RADIOMICS: potential role in the clinics and challenges

Transcription

1 27 giugno 2018 Dipartimento di Fisica Università degli Studi di Milano RADIOMICS: potential role in the clinics and challenges Dr. Francesca Botta Medical Physicist Istituto Europeo di Oncologia (Milano)

2 RADIOMICS: definition Radiomics is a field of medical study that aims to extract large amount of quantitative features from medical images using mathematical algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics that fail to be appreciated by the naked eye. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy. Radiomics emerged from the medical field of oncology and is the most advanced in applications within that field. However, the technique can be applied to any medical study where a disease or a condition can be tomographically imaged.

3 RADIOMICS: definition & workflow Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR Predictive / Prognostic models

4 RADIOMICS: history

5 RADIOMICS: history Texture analysis Extraction of quantitative parameters from images Big data analysis extraction of LARGE amount of quantitative features Computational power Haralick, 1973: - omics Experience from other fields (Molecular biology, genetics, ) Clinical data availability Store & retrieval of large amount of clinical data and images Digital Imaging

6 RADIOMICS: history

7 RADIOMICS: history

8 RADIOMICS: potential role in the clinics Considering that: 1. Imaging is routinely performed for oncologic patients: - diagnosis - treatment planning - follow up 2. Imaging is not invasive and minimally detrimental invasive alternatives: biopsy, blood sampling plenty of retrospective data database continuously updated no additional cost no additional patient discomfort 3. Radiomics quantifies the properties of the whole volume reduced risk of under-sampling as compared to e.g. biospy more complete information

9 RADIOMICS: history Pubmed search Radiomics

10 RADIOMICS: potential role in the clinics - Differentiate malignant / benign tissue - Tumour staging: differentiate between early and advanced stage disease - Prognostic models: correlation with survival - Predictive models: predict treatment response (chemotherapy, radiation therapy) - Assessment of the metastatic potential of tumours - Assessment of cancer genetics / biological or histopathological properties (biological basis of clinical application of radiomics) - Improve predictivity of models based on clinical,biological, genetic data

11 RADIOMICS: potential role in the clinics

12 RADIOMICS: potential role in the clinics

13 Models generalizability

14

15 Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR Data quality: «imaging biomarkers» are needed

16 Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR Imaging biomarker: Which requirements?

17 Interpretation?

18 RADIOMICS: workflow Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR

19 RADIOMICS: workflow 1. Image acquisition CT images: the voxel intensity describes the composition and the density of the tissue PET images: the voxel intensity is a measure of the concentration of the radiotracer MR images: according to the sequence applied, the voxel intensity can be representative of different properties of the tissue (relaxation times T1, T2, proton density), diffusion, perfusion, Discrete sampling

20 RADIOMICS: workflow Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR

21 RADIOMICS: workflow 2. Region segmentation The Volume Of Interest is a 3D array of numbers, from which many different parameters can be calculated VOI: Volume Of Interest Manual segmentation Semi-automatic / Automatic segmentation algorithms Machine learning

22 RADIOMICS: workflow Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR

23 RADIOMICS: features extraction Shape features: describe the shape of the Region Of Interest in 3D Geometric properties, like the Volume, the maximum diameter or the 3 diameters along the 3 orthogonal directions, the maximum surface

24 RADIOMICS: features categories Histogram-based (First order statistics) features: Describe the distribution of values of individual voxels without concern for spatial relationships Different spatial arrangement BUT Same Histogram!

25 RADIOMICS: features categories Histogram-based (First order statistics) features: Describe the distribution of values of individual voxels without concern for spatial relationships (histogram-based methods as mean, median, maximum, minimum, uniformity or randomness (entropy) of the intensities, skewness (asymmetry) and kurtosis (flatness) of the histogram of values.

26 RADIOMICS: features categories Textural (Second order statistics) features: Textural features describing statistical interrelationships between voxels with similar (or dissimilar) values and take into account the spatial arrangement of the values. «Haralick features»

27 RADIOMICS: features categories Image GLCM: Gray Level Cooccurrence Matrix.

28 RADIOMICS: features categories v Image GLRLM: Gray Level Run Length Matrix.

29 RADIOMICS: features categories Higher order statistics features: Higher-order statistical methods applies filter grids or mathematical transforms to the image (for example, to extract repetitive or nonrepetitive patterns) Fractal dimension wavelet transform; fractal analyses, wherein patterns are imposed on the image and the number of grid elements containing voxels of a specified value is computed; Laplacian transforms of Gaussian bandpass filters that can extract areas with increasingly coarse texture patterns from the image; Radiomic features calculation is performed on the filtered or decomposed images in order to extract multiple or more informative parameters from a single image.

