Prognostic Value of Tumor Hypoxia, as measured by 18F-FMISO Breath-Hold PET/CT, in NSCLC Memorial Sloan-Kettering Cancer Center S.A. Nehmeh, J. Schwartz, R. Schmidtlein, H. Schoder, A. Rimner, J. Chaft, M. Grkowski, J. Humm Fill in with your progress. You can use the milestone chart, data, discussion, etc. Make the story flow from upper left hand corner to lower right corner. Year 1 Goals: •Develop MC model for GE DSTE PET scanner. •Model dynamic FMISO PET data using MC. •Quantitate uncertainties in kinetic rate constants due to target motion. •Acquire 25 dynamic FMISO lung patients. Year 1 Accomplishments: •MC model for GE DSTE developed and validated. •Dynamic FMISO PET data has been modeled. •Uncertainties in kinetic rate constants due to target motion have been assessed. •Four FMISO NSCLC patients have been accrued. •Both Free-Breathing and Breath-Hold FDG PET data were acquired. Year 2 Goals: •Continue acquiring another 25 dynamic FMISO patients. •Assess repeatability of kinetic rate constants in sequential FMISO dynamic PET scans. •Assess the changes in FDG SUV between baseline and follow up scans, measured in Free-Breathing, and then in Breath-Hold PET images. Year 2 Accomplishment: • Manuscript on the uncertainties in kinetic rate constants due to target motion has been prepared. • Already acquired FMISO dynamic and FDG data being analyzed. • New protocol to incorporate a 2 nd clinical trial to improve patients accrual rate has been submitted and approved by IRB. Year 3 Goals: •Finish accruing patient data. •Assess repeatability of kinetic rate constants in sequential FMISO dynamic PET. •Compare changes in FDG SUV between baseline and follow up scans, measured in Free- Breathing, and then in Breath-Hold PET images. Year 4 Goals: N/A Year 5 Goals: •Correlate the presence of hypoxia with outcome and survival rate. •Assess the prognostic value of FDG using Breath-Hold PET images and compare it to that determined from Free- Breathing ones. MSKCC Year 1 Year 2 Year 3 Year 4 Year 5
Transcript
Prognostic Value of Tumor Hypoxia, as measured by 18F-FMISO Breath-Hold PET/CT, in NSCLC
Memorial Sloan-Kettering Cancer Center
S.A. Nehmeh, J. Schwartz, R. Schmidtlein, H. Schoder, A. Rimner, J. Chaft, M. Grkowski, J. Humm
Fill in with your progress. You can use themilestone chart, data, discussion, etc.Make the story flow from upper left handcorner to lower right corner.
Year 1 Goals:
•Develop MC model for GE DSTE PET scanner.•Model dynamic FMISO PET data using MC.•Quantitate uncertainties in kinetic rate constants due to target motion.•Acquire 25 dynamic FMISO lung patients.
Year 1 Accomplishments:
•MC model for GE DSTE developed and validated.•Dynamic FMISO PET data has been modeled.•Uncertainties in kinetic rate constants due to target motion have been assessed. •Four FMISO NSCLC patients have been accrued.•Both Free-Breathing and Breath-Hold FDG PET data were acquired.
Year 2 Goals:
•Continue acquiring another 25 dynamic FMISO patients.•Assess repeatability of kinetic rate constants in sequential FMISO dynamic PET scans.•Assess the changes in FDG SUV between baseline and follow up scans, measured in Free-Breathing, and then in Breath-Hold PET images.
Year 2 Accomplishment:• Manuscript on the uncertainties in
kinetic rate constants due to target motion has been prepared.
• Already acquired FMISO dynamic and FDG data being analyzed.
• New protocol to incorporate a 2nd clinical trial to improve patients accrual rate has been submitted and approved by IRB.
Year 3 Goals:
•Finish accruing patient data.•Assess repeatability of kinetic rate constants in sequential FMISO dynamic PET.•Compare changes in FDG SUV between baseline and follow up scans, measured in Free-Breathing, and then in Breath-Hold PET images.
Year 4 Goals: N/A
Year 5 Goals:
•Correlate the presence of hypoxia with outcome and survival rate.•Assess the prognostic value of FDG using Breath-Hold PET images and compare it to that determined from Free-Breathing ones.
