Association between pathology and texture features of multi parametric MRI of the prostate
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1 Association between pathology and texture features of multi parametric MRI of the prostate 1,2 Peter Kuess, 3 D. Nilsson, 1,2 P. Andrzejewski, 2,4 P. Georg, 1 J. Knoth, 5 M. Susani, 3 J. Trygg, 2,6 T. Helbich, 1,2 D. Georg, 7 T. Nyholm 1 Department of Radiation Oncology / Medical University Vienna & AKH Wien 2 Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology 3 Computational Life Science Cluster (CliC), Department of Chemistry, Umea University 4 EBG MedAustron GmbH, Wiener Neustadt (Austria) 5 Clinical Institute of Pathology, Medical University of Vienna 2,6 Department of biomedical Imaging and Image-guided Therapy, Medical University of Vienna 7 Department of Radiation Sciences, Radiation Physics, Umea University Peter Kuess - ESTRO 35, Turin
2 Acknowledgements Visit Umeå: Tufve Nyholm, David Nilsson, Patrik Brynolfsson The financial support by the Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development is gratefully acknowledged. 2
3 Motivation Finding a correlation between imaging parameters (textures) derived from mpmri and pathological verified tumor occurrence in the prostate Investigation of orthogonal partial least squares (OPLS) modelling approaches and the predictive power of parameter combinations Long-term goal: Usage of tumor prediction models as assistance during tumor delineation / diagnostic 3
4 Material & Methods: Dataset 25 Patient Data sets T2 (a) DCE (70 timepoints) o DCE: 0s, 79s, 300s (b-d) A B C D b1) o o AUC (e) ktrans (f) E F G H DWI (ADC map measured based on 4 b-values) (g) Pathology information after prostatectomy (h) Slice thickness 3-4 mm 4
5 Material & Methods: Delineation Registration of pathological slices and MR images is challenging Central Gland and Peripheral Zone were delineated on T2 Based on histological information Tumor was delineated by visual comparison and propagated to all image modalities In addition geometrical substructures (PIRADS) were used and scored in accordance to pathological information (4 distinct scoring levels) o 6 substructures in CG and 16 substructures in PZ 5
6 Material & Methods: Delineation Registration of pathological slices and MR images is challenging Central Gland and Peripheral Zone were delineated on T2 Based on histological information Tumor was delineated by visual comparison and propagated to all image modalities In addition geometrical substructures PIRADS Structure (PIRADS) 10p: were used and scored in accordance to pathological information (4 distinct scoring levels) Score: 0.75 / 1 o 6 substructures in CG and 16 substructures in PZ 6
7 Material & Methods: Delineation In 24/25 cases the situation was more complex 7
8 Material & Methods: Workflow ADC ADC T2 DCE 3 Timepoints Tumor GLCM (BinSize=8,16,32) X Tumor-free ktrans AUC Multivariate Image Analysis PCA OPLS 8
9 Material & Methods: Workflow ADC ADC T2 DCE 3 Timepoints Tumor GLCM (BinSize=8,16,32) X Tumor-free ktrans AUC Multivariate Image Analysis PCA OPLS 11 Histogramm -- based parameters per image modality Peter Kuess - ESTRO 35, Turin 9
10 Evaluation Parameters Textual Parameters Autocorrelation, Cluster Prominence, Cluster Shade, Maximum Probability, Energy, Sum of Squares Variance, Sum Variance, Sum Entropy Gray-level co-occurrence matrixes (GLCM) Bin Size (N=8,16 and 32) 4 Orientations GLCM Histogram based parameters Min, Max, 2%,15%...85%,98%, mean, median, standard deviation, skewness, kurtosis 10
11 Orthogonal partial least squares (OPLS) Modeling OPLS is a multivariate regression technique X data matrix: textures and histogram-based parameters of image modalities X = ഥ x + tp + T o P o + E y = തy + tq + F y: response representing histological information OPLS removes variations in X, T 0 P 0, that is orthogonal to response Quality of Model: R 2 Y: goodnes of model itself [0-1] Q 2 Y: explaining the cross-correlation [0-1] 11
12 Preliminary Results: PCA on PIRADS sturctures Histograms of Score projections of ADC Blue bars = distribution of tumor free PIRADS structures Orange bars = PIRADS structures with tumor occurence 12
13 Preliminary Results: PIRADS using PCA Histograms of Score projections of ADC Blue bars = distribution of tumor free PIRADS structures Orange bars = PIRADS structures with tumor occurence 13
