Oral presentation at ISMRM 2015 Annual Meeting in Toronto, Canada.

1. Optimization of DCE-MRI measurement
parameters for predicting response to neadjuvant
chemotherapy by breast cancer subtype
ISMRM
Wen Li PhD
University of California San Francisco
7/7/2016

2. Wen Li:
I have no financial interests or relationships to disclose with regard to the
subject matter of this presentation.
Declaration of
Financial Interests or Relationships

3. Purpose
 To assess whether breast functional tumor volume (FTV)
measurements from DCE-MRI optimized by breast cancer subtype,
improves prediction of recurrence-free survival (RFS) and pathologic
complete response (pCR) following neoadjuvant (pre-operative)
chemotherapy (NACT)
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4. Background
 Breast cancer is a heterogeneous disease1
 By immunohistochemistry (IHC) expression:
• Hormone receptor (HR) -- ER/PR
• Human epidermal growth factor
receptor 2 (HER2)
• TN: triple negative (HR-HER2-)
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70%
15%
15%
HR+HER2- HER2+ TN
1Perou et al. 2000

5. Background – DCE-MRI in neoadjuvant chemotherapy
Breast MRI is a sensitive measure of tumor change over
treatment
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Surgery
MRI1MRI2 MRI3 MRI4
AC Taxane
(a)Pretreatment
(b)1 cycle of AC
(c)Post-chemo
(a) MRI1 (b) MRI2 (c) MRI4

6. Background – functional tumor volume measurement
 Early percent enhancement (PE)
 Signal enhancement ratio (SER)
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MRISignal
t1 t2t0
S2
S1
∆S1 ∆S2
S0
injection
Plateau
0.9≤SER≤1.1
SER map
Washout
SER>1.1
Gradual
SER<0.9
PE =
ΔS1
S0
× 100%
SER =
ΔS1
ΔS2

7. Background functional tumor volume measurement
 Functional tumor volume is defined by PE and SER thresholds (PEt / SERt)
 Default: PEt = 70% / SERt = 0
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PEt=30% / SERt=0
FTV = 53 cc
PEt=70% / SERt=0
FTV = 41 cc
PEt=110% / SERt=0
FTV = 22 cc

8. Study goal
To determine optimal PE/SER thresholds for prediction of clinical outcomes:
 Recurrence-free survival (RFS) – long term survival
 Pathologic complete response (pCR) – after treatment
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9. 33%
23%
17%
27%
HR+HER2-
HER2+
TN
Unknown
Patient characteristics – UCSF pilot study
 Pilot study proceeded ACRIN 6657
 64 patients enrolled 1995 − 2002 with 5-year follow up
 Subtype
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Surgery
MRI1MRI2 MRI3 MRI4
AC Taxane
: n=21
: n=15
: n=11
: n=17

10. Breast MRI Protocol – UCSF pilot study
 1.5 T MRI scanner
 Ipsilateral sagittal contrast-enhanced sequence
• 3D T1-weighted with fat suppression
• TR = 8 ms / TE = 4.2 ms
• Acquisition matrix: 256 × 192 × 60
• Spatial resolution: 0.7 × 0.94 × 2.0 mm3
• Single dose gadolinium contrast agent injection (0.1 mmol/kg body weight)
• 3 DCE time points: pre-contrast (t = 0) , early (t = 2.5 min), and late (t = 7.5
min)
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11. Methods − Prediction of recurrence-free survival (RFS)
 Statistics model: Cox proportional hazard model
 Hazard ratio (CI, p)
 FTV predictors:
• Early treatment volume percent change:
• Final volume percent change:
 Adjust PEt and SERt in the ranges of:
• PEt: 30 − 200%
• SERt: 0 − 2
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DFTV2
=
FTV2
-FTV1
FTV1
´100%
DFTVf
=
FTVf
-FTV1
FTV1
´100%

12. Results − RFS prediction in full cohort for ΔFTVf Predictor
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PEt(%)
SERt
Hazard ratio
PEt(%)
SERt
P-value
Hazardratio
0.8
1
1.2
1.4
1.6
Default Optimized
P = 0.006 P < 0.001

13. Results: RFS prediction in full cohort ΔFTV2 Predictor
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PEt(%)
SERt
Hazard ratio
PEt(%)
SERt
P-value
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
Default Optimized
Hazardratio
P = 0.88 P = 0.0008

14. Results: Hazard ratio by subtype for ΔFTV2 predictor
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0
0.5
1
1.5
2
Full cohort HR+HER2- HER2+ TN
Default
Optimized

15. Results: Hazard ratio by subtype for ΔFTVf predictor
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16. Patient A, female, 37 y, HR+HER2-, pretreatment
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SER > 1.75
1.75 ≥ SER ≥ 1.3
1.3 ≥ SER > 1.0
1.0 ≥ SER > 0.9
SER < 0.9
Pre-contrast
Early
Late
Left -- default
PEt = 70% / SERt = 0
FTV = 43.4 cc
Right -- optimized
PEt = 100% / SERt = 1.0
FTV = 20.6 cc

17. Patient B, female, 50 y, triple negative, pretreatment
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SER > 1.75
1.75 ≥ SER ≥ 1.3
1.3 ≥ SER > 1.0
1.0 ≥ SER > 0.9
SER < 0.9
Pre-contrast
Early
Late
Left -- default
PEt = 70% / SERt = 0
FTV = 30.7 cc
Right -- optimized
PEt = 100% / SERt = 1.0
FTV = 11.6 cc

18. Methods − Prediction of pathologic complete response (pCR)
 Statistical evaluation: area under the curve (AUC) of receiver operating curve
(ROC)
 Impact of pCR on prognosis after neoadjuvant chemotherapy differs in breast
cancer subtype2
 FTV predictor:
 Adjust PEt and SERt in the ranges of:
• PEt: 30 − 200%
• SERt: 0 − 2
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DFTV2
=
FTV2
-FTV1
FTV1
´100%
2Minckwitz et al. 2012

19. Results − AUC prediction of pCR by subtype for ΔFTV2
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20. Conclusions
 This retrospective study showed that predictive value of FTV in breast MRI
was dependent on choice of PE/SER thresholds
 Prediction profiles differed by breast cancer subtype
 Although the study was image acquisition and subtype specific, the framework
can be extended to other DCE-MRI acquisitions and breast cancer
categorizations
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21. Future work
 Further develop the optimization model using the ACRIN 6657 cohort
 Apply the subtype-specific optimized PE/SER thresholds prospectively in the
on-going I-SPY 2 clinical trial for better prediction of treatment response to
neoadjuvant chemotherapy
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22. Acknowledgements
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Patients and their families
Funding:
NIH/NCI R01 CA069587 & CA132870
Susan G. Komen SAC110017
Breast MRI group at UCSF:
PIs: Nola M. Hylton PhD
Laura J. Esserman MD
Statistician: John Kornak PhD
Wei-Ching Lo Margarita Watkins Evelyn Proctor
Ella Jones Jessica Gibbs Roy Harnish
David Newitt Krysta Banfield
Lisa Wilmes Roxana Dhada