A predictive multi-modal imaging marker for designing efficient and robust AD clinical trials
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1 A predictive multi-modal imaging marker for designing efficient and robust AD clinical trials Vamsi K. Ithapu Vikas Singh, Ozioma Okonkwo Sterling C. Johnson, Computer Sciences Biostatistics and Medical Informatics Medicine William S Middleton Memorial VA Hospital University of Wisconsin Madison Wisconsin Alzheimer s Disease Research Center (WADRC) November 21, 2014 (UW-Madison, WADRC) rdam for enrichment November 21, / 21
2 Landscape of AD Clinical Trials As of October 2014, Clinicaltrials.gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III (UW-Madison, WADRC) rdam for enrichment November 21, / 21
3 Landscape of AD Clinical Trials As of October 2014, Clinicaltrials.gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III More than 400 trials since 2002 Very little success (Cummings 2014) (UW-Madison, WADRC) rdam for enrichment November 21, / 21
4 Landscape of AD Clinical Trials As of October 2014, Clinicaltrials.gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III More than 400 trials since 2002 Very little success (Cummings 2014) Right Target, Right Drug, Right Stage (Sperling 2011)... why do we keep testing drugs aimed at the initial stages of the disease process in patients at the end-stage of the illness? (UW-Madison, WADRC) rdam for enrichment November 21, / 21
5 Landscape of AD Clinical Trials As of October 2014, Clinicaltrials.gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III More than 400 trials since 2002 Very little success (Cummings 2014) Right Target, Right Drug, Right Stage (Sperling 2011)... why do we keep testing drugs aimed at the initial stages of the disease process in patients at the end-stage of the illness? Choosing the Right Stage Mild Cognitive Impairment (MCI), Pre-symptomatic or Pre-clinical (UW-Madison, WADRC) rdam for enrichment November 21, / 21
6 Landscape of AD Clinical Trials Right Stage However, the annual conversion rate from MCI to AD is 3 20% (Mitchell 2009) (UW-Madison, WADRC) rdam for enrichment November 21, / 21
7 Landscape of AD Clinical Trials Right Stage However, the annual conversion rate from MCI to AD is 3 20% (Mitchell 2009) The effect of a drug (if there is one) is diminished. = Right stage is not right enough! (UW-Madison, WADRC) rdam for enrichment November 21, / 21
8 Landscape of AD Clinical Trials Right Stage However, the annual conversion rate from MCI to AD is 3 20% (Mitchell 2009) The effect of a drug (if there is one) is diminished. = Right stage is not right enough! According to FDA, enrichment entails to... the prospective use of any patient characteristic to select a study population in which detection of a drug effect (if one is in fact present) is more likely than it would in an unselected population. (UW-Madison, WADRC) rdam for enrichment November 21, / 21
9 Population enrichment continued Enrichment Cut-Off Healthy Discarded Included AD Worsening Disease (Enrichment Criterion) (UW-Madison, WADRC) rdam for enrichment November 21, / 21
10 Population enrichment continued Enrichment Cut-Off Healthy Discarded Included AD Worsening Disease (Enrichment Criterion) Good enrichment criterion High correlation with disease Practical enrichment criterion High predictive power (UW-Madison, WADRC) rdam for enrichment November 21, / 21
11 Population enrichment Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome (UW-Madison, WADRC) rdam for enrichment November 21, / 21
12 Population enrichment Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome Imaging based ROI summaries (Lorenzi 2010) Not voxel wise, too much information loss, Mostly use future time-point data (UW-Madison, WADRC) rdam for enrichment November 21, / 21
13 Population enrichment Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome Imaging based ROI summaries (Lorenzi 2010) Not voxel wise, too much information loss, Mostly use future time-point data Voxel wise summaries (Kohannim 2010) Mostly use future time-point data Designed for classification/regression problems, not trial enrichment (UW-Madison, WADRC) rdam for enrichment November 21, / 21
