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)
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.
RADIOMICS: definition & workflow Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR Predictive / Prognostic models
RADIOMICS: history
RADIOMICS: history Texture analysis Big data analysis Extraction of quantitative … extraction of LARGE amount parameters from images of quantitative features Clinical data availability Computational power Store & retrieval of large Haralick, 1973: amount of clinical data and images Digital Imaging - omics Experience from other fields (Molecular biology, genetics , …)
RADIOMICS: history
RADIOMICS: history
RADIOMICS: potential role in the clinics Considering that: 1. Imaging is routinely performed for oncologic patients: - diagnosis • plenty of retrospective data - treatment planning - follow up • database continuously updated • no additional cost 2. Imaging is not invasive and minimally detrimental invasive alternatives: biopsy, blood sampling • no additional patient discomfort 3. Radiomics quantifies the properties of the whole volume • more complete information reduced risk of under-sampling as compared to e.g. biospy
RADIOMICS: history Pubmed search 120 100 Radiomics 80 60 40 20 0 1985 1986 1987 1995 1998 2000 2001 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
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
RADIOMICS: potential role in the clinics
RADIOMICS: potential role in the clinics
RADIOMICS: challenges Models generalizability
RADIOMICS: challenges
RADIOMICS: challenges Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR Data quality: «imaging biomarkers» are needed
RADIOMICS: challenges Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR Imaging biomarker: Which requirements?
RADIOMICS: challenges Interpretation?
RADIOMICS: workflow Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR
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
RADIOMICS: workflow Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR
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
RADIOMICS: workflow Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR
RADIOMICS: features extraction Sha Shape fea eature res: 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
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!
RADIOMICS: features categories His istogra ram-based (Fi (Firs rst ord order statistic ics) ) fea eature res: 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.
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»
RADIOMICS: features categories GLCM: Image Gray Level Cooccurrence Matrix ….
RADIOMICS: features categories v GLRLM: Gray Level Run Length Matrix Image ….
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) • 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 Fractal dimension 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.
RADIOMICS: challenges Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR The voxel intensity is the starting point for features calculation
RADIOMICS: challenges Computed Tomography – CT Positron Emission Tomography – PET Magnetic Resonance - MR A. What affects the voxel intensity? Does it also affect the radiomic feature value?
RADIOMICS: challenges 1. Image acquisition affects the informative content of the image Discrete sampling Partial Volume Effect
RADIOMICS: challenges 1. Image acquisition affects the informative content of the image Voxel size: Pixel size Slice Thickness Discrete sampling
RADIOMICS: challenges 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): 932-945
RADIOMICS: challenges 1. Image acquisition affects the informative content of the image Scanner properties Acquisition protocol Reconstruction algorithm Noise
RADIOMICS: challenges 1. Image acquisition affects the informative content of the image Bit depth
RADIOMICS: challenges 2. Image post-processing Post-reconstruction image filtering Useful for physicians, visual inspection, clinical reporting …impact on quantification? Discretization N possible values Size of GLCM: NxN in the image
RADIOMICS: challenges 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?
RADIOMICS: challenges 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?
RADIOMICS: challenges 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?
RADIOMICS: challenges Repeatability - CT Concordance Correlation Coefficient > 0.9
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