30 Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR The voxel intensity is the starting point for features calculation

31 Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR A. What affects the voxel intensity? Does it also affect the radiomic feature value?

32 1. Image acquisition affects the informative content of the image Discrete sampling Partial Volume Effect

33 1. Image acquisition affects the informative content of the image Voxel size: Pixel size Slice Thickness Discrete sampling

34 1. Image acquisition affects the informative content of the image Scanner properties Acquisition protocol Reconstruction algorithm Spatial resolution Image from Soret et al., Partial Volume Effect in PET tumour imaging, Journal of Nuclear Medicine (2007), 48(6):

35 1. Image acquisition affects the informative content of the image Scanner properties Acquisition protocol Reconstruction algorithm Noise

36 1. Image acquisition affects the informative content of the image Bit depth

37 2. Image post-processing Post-reconstruction image filtering Useful for physicians, visual inspection, clinical reporting impact on quantification? Discretization N possible values in the image Size of GLCM: NxN

38 A. what affects the voxel intensity? Pixel size Slice Thickness Scanner properties Acquisition protocol Reconstruction algorithm Bit depth Post-reconstruction image filtering Discretization Does it also affect the radiomic feature value? Reproducibility: different modalities are comparable? Repeatability: one modality more stable than others?

39 A. what affects the voxel intensity? Pixel size Slice Thickness Scanner properties Acquisition protocol Reconstruction algorithm Bit depth Post-reconstruction image filtering Discretization Does it also affect the radiomic feature value? Reproducibility: different modalities are comparable? Repeatability: one modality more stable than others?

40 A. what affects the voxel intensity? Pixel size Slice Thickness Scanner properties Acquisition protocol Reconstruction algorithm Bit depth Post-reconstruction image filtering Discretization Does it also affect the radiomic feature value? Reproducibility: different modalities are comparable? Repeatability: one modality more stable than others?

41 Repeatability - CT Concordance Correlation Coefficient > 0.9

42 Repeatability - CT Example: Phantom experiment, CT Acquisition/ Reconstruction setting % Coefficient of Variation among 10 consecutive acquisitions Feature Different acquisition/reconstruction settings have different repeatability

43 Repeatability - PET

44 about Repeatability: It should be assessed according to the Dynamic Range observed in-vivo, and according to the difference between the groups Distribution of values assessed after repeated acquisitions Feature value

45 about Repeatability: It should be assessed according to the Dynamic Range observed in-vivo, and according to the difference between the groups Distribution among many subjects of a group Feature value

46 about Repeatability: It should be assessed according to the Dynamic Range observed in-vivo, and according to the difference between the groups Group 1 Group 2 Feature value

47 Reproducibility - CT

48 Reproducibility - CT Example: Phantom experiment, CT Acquisition/ Reconstruction setting Feature Values obtained for 40 different acquisition and reconstruction settings Some features are highly dependent on acquisition/reconstruction settings Feature

49 Image processing

50 Computed Tomography CT Positron Emission Tomography PET Magnetic Resonance - MR A. What affects the voxel intensity? Does it also affect the radiomic feature value? B. Other factors affecting the radiomic feature value?

51 3. Volume: segmentation

52 3. Volume: size Small volumes, not only partial volume effect:

53 Rigorous methodology Standardization

54 Rigorous methodology Standardization Ask yourself: is the feature really quantifying a biological property? Or am I «measuring» noise? am I «measuring» volume?

55 Rigorous methodology Standardization Ask yourself: A. is the feature really quantifying a biological property? Or am I «measuring» noise? am I «measuring» volume? B. can I explain the meaning of the features extracted from images?

56 RADIOMICS: statistical analysis Redundancy: Hierarchical Clustering High correlation among features High intra-cluster correlation Low inter-cluster correlation

57 RADIOMICS: the future Data analysis - radiomic features - clinical data Feature calculation Machine Learning - image + Region Of Interest - clinical data Segmentation - image - clinical data