14 Population enrichment Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome Imaging based ROI summaries (Lorenzi 2010) Not voxel wise, too much information loss, Mostly use future time-point data Voxel wise summaries (Kohannim 2010) Mostly use future time-point data Designed for classification/regression problems, not trial enrichment Not explicitly designed to result in small sample (high power) trials (UW-Madison, WADRC) rdam for enrichment November 21, / 21
15 Designing a good enricher In this work, we construct a predictive multi-modal imaging marker that is explicitly designed for conducting efficient clinical trial and uses only baseline data (UW-Madison, WADRC) rdam for enrichment November 21, / 21
16 Designing a good enricher In this work, we construct a predictive multi-modal imaging marker that is explicitly designed for conducting efficient clinical trial and uses only baseline data Consider the setup of a clinical trial δ the change in trial outcome for the placebo group η the hypothesized decrease in the change of outcome due to the drug/treatment (UW-Madison, WADRC) rdam for enrichment November 21, / 21
17 Designing a good enricher In this work, we construct a predictive multi-modal imaging marker that is explicitly designed for conducting efficient clinical trial and uses only baseline data Consider the setup of a clinical trial δ the change in trial outcome for the placebo group η the hypothesized decrease in the change of outcome due to the drug/treatment To detect this drug effect of 1 η with statistical power of 1 β, Samples per arm, s = 2(Z α + Z 1 β )σ 2 (1 η) 2 δ 2 σ 2 pooled variance of the outcome (UW-Madison, WADRC) rdam for enrichment November 21, / 21
18 Designing a good enricher continued Samples per arm, s = 2(Z α + Z 1 β )σ 2 (1 η) 2 δ 2 s decreases if δ increases and σ 2 decreases (UW-Madison, WADRC) rdam for enrichment November 21, / 21
19 Designing a good enricher continued Samples per arm, s = 2(Z α + Z 1 β )σ 2 (1 η) 2 δ 2 s decreases if δ increases and σ 2 decreases H 0 = δ pb δ tr = 0 H 0 = δ pb δ tr = (1 η)δ Power = 1 β α/2 β α/2 Critical Value (UW-Madison, WADRC) rdam for enrichment November 21, / 21
20 Designing a good enricher continued Samples per arm, s = 2(Z α + Z 1 β )σ 2 (1 η) 2 δ 2 s decreases if δ increases and σ 2 decreases H 0 = δ pb δ tr = 0 H 0 = δ pb δ tr = (1 η)δ Power = 1 β α/2 β α/2 Critical Value (UW-Madison, WADRC) rdam for enrichment November 21, / 21
21 Designing a good enricher continued = we want the trial outcome to have large δ and small σ (UW-Madison, WADRC) rdam for enrichment November 21, / 21
22 Designing a good enricher continued = we want the trial outcome to have large δ and small σ = The trial population should be filtered with some enrichment criterion that results in large δ and small σ for any given outcome (UW-Madison, WADRC) rdam for enrichment November 21, / 21
23 Designing a good enricher continued = we want the trial outcome to have large δ and small σ = The trial population should be filtered with some enrichment criterion that results in large δ and small σ for any given outcome A good inclusion criterion should small variance on the trial population correlate very strongly to spectrum of dementia (unbiased) i.e. a minimum variance unbiased (MVUB) estimator is desired (UW-Madison, WADRC) rdam for enrichment November 21, / 21
24 randomized denoising autoencoders (rda) Novel machine learning model based on Stacked Denoising Autoencoders (Bengio 2009) (UW-Madison, WADRC) rdam for enrichment November 21, / 21
25 randomized denoising autoencoders (rda) Novel machine learning model based on Stacked Denoising Autoencoders (Bengio 2009) L-layered Stacked Denoising Autoencoder: Layer 1 Layer 2 Layer 3 Layer L Inputs Outputs (UW-Madison, WADRC) rdam for enrichment November 21, / 21
26 randomized denoising autoencoders (rda) (UW-Madison, WADRC) rdam for enrichment November 21, / 21