MSKCC
Year 1 Year 2 Year 3 Year 4 Year 5
Prognostic Value of Tumor Hypoxia, as measured by 18F-FMISO Breath-Hold PET/CT, in NSCLC
Memorial Sloan-Kettering Cancer Center
S.A. Nehmeh, J. Schwartz, R. Schmidtlein, H. Schoder, A. Rimner, J. Chaft, M. Grkowski, J. L. Humm
• Developed and validated MC model for GE DSTE PET scanner to assess the uncertainties in kinetic analysis of dynamic FMISO PET.• Dynamic FMISO PET data of moving targets..
•Protocol has been extended to accrue patients from two clinical trials:
Trial-I: predictive value of FDG PET in NSCLC pts undergoing Neoadjuvant Chemotherapy.
Trial-II: predictive value of FDG PET in NSCLC pts undergoing radiotheray.
• Total of 54 NSCLC pts, 2 FMISO each at baseline.• Assess repeatability of kinetic analysis. • Correct for target motion in dynamic FMISO PET data. • Conduct kinetic analysis in both free-breathing and motion-corrected dynamic FMISO data sets.
• Developed software tool to correct for breathing-induced motion in dynamic PET data.
•
Year 3 Goals:
•Finish accruing patient data.•Assess repeatability of kinetic rate constants in sequential FMISO dynamic PET.•Compare changes in FDG SUV between baseline and follow up scans, measured in Free-Breathing, and then in Breath-Hold PET images.
Year 4 Goals: N/A
Year 5 Goals:
•Correlate the presence of hypoxia with outcome and survival rate.•Assess the prognostic value of FDG using Breath-Hold PET images and compare it to that determined from Free-Breathing ones.
Year 1 Year 2 Year 3Year 4 Year 5
Static Oscillating
Time Activity Curves of Static and Oscillating 2cm target
%Errors in kinetic rate constants due to target motion.
Discussions:
At early time frames, target motion results in bkgd spill in, and consequently in overestimation of K1.
Target motion results in underestimation of k3, thus of tumor hypoxia.
Less than 10% error in kinetic rate constants when target motion in the order of 1cm.
Results:
Results:
Prognostic Value of Tumor Hypoxia, as measured by 18F-FMISO Breath-Hold PET/CT, in NSCLC
Memorial Sloan-Kettering Cancer Center
S.A. Nehmeh, J. Schwartz, R. Schmidtlein, H. Schoder, A. Rimner, J. Chaft, M. Grkowski, J. L. Humm
• Developed and validated a MC model for GE DSTE PET scanner to assess the uncertainties in kinetic analysis of dynamic FMISO PET.
• Methods: GATE was used to model the GE DSTE.
Year 1
Year 2Project Aims:• To assess the prognostic value of hypoxia, as measured with 18FMISO PET in NSCLC.
• To correlate the extents of tumor hypoxia with treatment outcome.
• To assess whether BH-FMISO correlates better with outcome than FB-FMISO.
Scanning Mode 3D
DSTE Meas.Trans. Res.
Spec GATE Sim
Source @ 1cm 5.0 mm 5.3 mm 4.53 mmSource @10
cm5.6 mm 5.8 mm 5.57 mm
MSKCC GE DSTE •Scanning Mode 3D•Scatter Fraction: 35.1%•Specification: 37%•Peak NEC Rate: 87.07 kcps @ 12.18
%Errors in kinetic rate constants due to target motion.
Discussions:• At early time frames, target motion results in bkgd spill in, and consequently in overestimation of K1.
• Target motion results in underestimation of k3, thus of tumor hypoxia.
• Less than 10% error in kinetic rate constants when target motion in the order of 1cm.
Results:
Aim-I: Assessed uncertainties in kinetic analysis of dynamic FMISO PET. due to breathing motion.
Methods: • Spheres with d=2cm to 6cm in lung bkgd• Motion Amplitude: 1cm to 4cm.• Acquisition Mode: Dynamic• TAC: Both target and lung bkgd TACs deduced from FMISO NSCLC patient.
Aim-II: Assess the repeatability of kinetic analysis of FMISO PET, using both Free-Breathing (FB) and Breath-Hold (BH) data sets.
Methods: • Have incorporated a 2nd clinical trial to improve pt accrual• Developed software tool to compensate for breathing motion in dynamic PET images.• NSCLC patients• FDG + two dynamic FMISO PET at baseline (both FB and BH)• FDG at mid-treatment to evaluate response (both FB and BH)
Results:
Discussions:• Preliminary data show k3 to be repeatable to within 30%, in agreement with other studies. • The high percent error in K1 and Vb may be due to differences and irregularities in breathing motion.• Investigation of corresponding breathing signal may clarify reasons for the high %erros between the two FMISO scans.
CT FDG
FMISO
Tumor Time Activity curves from Baseline FMISO1 and FMISO2
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