1. Update on Lung Cancer Image Processing Rick Avila Karthik Krishnan Luis Ibanez Kitware, Inc. rick.avila@kitware.com April 19, 2006

2. Kitware Therapy Assessment Start Therapy Time Assessment Tumor responseTumor response ID new lesionsID new lesions ? Tumor Size tttt Assess Response 4 cm lesion Characteristics Late stageLate stage Thick CTThick CT ?

3. Kitware RECIST Time Baseline & Treat Assess Response  D = -30%  D = +20% Stable Disease Partial Response Complete Response Progressive Disease weeks Target Lesion Measurement RECIST: Sum of LD Unaided Interpretation 4cm lesion 8mm  D, 13 pixels 73%  Volume Erasmus et. al., JCO 2003 Intra-observer error PD: 9.5% of tumors PR: 3% of tumors Inter-observer error PD: 30% of tumors PR: 14% of tumors Erasmus et. al., JCO 2003 Intra-observer error PD: 9.5% of tumors PR: 3% of tumors Inter-observer error PD: 30% of tumors PR: 14% of tumors

4. Kitware We can do better Time Stable Disease Partial Response Complete Response Progressive Disease tt Target Lesion Measurement RECIST: Sum of LD Aided 3D Interpretatio n Improve: BiasBias VarianceVariance For Lower: Interval (  t)Interval (  t) Study NStudy N Early Detection & Nodule Sizing? 4cm lesion

5. Kitware First Step: Open Development Databases All Cases Shown In This Presentation Came From These Databases

6. Kitware Measurement Challenges Patient/Lesion Presentation –Size –Complexity –Changes over time (necrosis) Scanners –Hardware –Software Protocols –ScanRx –Contrast –Patient position Observer –Seed points/ROI –Data Interpretation 5mm 2.5mm

7. Kitware Complex Boundaries

8. Kitware

9. Kitware Volumetric Algorithm Challenges Boundary Identification Challenges Vascular network (Ev)Vascular network (Ev) Bronchial network (Eb)Bronchial network (Eb) Pleura (Ep)Pleura (Ep) Sub-voxel edge (Es)Sub-voxel edge (Es) Errors at 2 time points Ev Error strongly depends on lesion size and slice thickness Ep Pleura Es No/Small  I

10. Kitware Solid Algorithm: Operating Envelope Lesion Size Slice Thickness 10mm20mm 15mm 5mm0 1.25 mm 2.5 mm 3.75 mm 5.0 mm ComplexBoundaries PartialVolume Noise… Curvature… ClinicalTrials Solution <= 1.25mm thickness<= 1.25mm thickness Algorithm support for complex intersectionsAlgorithm support for complex intersections Validate against wide patient and protocol populationValidate against wide patient and protocol population

11. Kitware Motivating Example 46d 69d 59mm48mm44mm 1D  25% RECIST would classify response to therapy as Stable Disease

12. Kitware 3D Analysis

13. Kitware

14. Kitware Validation Approach Time Baseline & Treat Stable Disease Partial Response Complete Response Progressive Disease Response Metric Case Collection Collect cases w/many short interval scansCollect cases w/many short interval scans Assessment on last scan(s) is clearAssessment on last scan(s) is clear Annotation One or more expert(s) classify each case based on all dataOne or more expert(s) classify each case based on all data T1T2T3T4T5 Metric Measure sens/spec between assess pairsMeasure sens/spec between assess pairs Compare metrics at last time pointCompare metrics at last time point At what time can a sens/spec be met?At what time can a sens/spec be met?

15. Kitware Open Database Collection Priorities Add Annotation to Open Databases –Need to assess RECIST as the baseline performance –Need an expert assessment of response for case Add More Cases to Open Databases –Wide range of patient/lesion presentations –Wide range of therapy interactions Emphasize Thin Slice –Algorithms perform better (e.g. I’’) Collect Data at Smaller Time Intervals –Algorithms perform better (e.g. registration)

16. Thank You

17. Kitware Edge Detection Algorithms that utilize acquisition characteristics (e.g. PSF, SNR) can adapt to changes in acquisition Step Function PSF + Noise ObjectScannerImage SmoothLocalize Recover Edge Using Acquisition Characteristics Elder et. al. TPAMI 1998

18. Kitware Cross-Platform Capability Goal:Goal: –Software achieves accuracy despite variation in: Scanning equipmentScanning equipment Acquisition protocolsAcquisition protocols Solution:Solution: –Establish minimum acquisition standards/protocols –Keep acquisition technique constant per patient –Measure scanner characteristics utilizing a standard phantom and publish –Utilize model-based algorithms

19. Kitware Unexpected Results Many studies report greater variance and error when comparing 1D/2D/3D analysis –Issue 1: More is not always better All measurements need high precisionAll measurements need high precision Consider slice thicknessConsider slice thickness –Issue 2: New metrics need optimization Development data needed to establish best separation between response classesDevelopment data needed to establish best separation between response classes