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quantifying the value of volumetric image analysis to pharma
Mozley PD, et al.
Merck Research Laboratories
Problem Statement: General
estimated costs $0.8 – 1.7BB
www.inovation.org
Problem Statement: Specific
paucity of evidence that CT imaging adds value
gap: signal processing– signals are becoming
progressively brilliant– analysis lags behind
Volumetric Image Analysis (VIA)
hypothesis: better quantification techniques will add value
• improve patient care• decrease number of subjects per trial• shorten time-on-study per subject• reduce development times• speed delivery of new treatments
pharma’s qualification process
assess feasibility, time & effort; if go, then characterize precision & accuracy; if go, then estimate value
– retrospectively– prospectively
• small studies at a few premier centers• options in mega, multi-national settings
value
defined as the ability to have a unique impact on patient management
precision: Methods
sample: 2 phantom sets + 10 clinical cases target lesions: measurable lung tumors CT scans: 5 mm RI ( QIBA UPICT
protocol) image analysts: 7 independent teams
– provided with one screenshot per tumor on one slice
accuracy: Results +/- 2.7 to 12% @ 5 mm
volumes spheres with diameters of 40, 20, & 10 mm corresponding volumes = 33.5, 4.2, and 0.5 mL. Courtesy of Petrick N, et al. FDA
precision: Results
image sets per subject: mean = 7.2 +/- 1.5 target lesion volumes: mean = 117 +/- 136 mL time on trial: mean = 283 +/- 72 days
Left Panel: largest = 413 mL; Right: smallest = 1.8 mL
precision: Results for Merck clinical only
volume Merck & partners
mean %relvar
median %relvar
90%CI 95% CImax discord
intra-rater
5.0 4.3 10.3 12.5 17.0
inter-rater
5.3 4.4 10.8 15.7 19.1
precision: Results for 7 teams
agreement between 7 teams
CV 95% CI
absolute values 25% 59%
% change 12.4% 20%
precision for change: iota values
auto-SLD VIA
overall 0.73 0.95
minimum 0.58 0.86
maximum 0.96 0.99
Conclusion: agreement is higher for volume than for SLD
Conclusions:
• biases between teams seem highly consistent for both volume & SLD
• CV for absolute volume too high in this setting
• CV for change OK• CV higher for SLD than for volume • Therefore, GO to next step
Response Rate: volume versus SLD
p=0.002, log rank test
02
04
06
08
01
00
Time on Trial [days]
% S
urv
iva
l (P
R e
nd
po
int)
42 84 126 168 210 252 294 336 378
SLD.Averagevol.Average
PFS: volume versus SLD
p=0.039, log rank test
02
04
06
08
01
00
Time on Trial [days]
% S
urv
iva
l (P
D e
nd
po
int)
42 84 126 168 210 252 294 336 378
SLD.Averagevol.Average
Conclusions: precision
volumes might be – more precise than SLDs as a basis for
RECIST
– more sensitive indicators of response
– cost effective Therefore, GO to next step
next step: retrospective analysis Phase III
RANDOMI ZE
SOC drug 1SOC = q 21 d
SOC drug 2
+ placebo p.o. x 14 dn = 123
+ MK p.o. x 14 dn = 125
Can VIA add value?
Reduce the number of subjects who never meet criteria for PR or PD?
Decrease the number of subjects who come off trial because of New Lesions?
Bring trials to closure faster?
Methods: image re-analysis
selection of target lesions by independent radiologists
automatic edge detection algorithm manual revision of edges certification of final boundaries by radiologists automatic computation:
– 3D tumor volume [mm3]– 1D longest diameter greatest distance [mm]
between any two in-plane pixels on any slice in the stack of tomographic images representing the target
Methods (continued)
outcome measures– auto-SLD sum of longest diameters of all target
lesions– volume sum of corresponding tumor volumes
endpoints– Objective Response Rate (ORR) number of subjects
with a Best Overall Response (BOR) of PR or CR divided by the total number of subjects
– Progression Free Survival (PFS) time from first drug dose until PD based on change in tumor mass or the appearance of new metastases
Methods (continued)
statistical analysis– 3D versus 1D in all subjects– MK arm versus placebo arm
categorical variables: RECIST versus “Enhanced RECIST” – Partial Response (PR) = decrease of >30% from baseline– Progressive Disease (PD) = increase of >20% from nadir
continuous variables– median change in auto-SLD or volume at each time-point
for whole groups
Results: Objective Response Rates
0.0
0.2
0.4
0.6
0.8
1.0
% R
each
ing
Par
tial R
espo
nse
0 100 200 300 400
BRR (days)
SLD
Volumetric
Survival Plot
Log-Rank
Wilcoxon
Test
22.6529
26.5519
ChiSquare
1
1
DF
<.0001*
<.0001*
Prob>ChiSq
Tests Between Groups
all 184 patients
Kaplan-Meier analysis shows that VIA is more sensitive than auto-SLD for detecting PR
0.0
0.2
0.4
0.6
0.8
1.0
% R
each
ing
Par
tial R
espo
nse
0 42 84 126 168 210 252 294 336 378
Time on Trial (days)
SLD
Volumetric
Survival Plot
Results: Objective Response Rates
auto-SLD 23.9%
VIA 41.8%
time to Partial Response (PR)
Results: VIA detects PR sooner than auto-SLD– MK-arm: 22.2 days, p-value = 0.0002– placebo-arm: 29.5 days, p-value < 0.0001
MK (n = 87) Placebo (n = 97)
auto-SLD[days]
VIA[days]
auto-SLD[days]
VIA[days]
mean 92.2 69.9 104.8 75.3
median 83.0 49.0 86.0 47.0
Conclusions: Response Rates
VIA is more sensitive than auto-SLD VIA confirms favorable responses sooner
than auto-SLD the differences are potentially meaningful?
Results for PFS: auto-SLD v VIA
0.0
0.2
0.4
0.6
0.8
1.0
% R
each
ing
Par
tial R
espo
nse
0 100 200 300 400
BRR (days)
SLD
Volumetric
Survival Plot
0.0
0.2
0.4
0.6
0.8
1.0
% R
each
ing
Pro
gres
sive
Dis
ease
0 42 84 126 168 210 252 294 336 378
Time on Trial (days)
SLD
Volumetric
Survival Plot
Log-Rank
Wilcoxon
Test
11.5214
7.3709
ChiSquare
1
1
DF
0.0007*
0.0066*
Prob>ChiSq
Tests Between Groups
VIA is more sensitive than auto-SLD for patients with longer times on trial
Stable Disease as Best Overall Response
auto-SLD: 71.2% of all subjects came off trial without target lesions ever meeting radiological criteria for PR or PD– 35.1% were re-categorized by VIA
VIA: 50.5%– 8.8% were re-categorized by auto-SLD
Conclusion: VIA results in fewer patients being right-censored
change = PD before new lesions?
auto-SLD VIA conclusion
<20% before new lesions 6.0% 9.2%
loss for quants
>20% before new lesions 16.8% 38.0%
win for quants
ties between quants & new lesions 10.3% 5.4%
no added value
no new lesions AND no >20% 68.3% 48.9%
no information
extrapolation to a successful trial
decrease sample size by ~20% save ~ US$2 MM in external cash burn for
conventional image management & analysis services
cost ~$300,000 to $500,000 USD in extra cash burn for VIA
speed the conclusion of clinical trials
Conclusions
VIA is more sensitive than auto-SLD in some contexts
quantification is sometimes more sensitive than new lesions for assessing PD
VIA could shorten trials advanced NSCLC is a context in which VIA
could add value– to clinical trials– to a few, highly selected patients
