Project ID: P202205130003
Identifying Reliable and Robust Tensor Radiomics Features in Lung Cancer
Deep Learning, Machine Learning, Image Processing
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 3-D Deep learning methods 3- The individual with enough experience in traditional and deep learning fusion techniques. Both programming languages Matlab and Python are acceptable, but Python works better for some people who aim at working on google Colab.
Objectives: Radiomics is a major frontier in medical image analysis, enabling the 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 identify robust RFs that are less sensitive to different fusion techniques in lung cancer where fused PET-CT imaging holds significant value. Methods: 200 patients with lung cancer were extracted from the Cancer Imaging Archive (TCIA). In the pre-processing step, PET images are first registered to CT, enhanced, normalized, and cropped. We employ multiple typical image-level fusion techniques to combine PET and CT information. Each variable extracted from each modality or fused image calls a flavor of a feature. Subsequently, 215 RFs are 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 are 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 are denoted as having excellent, good, moderate, and poor reliabilities, respectively. Furthermore, considering 95% confidence intervals for ICC values, we further categorize 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.