It is important. It is important. MTR: Imaging Clinical Trials. Isn t t it obvious? then why do we ask the question?

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Pamela Brittany Lee
6 years ago
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1 Isn t t it obvious? Image Treatment Response Assessment: How Important is Quantification? It is important Mitchell chnall MD, PhD Mathew J Wilson Professor of Radiology University of Pennsylvania Chair, ACRIN Clinical Trial Network Isn t t it obvious? Diagnostics and clinical decisions Imaging It is important erum Markers Decision Model Informed treatment decision then why do we ask the question? Molecular Markers

Processing Adapt Therapy 123 2346.. 65789 6578.

2 Role of imaging in cancer care Imaging as a system Filtering Reconstruction H 3C Detection Characterization Optimized Treatment Response Assessment HN N N N N Cu NH Contrast agent ignal acquisition Raw data Raw data Processing Adapt Therapy Analysis Data output Image Data RECIT Response ize Architecture Perfusion/blood flow Metabolism Diffusion Proliferation Hypoxia RECIT LD = 2.9 cm LD = 3.8 cm 31% increase PD

GIT metabolic response: ACRIN 6665 Treatment decisions for RT Compare

Regional response Pre-treatment Post-treatment treatment Imaging as a

N N N Cu NH Contrast agent ignal acquisition Raw data 123 2346.

3 GIT metabolic response: ACRIN 6665 Treatment decisions for RT Compare tumor characteristics prior to and after therapy Global response Regional response Pre-treatment Post-treatment treatment Imaging as a system Imaging as a source of data Filtering Reconstruction H 3C HN N N N N Cu NH Contrast agent ignal acquisition Raw data Raw data Analysis Processing Feature 1 Feature 2 Feature 3... Data output

Reducing the image to data Global assessment Detection, diagnosis, time point comparison Categorical classification Qualitative, semi-quantitative Human extracted quantitative data RECIT, ROI based

4 Reducing the image to data Global assessment Detection, diagnosis, time point comparison Categorical classification Qualitative, semi-quantitative Human extracted quantitative data RECIT, ROI based measurements Automated quantitative data Tumor volume segmentation Reducing the image to data Global assessment Detection, diagnosis, time point comparison Categorical classification Qualitative, semi-quantitative Human extracted quantitative data RECIT, ROI based measurements Automated quantitative data Tumor volume segmentation Qualitative Imaging Quantitative Imaging Qualitative Imaging Positive Ease of implementation Platform independent Observers can control for artifacts, orientation, and technique Challenges Dependence on presentation Inter and intra observer variability Limited dynamic range Bias (lack of feature independence) Training/credentialing challenge Effect of Threshold

Qualitative Imaging Results: Reader agreement and RR vs. O Model Fleiss Kappa RR Pred of O? RECIT 0.211 0.96 Unconstrained RECIT 0.

457 0.02 (c) Post-Chembo: Arterial phase (d) Post-Chembo: Delayed phase EAL 0.296 0.71 EAL 2D 0.458 0.

forward compatibility Often involves observer input (introduces variability) Implementation standards Adoption Interobserver Misclassifications by Case Using RECIT and WHO Criteria for Progressive

0 170. 86.67 47.50 90.91 150.0 109.0 194.1 123.5 66.67 116.91 Median RD, % 13.33 9.62 10.00 7.14 7.79 20.00 8.85 15.00 11.11 10.34 11.32 No. of 12 8 9 7 10 16 12 12 8 7 10.

5 Qualitative Imaging Results: Reader agreement and RR vs. O Model Fleiss Kappa RR Pred of O? RECIT Unconstrained RECIT N/A (a) Pre-Chembo: Arterial phase (b) Pre-Chembo: Delayed phase WHO D N/A (RECIT)*(%Nec) (Unconst RECIT)*(%Nec) (WHO)*(%Nec) (3D)*(%Nec) (c) Post-Chembo: Arterial phase (d) Post-Chembo: Delayed phase EAL EAL 2D Quantitative assessments Positive Un-biased Data, not presentation dependent In principal reduces variability Larger dynamic range Challenges Generalizability across platforms/systems Maintaining forward compatibility Often involves observer input (introduces variability) Implementation standards Adoption Interobserver Misclassifications by Case Using RECIT and WHO Criteria for Progressive Disease (RECIT > 20%, WHO > 25%) Observer Pair Measurement Method 1, 2 1, 3 1, 4 1, 5 2, 3 2, 4 2, 5 3, 4 3, 5 4, 5 Avera ge Unidimensional Minimum RD, % Maximum RD, % Median RD, % No. of misclassifications % of cases Bidimensional Minimum RD, % Maximum RD, % Median RD, % No. of misclassifications % of cases

Point pread Function Point pread Patient image Dependent on acquisition system and reconstruction Effects mapping of signals onto the image Impacts

Technology Evolution: 8 Channel Array upgrade Before correction original MR Before correction segmented MR After correction After correction segmented

6 Point pread Function Point pread Patient image Dependent on acquisition system and reconstruction Effects mapping of signals onto the image Impacts detection of boarders Impacts peak signal values Impacts relationship of signals between modalities hared signal data to accelerate reconstruction Technology Evolution: 8 Channel Array upgrade Before correction original MR Before correction segmented MR After correction After correction segmented MR pure image time filtered image ignal Intensity x48 Res Temporal Filtering Time (sec) Christos Davatzikos et al

7 Why do we ask the question? What is needed? Challenges To reliable quantitation Modest commercial demand for quantitative imaging Challenges to Conducting trials to show clinical effectiveness Lack of data demonstrating effectiveness of quantitative imaging Extraction of (quantitative) data from images that: Has dynamic range consistent with the biology Is generalizable urvives platform variability urvives system upgrades Can be implemented with minimal overhead

Conflicts of Interest

Analysis of Dependent Variables: Correlation and Simple Regression Zacariah Labby, PhD, DABR Asst. Prof. (CHS), Dept. of Human Oncology University of Wisconsin Madison Conflicts of Interest None to disclose