27 randomized denoising autoencoders (rda) T networks L layered SDA T outputs Block 1 L layered SDA L layered SDA (UW-Madison, WADRC) rdam for enrichment November 21, / 21
28 randomized denoising autoencoders (rda) T networks L layered SDA T outputs Block 1 L layered SDA L layered SDA Block 2 T networks L layered SDA L layered SDA T outputs L layered SDA (UW-Madison, WADRC) rdam for enrichment November 21, / 21
29 randomized denoising autoencoders (rda) T networks L layered SDA T outputs Block 1 L layered SDA L layered SDA f 1 Block 2 T networks L layered SDA L layered SDA L layered SDA T outputs f 2 f 1 f B f f f 3 6 2f4 f 5 Block B T networks L layered SDA L layered SDA L layered SDA T outputs f B (UW-Madison, WADRC) rdam for enrichment November 21, / 21
30 randomized denoising autoencoders (rda) T networks L layered SDA T outputs Block 1 L layered SDA L layered SDA Block 2 T networks L layered SDA T outputs L layered SDA L layered SDA Linear Combination Final Output T networks L layered SDA T outputs Block B L layered SDA L layered SDA (UW-Madison, WADRC) rdam for enrichment November 21, / 21
31 randomized denoising autoencoder (rda) marker (rdam) SDAs model complex concepts (Bengio 2009) Predictions are unbiased Averaging uncorrelated data reduces variance = rda outputs ( [0, 1]) are MVUB estimators. (UW-Madison, WADRC) rdam for enrichment November 21, / 21
32 randomized denoising autoencoder (rda) marker (rdam) SDAs model complex concepts (Bengio 2009) Predictions are unbiased Averaging uncorrelated data reduces variance = rda outputs ( [0, 1]) are MVUB estimators. rda for enrichment Train multimodal rda: For each input imaging modality Train a rda on AD (labeled 0) and CN (labeled 1) subjects Combine the outputs across all modalities Predict the trained model on MCI rda marker (rdam) Choose a cut-off t [0, 1], and filter out subjects with rdam > t (UW-Madison, WADRC) rdam for enrichment November 21, / 21
33 Experiments Data (ADNI2) T1 MRI and PET (FDG, florbetapir) of 516 subjects (median age 72.46, 38% female) 101 AD, 148 CN, 131 early MCI, 136 late MCI (at baseline) 174 FH positive, 141 APOEe4 positive Standard SPM8 pre-processing on all images (UW-Madison, WADRC) rdam for enrichment November 21, / 21
34 Experiments Data (ADNI2) T1 MRI and PET (FDG, florbetapir) of 516 subjects (median age 72.46, 38% female) 101 AD, 148 CN, 131 early MCI, 136 late MCI (at baseline) 174 FH positive, 141 APOEe4 positive Standard SPM8 pre-processing on all images Evaluations Predictive power of baseline rdam Sample sizes (by fixing the desired power) using baseline rdam vs. alternate enrichers (UW-Madison, WADRC) rdam for enrichment November 21, / 21
35 Predictive power of baseline rdam Correlations Spearman correlation coefficient (and p-value) of baseline rdam with change in other markers from baseline to 12 and 24 months Marker 12m 24m MMSE , p = , p = ADAS , p = , p < 10 4 MOCA , p > , p = 10 4 RAVLT , p = , p = PsyMEM , p < , p = HippoVol , p , p 10 4 CDR-SB , p , p 10 4 DXConv 1 > 20, p 10 4 > 20, p ANOVA test results are reported since this variable is categorical (UW-Madison, WADRC) rdam for enrichment November 21, / 21
36 Predictive power of baseline rdam Correlations Spearman correlation coefficient (and p-value) of baseline rdam with change in other markers from baseline to 12 and 24 months Marker 12m 24m MMSE , p = , p = ADAS , p = , p < 10 4 MOCA , p > , p = 10 4 RAVLT , p = , p = PsyMEM , p < , p = HippoVol , p , p 10 4 CDR-SB , p , p 10 4 DXConv 1 > 20, p 10 4 > 20, p ANOVA test results are reported since this variable is categorical (UW-Madison, WADRC) rdam for enrichment November 21, / 21
37 Predictive power of baseline rdam Correlations Spearman correlation coefficient (and p-value) of baseline rdam with change in other markers from baseline to 12 and 24 months Marker 12m 24m MMSE , p = , p = ADAS , p = , p < 10 4 MOCA , p > , p = 10 4 RAVLT , p = , p = PsyMEM , p < , p = HippoVol , p , p 10 4 CDR-SB , p , p 10 4 DXConv 1 > 20, p 10 4 > 20, p ANOVA test results are reported since this variable is categorical (UW-Madison, WADRC) rdam for enrichment November 21, / 21
