1. DW-MRI and MRS to Differentiate Radiation Necrosis and Recurrent Disease in Gliomas Protocol MRS data status 11/27/07 – 12/17/07 Thomas Chong

2. Patient MRS (Magnetic Resonance Spectroscopy) Data Provides data on in vivo chemical composition (via proton NMR) of the lesion and surrounding regions of the brain 15x15x3 grid of 1cc voxels Raw data is time response processed to spectrum (ppm)‏ Characteristic resonance peaks in spectrum correspond to specific compounds (e.g. water, lipids, metabolites). area = ~amount Focus on Choline/ NAA ratio change for glioma diagnosis

3. Status Summary 11/27/07 - 12/17/07 Previous Work  What have I done? team role? Recap of last summary: Remaining patient #'s 1-10 data manually processed Knowledge available from the EU INTERPRET project – show and tell of journal paper info

4. Related Previous Work Metabolite phantom experiment goal to quantify MRS errors (“registration” or alignment)‏  T2 FLAIR pulse sequence inappropriate for cuvette phantom  but enough data to show alignment is close enough for our purposes (*)‏ created dynamic web-accessible MRS database and metabolite amount computation tool (http://web.arizona.edu/mrs_db)‏http://web.arizona.edu/mrs_db processed all inf/med/sup MRS grid slices for all patients (1-10), inc. validity maps

5. Low Percentage of “Valid” MRS Spectra -1 No metabolite amount info obtainable from most data  best, cleanest P1, P2, P5 Motivated investigation into possible spatial correlations  No clear correlations obvious. Need more data

6. Low Percentage of “Valid” MRS Spectra -2 Artifacts present in MRS protocol data are consistent with those seen by other researchers  large baseline distortions  exceptionally broadened metabolite peaks  large phase errors Other observed data corrupting factor  SNR of cho, cre, or naa peaks reduced by large unknown resonance peak  broad non-metabolite peak, or non-constant floor MRS signal interpretation for tumors recognized as a complicated task – see INTERPRET project (International Network for Pattern Recognition of Tumours Using Magnetic Resonance), a consortium of 10 EU countries [1]

7. INTERPRET MRS project Ongoing since 2000, latest developments in 2007 paper Their goal: “to develop a computer-based decision support tool, that will enable radiologists and other clinicians without special expertise to diagnose and grade brain tumours routinely using magnetic resonance spectroscopy.” Using:  “A large "training set" of data contributed by members of the INTERPRET consortium.  “Automated pattern recognition techniques for tumour classification.

8. INTERPRET Tumor Classifier Description INTERPRET DSS (decision support system)‏ “Easy access to a database of spectra, images and clinical information from 304 validated cases of human brain tumour. “designed to allow the display of classification plots useful for automating the classification of tumour spectra. “Currently only one classification plot is provided (suitable for discriminating spectra from low grade gliomas vs Glioblastomas and Metastasis vs Low-grade Meningiomas). http://www.cogs.susx.ac.uk/users/joshuau/interpret/

9. INTERPRET MRS project Developed MRS brain tumor classification software. Pattern recognition based on large training dataset:  6 MR systems/ 3 diff manufacturers, custom phantom, 3yrs of patient protocol data SW and data is available for download to aid clinicians not researchers http://www.cogs.susx.ac.uk/users/joshuau/inter pret/ A lot of interesting MRS-tumor related info

10. INTERPRET MRS project – so? what of it? Still, how to distinguish artifacts from presence of unwanted/unknown substances? Common recognition that MRS data interpretation is not easy:  see above name of the big EU project  rigorous process for deciding validity of MRS voxel data in their database entailed up to 3 expert spectrologists.

11. INTERPRET MRS project – so? what of it? Useful SNR and WBW measures defined  phantom reference gives info on data variability (it's noisy, based on successive bimonthly meas.)‏  automated program to check spectrum for WBW 10 Tumor recognition tool does not utilize track of time trend changes in data

12. INTERPRET Recognition Software Still research-quality, i.e. use at own risk Reverse-engineered guess at method: candidate spectrum matched to database- derived mean reference spectrum using neural network and/or (Bayesian) statistical methods. Can leverage their data & findings as resource to better understand our own data

13. SV 1H-MRS SHORT echo time spectra of different human brain tumoural pathologies Normal brain mean of 22 cases Glioblastoma mean of 86 cases Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

14. SV 1H-MRS SHORT echo time spectra of different human brain tumoural pathologies Normal brain mean of 22 cases Astrocytoma II mean of 22 cases Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

15. SV 1H-MRS SHORT echo time spectra of different human brain tumoural pathologies Normal brain mean of 22 cases Abscess mean of 8 cases Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

16. SV 1H-MRS SHORT echo time spectra of different human brain tumoural pathologies Glioblastoma mean of 86 cases Abscess mean of 8 cases v similar to glio.

17. SV 1H-MRS SHORT echo time spectra of different human brain tumoural pathologies Normal brain mean of 22 cases Metastasis mean of 38 cases Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

18. SV 1H-MRS SHORT echo time spectra of different human brain tumoural pathologies Normal brain mean of 22 cases Meningioma mean of 58 cases Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

19. New Perspective of Our “Bad” Data Absence of distinct metabolite peaks prevents calculation of relative amounts and ratios,... But others have empirically categorized tumor types based on spectral characteristics. MRS data we're collecting usable for future studies using different methods

20. Patient 10, Exam 3446, S16.1 “short” or “long” echo time in our protocol?

21. Patient 1, Exam 3103, S38

22. Patient 1, Exam 3103, S67

23. Questions What is water-suppressed spectrum?  non-suppressed spectrum? What is the water line artifact in spectrum? Shimming procedure for optimization of field homogeneity? What does van der Graaf, et al refer to as signal linearity? WBW (water bandwidth): line width at half max intensity of water resonance in real non- suppressed spectrum - a measure of field homogeneity [1]. significance?

24. Tasks Process patient 11 scan data Answer questions on previous slide Return to INTERPRET website, continue looking for useful information  How useful was their diagnosis system in the clinical setting? (MRS data alone)‏  Read source ref for mean brain MRS data: Tate et al. NMR Biomed. 19:411-434, 2006 Request e-mailed for access to their tool download page and database

25. Reference [1] MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. M. van der Graaf, et. al., NMR in Biomedicine, DOI: 10.1002, 2007. [2] Tate et al. NMR Biomed. 19:411-434, 2006  http://azizu.uab.es/INTERPRET/mean_spectra _images.html