Advanced quantification in oncology PET

Transcription

1 Advanced quantification in oncology PET Irène Buvat IMNC UMR 8165 CNRS Paris 11 University Orsay, France ISSSMA June 3rd

2 Two steps Static imaging Dynamic imaging Radiotracer concentration (kbq/ ml) Glucose metabolism Metabolically active tumor volume etc ISSSMA June 3 rd

3 Quantification issues in oncology PET Tumor segmentation Identification of indices that best characterize the tumor in a specific context Interpretation of tumor changes during therapy Understanding the relationship between macroscopic parameters (from PET images) and microscopic tumor features ISSSMA June 3 rd

4 Quantification issues in oncology PET Tumor segmentation Identification of indices that best characterize the tumor in a specific context Interpretation of tumor changes during therapy Understanding the relationship between macroscopic parameters (from PET images) and microscopic tumor features ISSSMA June 3 rd

5 Current quantification in oncology PET Tracer uptake (kbq/ml) SUV (Standardized Uptake Value) = Tracer uptake Injected activity / patient weight SUV ~ metabolic activity of tumor cells ISSSMA June 3 rd

6 Comparing 2 PET scans : current approach SUV=10 SUV=5 SUV=2 SUV=5 PET1 ~12 weeks PET2 Need to identify and possibly delineate the tumors Each tumor = 1 single SUV Change compared to an empitical threshold (provided in recommendations such as EORTC, PERCIST) Tedious when there are many tumor sites ISSSMA June 3 rd

7 A novel parametric imaging approach Goal : Get an objective voxel-based comparison of 2 PET/ CT scans PET1 PET2 ~ 12 weeks ISSSMA June 3 rd

8 Main steps 1. PET image registration based on the CT associated with the PET scans PET1 PET2 2. Voxel-based subtraction of the 2 image volumes PET1 - T 21 = PET2 PET1-PET2 3. Identification of voxels in which SUV significantly changed between the 2 scans using a biparametric analysis ISSSMA June 3 rd

9 Step 1 VOI selection PET1 PET2 CT1 CT2 Identification of the transformation needed to realign the 2 CT CT2 T 21 (rigid transform using Block Matching) Registration of the PET volumes using the T 21 transformation CT2 T 21 realigned with PET2 PET1 ISSSMA June 3 rd

10 Step 2 Subtraction of the 2 realigned PET scans T 21 {PET2/CT2} - PET1/CT1 = T 21 {PET2} PET1/CT1 Each point corresponds to a voxel ISSSMA June 3 rd

11 Step 3 Identification of the significant tumor changes in the 2D-space by solving a Gaussian mixture model x i PET1(i)-PET2(i) PET1(i) mixture density K Σ f(x i θ) = p k φ(x i µ k,σ k ) k=1 2D Gaussian density mixture parameters θ : vector of parameters (p 1, p K, µ 1,.µ K, Σ 1, Σ K ) ISSSMA June 3 rd

12 Step 3: results Nb of voxels Background Tumor voxels Physiological changes [PET1] SUV ΔSUV 0-3 Parametric image ΔV: volume with a significant change ΔSUV: change magnitude -6 ISSSMA June 3 rd

13 Example Identification of small tumor changes (lung cancer) +1.3 Progressive* PET1 T 21 {PET2} T 21 {PET2} - PET1 after solving the GMM PET PET1 T 21 {PET2} T 21 {PET2} - PET1 after solving the GMM PET3 ISSSMA June 3 rd

14 Clinical validation : 28 patients with metastatic colorectal cancer 78 tumors with 2 PET/CT (baseline and 14 days after starting treatment) NPV PPV Sensitivity* Specificity EORTC 91% 38% 85% 52% PI 100% 43% 100% 53% * for detecting lesions All tumors identified as progressive tumors at D14 were confirmed as such 6 to 8 weeks after based on CT (RECIST criteria) Among the 14 tumors identified as progressive tumors by RECIST criteria, 12 were identified as such at D14 using PI while only 2 were identified using EORTC criteria (SUVmax) Necib et al, J Nucl Med 2011 ISSSMA June 3 rd

15 Comparing more than 2 PET/CT scans Longitudinal study Problem : characterize the tumor changes No method, each scan is usually compared only to the previous one time (weeks) ISSSMA June 3 rd

16 A parametric imaging solution First step: PET image registration based on the associated CT Time (weeks) Use of the transformation identified based on the CT to register the PET scans T 31 T 21 ISSSMA June 3 rd

17 Model : a factor analysis model K SUV ( i, t ) = Σ I k ( i ). f k ( t ) + ε k ( t ) k = 1 SUV units voxel i SUV time I 1 (i). + I 2 (i). + I 3 (i). Stable uptake over time f 1 (t) t t Decreasing uptake over time f 2 (t) t Increasing uptake over time f 3 (t) ISSSMA June 3 rd

18 Solving the model K SUV ( i, t ) = Σ I k ( i ). f k ( t ) + ε k ( t ) k = 1 Priors : - Non-negative I k (i) coefficients - Non-negative f k (t) values - In each voxel, the variance of the voxel value is roughly proportional to the mean Iterative identification of I k (i) et f k (t) (Buvat et al Phys Med Biol 1998) using a Correspondence Analysis followed by an oblique rotation of the orthogonal eigenvectors ISSSMA June 3 rd

19 Sample results Lung cancer patient with 5 PET/CT scans Normalized SUV weeks ISSSMA June 3 rd

20 Why is such an approach useful? Heterogeneous tumor responses can be easily identified Normalized SUV weeks ISSSMA June 3 rd

21 Sample results: early detection of tumor recurrence (1) T1 T1 2 cyc-4 cyc-3 cyc-no cycle T2 2 cyc - 4 cyc - 3 cycles T weeks weeks T1 T1 2 cycles - 4 cycles T2 2 cycles T weeks 12 weeks ISSSMA June 3 rd

22 Sample results: early detection of tumor recurrence (2) SUVmean EORTC : no tumor recurrence detected at PET3 lol SUVmoy T2 SUVmoy T3 B 2 cycles - 4 cycles T2 T PET scan Examen weeks PI : tumor recurrence detected at PET3 ISSSMA June 3 rd

23 Discussion / conclusion - No need to precisely delineate the tumors - Makes it possible to detect small changes in metabolic activity - Summarizes changes between two or more scans in a single image - Shows heterogeneous tumor response within a tumor or between tumors ISSSMA June 3 rd

24 Thanks to Hatem Necib, PhD Jacques Antoine Maisonobe, PhD student Michaël Soussan, MD Camilo Garcia, MD, Institut Jules Bordet, Bruxelles Patrick Flamen, MD, Institut Jules Bordet, Bruxelles Bruno Vanderlinden, MSc, Institut Jules Bordet, Bruxelles Alain Hendlisz, MD, Insitut Jules Bordet, Bruxelles ISSSMA June 3 rd