38 Predictive power of baseline rdam Correlations Spearman correlation coefficient (and p-value) of baseline rdam with change in other markers from baseline to 12 and 24 months Marker 12m 24m MMSE , p = , p = ADAS , p = , p < 10 4 MOCA , p > , p = 10 4 RAVLT , p = , p = PsyMEM , p < , p = HippoVol , p , p 10 4 CDR-SB , p , p 10 4 DXConv 1 > 20, p 10 4 > 20, p ANOVA test results are reported since this variable is categorical (UW-Madison, WADRC) rdam for enrichment November 21, / 21
39 Predictive power of baseline rdam Correlations Spearman correlation coefficient (and p-value) of baseline rdam with change in other markers from baseline to 12 and 24 months Marker 12m 24m MMSE , p = , p = ADAS , p = , p < 10 4 MOCA , p > , p = 10 4 RAVLT , p = , p = PsyMEM , p < , p = HippoVol , p , p 10 4 CDR-SB , p , p 10 4 DXConv 1 > 20, p 10 4 > 20, p 10 4 In general, very strong correlations across all markers 1 ANOVA test results are reported since this variable is categorical (UW-Madison, WADRC) rdam for enrichment November 21, / 21
40 Predictive power of baseline rdam Change plots Mean longitudinal change in MMSE, ADAS-Cog, MOCA and RAVLT vs. baseline rdam enrichment cut-off t [0, 1] (0 AD, 1 healthy) MMSE Change m 24m rdam Cut Off 1 ADAS Change m 24m rdam Cut Off 8 MOCA Change m 24m rdam Cut Off RAVLT Change m 24m rdam Cut Off (UW-Madison, WADRC) rdam for enrichment November 21, / 21
41 Predictive power of baseline rdam Change plots Mean longitudinal change in Hippo. Vol, PsyMEM, CDR-SB and DxConv vs. baseline rdam enrichment cut-off t [0, 1] (0 AD, 1 healthy) PsychMEM Change m 24m rdam Cut Off 5 Hippo Vol Change m 24m rdam Cut Off CDR Change 3 2 DX Change m 24m rdam Cut Off m 24m rdam Cut Off (UW-Madison, WADRC) rdam for enrichment November 21, / 21
42 Baseline rdam for enrichment Sample sizes per arm using rdam enrichment vs. no enrichment on several outcomes (at 80% power and drug effect of 0.25) Outcome Bottom 20% Bottom 25% Bottom 33% Bottom 50% None measure rdam< 0.41 rdam< 0.46 rdam< 0.52 rdam< 0.65 MMSE ADAS > >2000 >2000 MOCA > RAVLT > >2000 >2000 PsyMEM > PsyEF > 2000 > 2000 > 2000 > 2000 > 2000 HippoVol > CDR-SB DxConv (UW-Madison, WADRC) rdam for enrichment November 21, / 21
43 Baseline rdam for enrichment Sample sizes per arm using rdam enrichment vs. no enrichment on several outcomes (at 80% power and drug effect of 0.25) Outcome Bottom 20% Bottom 25% Bottom 33% Bottom 50% None measure rdam< 0.41 rdam< 0.46 rdam< 0.52 rdam< 0.65 MMSE ADAS > >2000 >2000 MOCA > RAVLT > >2000 >2000 PsyMEM > PsyEF > 2000 > 2000 > 2000 > 2000 > 2000 HippoVol > CDR-SB DxConv (UW-Madison, WADRC) rdam for enrichment November 21, / 21
44 Baseline rdam for enrichment Sample sizes per arm using rdam enrichment vs. no enrichment on several outcomes (at 80% power and drug effect of 0.25) Outcome Bottom 20% Bottom 25% Bottom 33% Bottom 50% None measure rdam< 0.41 rdam< 0.46 rdam< 0.52 rdam< 0.65 MMSE ADAS > >2000 >2000 MOCA > RAVLT > >2000 >2000 PsyMEM > PsyEF > 2000 > 2000 > 2000 > 2000 > 2000 HippoVol > CDR-SB DxConv (UW-Madison, WADRC) rdam for enrichment November 21, / 21
45 Baseline rdam for enrichment Sample sizes per arm using rdam enrichment vs. no enrichment on several outcomes (at 80% power and drug effect of 0.25) Outcome Bottom 20% Bottom 25% Bottom 33% Bottom 50% None measure rdam< 0.41 rdam< 0.46 rdam< 0.52 rdam< 0.65 MMSE ADAS > >2000 >2000 MOCA > RAVLT > >2000 >2000 PsyMEM > PsyEF > 2000 > 2000 > 2000 > 2000 > 2000 HippoVol > CDR-SB DxConv (UW-Madison, WADRC) rdam for enrichment November 21, / 21
46 Baseline rdam for enrichment Sample sizes per arm using rdam enrichment vs. no enrichment on several outcomes (at 80% power and drug effect of 0.25) Outcome Bottom 20% Bottom 25% Bottom 33% Bottom 50% None measure rdam< 0.41 rdam< 0.46 rdam< 0.52 rdam< 0.65 MMSE ADAS > >2000 >2000 MOCA > RAVLT > >2000 >2000 PsyMEM > PsyEF > 2000 > 2000 > 2000 > 2000 > 2000 HippoVol > CDR-SB DxConv (UW-Madison, WADRC) rdam for enrichment November 21, / 21
