Project ID: P202203170004
Robustness and Reproducibility of Radiomics Features from Fusions of PET-CT Images
Skills:
1-The individual with enough experience in medical image processing techniques. 2- The individual with enough experience in traditional and deep learning fusion techniques. 3-The individual with enough experience in machine learning methods (dimension reduction algorithms, classifiers and others). 5- The individual with enough experience in extracting radiomics features from each region of interest (via SERA package). 6- Familiar with intraclass correlation (ICC) concept to select reliable tensor radiomics features. Both programming languages Matlab and Python are acceptable, but Python works better for some people who aim at working on google Colab.
Requirements:
People with the below expertise are able to apply for this project: 1-The individual with enough experience in medical image processing techniques. 2- The individual with enough experience in traditional and deep learning fusion techniques. 3-The individual with enough experience in machine learning methods (dimension reduction algorithms, classifiers and others). 5- The individual with enough experience in extracting radiomics features from each region of interest (via SERA package). 6- Familiar with intraclass correlation (ICC) concept to select reliable tensor radiomics features. Both programming languages Matlab and Python are acceptable, but Python works better for some people who aim at working on google Colab.
This project includes:
Description:
Objectives: Radiomics is a major frontier in medical image analysis, enabling mining of high-dimensional data from images. Although radiomics features (RF) are increasingly extracted via standardized radiomics software packages towards more reproducible research, employing different feature-generation hyperparameters, fusion techniques, and segmentation methods, may still lead to variable RFs. As such, employing RFs which are robust to processing variations is another important step towards reproducible study. The present work aims, specifically, to identity robust RFs that are less sensitive to different fusion techniques in head and neck (HN) cancer where fused PET-CT imaging hold significant value. To the best of our knowledge, no previous study has investigated the sensitivity of RFs to different fusion models in PET-CT imaging. Methods: 408 patients with HN cancer were extracted from the Cancer Imaging Archive (TCIA). In the pre-processing step, PET images were first registered to CT, enhanced, normalized, and cropped. We employed 15 typical image-level fusion techniques to combine PET and CT information: 1) Laplacian pyramid, 2) Ratio of low-pass pyramid, 3) Discrete wavelet transform, 4) Dual-tree complex wavelet transform (DTCWT), 5) Curvelet transform (CVT), 6) Nonsubsampled contourlet transform (NSCT), 7) Sparse representation (SR), 8) DTCWT+SR, 9) CVT+SR, 10) NSCT+SR, 11) Bilateral cross filter, 12) Wavelet Fusion, 13) Weighted Fusion, 14) Principal Component Analysis, and 15) Hue, Saturation and Intensity Fusion. Subsequently, 211 RFs were extracted from each region of interest in PET-only, CT-only, and 15 fused PET-CT images through the standardized SERA radiomics package. Variabilities of RFs were studied using the Intraclass Correlation Coefficient (ICC) (with carefully selected parameters, including for two-way random effects, absolute agreement and, multiple raters/measurements). ICC>0.90, 0.75<ICC<0.90, 0.50<ICC<0.75, ICC<0.50 were denoted as having excellent, good, moderate, and poor reliabilities, respectively. Furthermore, considering 95% confidence intervals for ICC values, we further categorized the features into seven reliability groups, including i) poor-poor (lower bound-upper bound), ii) poor- moderate, iii) moderate-moderate, iv) moderate -good, v) good-good, vi) good-excellent, and vii) excellent-excellent .
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