14 Preliminary Resutls: OPLS modeling Imaging Parameters Q2Y R2Y ADC Txt ADC Hist ADC Txt+Hist DCE (79s) Txt DCE (79s) Hist DCE (79s) Txt+Hist T2 Txt T2 Hist T2 Txt+Hist OPLS Model including textual and histogrambased paramters for ADC Blue dots = tumor structures Red dots = tumor free structurs ktrans Txt ktrans Hist ktrans Txt+Hist
15 Q²Y OPLS Modeling ADC T2 DCE (79s) AUC ktrans DCE (0s) DCE (300s) Histogram based parameters Textual Parameters Combined: Hist & Txt 15
16 Q²Y OPLS Modeling ADC T2 DCE (79s) AUC ktrans DCE (0s) DCE (300s) Histogram based parameters Textual Parameters Combined: Hist & Txt 16
17 OPLS Modeling Benefit from using all mpmri image methods available R2Y Q2Y Linear (R2Y) Linear (Q2Y) 17
18 OPLS Modeling Benefit from using all mpmri image methods available R2Y Q2Y Linear (R2Y) Linear (Q2Y) 18
19 Conclusion Textual parameters proved to be an additional supplement to histogram-based parameters in mpmri analysis Tumor prediction using OPLS shows encouraging results Best prediction value obtained were based on ADC PIRADS classification for tumor prediction is promising But structure size is too large Next step: voxel-based GLCM Thanks for your attention! 19
20 Additonal Slides: Peter Kuess - ESTRO 35, Turin 20
21 Background: DIL boosting study Andrzejewski et al 2015 Rad Oncol in press Material and Methods: DILs were defined based on multiparametric magnetic resonance imaging and fused with planning computed tomography images for twelve patients. VMAT, IMPT and HDR-BT treatment plans were created for each patient with the EQD2 α/β dose to the DIL escalated up to Gy, PTV initial D pres = 80.9 Gy (EBRT) and CTV D 90% = 81.9 Gy (HDR-BT). Hard dose constraints were applied to spare the OARs. Treatment plans were evaluated and compared between used techniques in CERR software. Results: Higher boost doses were achieved with IMPT compared to VMAT, keeping major OARs doses at similar level. HDR-BT was superior both in terms of OARs sparing and DIL boosting. 21
22 Outlook: Voxel-based GLCM Local texture around every voxel is analyzed A GLCM is generated for all voxels in image Data is assembled as X and subjected to multivariate methods Score values can be refolded to original image dimensions for visualization Example from glioma data set 22
23 Outlook: Local Binary Patterns Analyzes the texture around a voxel Surrounded voxels are thresholded, which gives 0s and 1s Put together, these form a pattern, which is a binary number The LBP is the numerical number All LBPs from a ROI can be binned in a histogram, so the frequency of each pattern can be accessed The histograms can be subjected to multivariate data analysis 23
24 Multi parametric MR Images ADC T2 Histoscore: Substructure 8p: 0.75 ktrans PET 24
25 Speciificity Sensitivity Sensitivity and Specificity of OPLS % 95.00% 90.00% 85.00% 80.00% 75.00% 70.00% 65.00% 60.00% 55.00% 50.00% % 95.00% 90.00% 85.00% ADC T2 AUC ktrans DCE_early DCE_pre DCE_late Image Modality Histo Text Hist+Txt 80.00% 75.00% 70.00% 65.00% 60.00% 55.00% Histo Text Hist+Txt 50.00% ADC T2 AUC ktrans DCE_early DCE_pre DCE_late Image Modality 25
26 Sensitivity and Specificity of OPLS 95.00% 90.00% 85.00% 80.00% 75.00% 70.00% Sensitivity Specificity 26
27 Textual descriptors All: autocorrelation, Cluster prominence, cluster shade, contrast, correlation, difference entropy, dissimilarity, energy, entropy, homogeneity 1 (as described by Soh et al.), homogeneity 2 (as implemented in MatLab 2014a, Image Processing Toolbox v. 9.0), information measure of correlations 1 and 2, inverse difference moment, normalized inverse difference moment, maximum probability, sum average, sum entropy, sum of squares, variance, sum variance Not included due to size dependency: contrast, correlation, difference entropy, difference variance, dissimilarity, energy, entropy, homogeneity, inverse difference, information measures of correlation 1 and 2. Finally used textual parameters: Autocorrelation, Cluster Prominence, Cluster Shade, Maximum Probability, Energy, Sum of Squares Variance, Sum Variance, Sum Entropy 28
28 Loading Plot Size dependency of textual parameters 29
29 Zonal Segmentation 30
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