47 Baseline rdam for enrichment Sample sizes per arm using rdam enrichment vs. no enrichment on several outcomes (at 80% power and drug effect of 0.25) Outcome Bottom 20% Bottom 25% Bottom 33% Bottom 50% None measure rdam< 0.41 rdam< 0.46 rdam< 0.52 rdam< 0.65 MMSE ADAS > >2000 >2000 MOCA > RAVLT > >2000 >2000 PsyMEM > PsyEF > 2000 > 2000 > 2000 > 2000 > 2000 HippoVol > CDR-SB DxConv About 5 times reduction with 1-in-5, 1-in-4 enrichment (UW-Madison, WADRC) rdam for enrichment November 21, / 21
48 Baseline rdam enrichment vs. alternate enrichers Sample sizes per arm using rdam vs. other imaging-derived enrichers (at 80% power and drug effect of 0.25) Sample Outcome measure enricher MMSE ADAS MOCA RAVLT PsyMEM HipVol CDR-SB DxConv HipVol 500 > > FDG > AV > > FAH 296 > > MKLm rdam MKLm is the current state-of-the-art based on SVMs (UW-Madison, WADRC) rdam for enrichment November 21, / 21
49 Baseline rdam enrichment vs. alternate enrichers Sample sizes per arm using rdam vs. other imaging-derived enrichers (at 80% power and drug effect of 0.25) Sample Outcome measure enricher MMSE ADAS MOCA RAVLT PsyMEM HipVol CDR-SB DxConv HipVol 500 > > FDG > AV > > FAH 296 > > MKLm rdam MKLm is the current state-of-the-art based on SVMs (UW-Madison, WADRC) rdam for enrichment November 21, / 21
50 Baseline rdam enrichment vs. alternate enrichers Sample sizes per arm using rdam vs. other imaging-derived enrichers (at 80% power and drug effect of 0.25) Sample Outcome measure enricher MMSE ADAS MOCA RAVLT PsyMEM HipVol CDR-SB DxConv HipVol 500 > > FDG > AV > > FAH 296 > > MKLm rdam rdam has smallest estimates across all outcomes 3 MKLm is the current state-of-the-art based on SVMs (UW-Madison, WADRC) rdam for enrichment November 21, / 21
51 Baseline rdam enrichment vs. alternate enrichers Sample sizes per arm using rdam vs. other imaging-derived enrichers (at 80% power and drug effect of 0.25) Sample Outcome measure enricher MMSE ADAS MOCA RAVLT PsyMEM HipVol CDR-SB DxConv HipVol 500 > > FDG > AV > > FAH 296 > > MKLm rdam MKLm is the current state-of-the-art based on SVMs (UW-Madison, WADRC) rdam for enrichment November 21, / 21
52 Conclusions & Future Work Existing markers are not explicitly designed for clinical trials Novel learning model (rda) and a multimodal disease marker (rdam) that predicts future decline confidently Smaller sample sizes (2 5) compared to alternate enrichers < 320 with 1-in-4 enrichment (MMSE, CDR-SB, DxConv outcomes) RAVLT and ADAS; > 1600 using ROIs, 775 and 591 using rdam DxConv; > 400 using ROIs, 230 using rdam Project webpage (UW-Madison, WADRC) rdam for enrichment November 21, / 21
53 Conclusions & Future Work Existing markers are not explicitly designed for clinical trials Novel learning model (rda) and a multimodal disease marker (rdam) that predicts future decline confidently Smaller sample sizes (2 5) compared to alternate enrichers < 320 with 1-in-4 enrichment (MMSE, CDR-SB, DxConv outcomes) RAVLT and ADAS; > 1600 using ROIs, 775 and 591 using rdam DxConv; > 400 using ROIs, 230 using rdam Project webpage Future directions Improving rda (model complexity, architecture etc.) Improving rdam by using co-variate information like age, gender, education and CSF levels, genetic data etc (UW-Madison, WADRC) rdam for enrichment November 21, / 21
54 Conclusions & Future Work Existing markers are not explicitly designed for clinical trials Novel learning model (rda) and a multimodal disease marker (rdam) that predicts future decline confidently Smaller sample sizes (2 5) compared to alternate enrichers < 320 with 1-in-4 enrichment (MMSE, CDR-SB, DxConv outcomes) RAVLT and ADAS; > 1600 using ROIs, 775 and 591 using rdam DxConv; > 400 using ROIs, 230 using rdam Project webpage Future directions Improving rda (model complexity, architecture etc.) Improving rdam by using co-variate information like age, gender, education and CSF levels, genetic data etc Thank you. Questions? (UW-Madison, WADRC) rdam for enrichment November 21, / 21
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