systemic therapyRe-imageCompare images or imaging dataExpress volumetric change as numbers, sets of numbers, graphs, or equationsExpress sub-volumetric changes as numbers, sets of numbers, graphs, or equationsCompare volumetric change to volumetric data bank of changes for which outcome is knownCompare sub-volumetric changes to sub-volumetric data bank of changes for which outcome is knownExpress relative volumetric changeExpress relative sub-volumetric changePredict ultimate likelihood of favorable volumetric change, or other clinical endpoints, assuming no change in planRules engine-based recommendation for next cycle (change interval, dose, agents)
18 Conclusions and Future Directions Relevant to multiple imaging sources Relevant to multiple applicationsDisplayDiagnosisChemotherapy tailoringTailoring radiation therapy doseTailoring radiation therapy targeting and intra-tumor dose-paintingDynamic and change-based targetingSurgical planningSurgical targetingMRIPerfusion MRIDiffusion MRIDCE-MRIMR SpectroscopyPETSPECTOthers