Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management. While extremely powerful with the potential to make a tremendous clinical impact, the current state of radiomics rely entirely on existing, predefined imaging-based feature models based on aspects such as intensity, texture, and shape, which can greatly limit its ability to fully characterize the unique traits of different forms of cancer.
We have taken radiomics to the next level in personalized cancer quantification by introducing the concept of discovery radiomics, where we forgo the notion of predefined feature models by discovering customized, tailored radiomics feature models directly from the wealth of medical imaging data already available. This enables an unprecedented level of understanding and characterization of the unique cancer phenotype associated with different forms of cancer, allowing for the identification of a large amount of imaging-based features that capture highly unique tumor traits and characteristics beyond what can be captured using predefined feature models.
The discovery radiomics framework consists of the following steps (see Figure above). First, a wealth of standardized medical imaging data from past patients are fed into the radiomics sequencer discovery engine, where a customized radiomics sequencer is constructed based on a large number of radiomics features that were discovered to capture highly unique tumor traits and characteristics. Second, for a new patient case, the discovered radiomics sequencer is then used to extract a wealth of customized, tailored imaging-based features from the medical imaging data of the new patient case for comprehensive, custom quantification of the tumor phenotype.
Related people
Directors
Students
Alumni
Related publications
Wong A., Gunraj H., Sivan V., and Haider M.A, "Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer", Scientific Reports, vol. 12, 2022. Get it here. Lee J.R.H., Pavlova M., Famouri M., and Wong A, "Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images", BMC Medical Imaging, vol. 22, 2022. Get it here. Dulhanty C., Wang L., Cheng M., Gunraj H., Khalvati F., Haider M.A., and Wong A, "Radiomics driven diffusion weighted imaging sensing strategies for zone-level prostate cancer sensing", Sensors (Switzerland), vol. 20, 2020. Get it here. Kumar D., Sankar V., Clausi D., Taylor G.W., and Wong A, "SISC: End-to-End Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells", IEEE Access, vol. 7, pp. 145444-145454, 2019. Get it here. Khalvati F., Zhang Y., Wong A., and Haider M.A, "Radiomics", Encyclopedia of Biomedical Engineering, vol. 1, pp. 597-603, 2019. Get it here. Kumar D., Taylor G.W., and Wong A, "Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy", IEEE Access, vol. 7, pp. 25891-25896, 2019. Get it here. Khalvati F., Zhang J., Chung A.G., Shafiee M.J., Wong A., and Haider M.A, "MPCaD: A multi-scale radiomics-driven framework for automated prostate cancer localization and detection", BMC Medical Imaging, vol. 18, 2018. Get it here. Shafiee M.J., Chung A.G., Khalvati F., Haider M.A., and Wong A, "Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection", Journal of Medical Imaging, vol. 4, 2017. Get it here. Zhang Y., Oikonomou A., Wong A., Haider M.A., and Khalvati F, "Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer", Scientific Reports, vol. 7, 2017. Get it here. Cameron A., Khalvati F., Haider M.A., and Wong A, "MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection", IEEE Transactions on Biomedical Engineering, vol. 63, pp. 1145-1156, 2016. Get it here. Chung A.G., Khalvati F., Shafiee M.J., Haider M.A., and Wong A, "Prostate cancer detection via a quantitative radiomics-driven conditional random field framework", IEEE Access, vol. 3, pp. 2531-2541, 2015. Get it here. Khalvati F., Wong A., and Haider M.A, "Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models", BMC Medical Imaging, vol. 15, 2015. Get it here. Khalvati F., Modhafar A., Cameron A., Wong A., and Haider M.A, "A multi-parametric diffusion magnetic resonance imaging texture feature model for prostate cancer analysis", Mathematics and Visualization, vol. 39, pp. 79-88, 2014. Get it here. Tai C.-E.A., Janes E., Czarnecki C., and Wong A, "Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images", Sensors, vol. 24, . Get it here. Tai C.-E.A., and Wong A, "Optimized Synthetic Correlated Diffusion Imaging for Improving Breast Cancer Tumor Delineation", Sensors, vol. 24, . Get it here.
Conference PapersTai C.-E.A., Gunraj H., Hodzic N., Flanagan N., Sabri A., and Wong A, "Enhancing Clinical Support for Breast Cancer with Deep Learning Models Using Synthetic Correlated Diffusion Imaging", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024. Get it here. Neher H., Arlette J., and Wong A, "Discovery radiomics for detection of severely atypical melanocytic lesions (saml) from skin imaging via deep residual group conv", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019. Get it here. Cho D.S., Khalvati F., Clausi D.A., and Wong A, "A machine learning-driven approach to computational physiological modeling of skin cancer", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. Get it here. Kumar D., Chung A.G., Shaifee M.J., Khalvati F., Haider M.A., and Wong A, "Discovery radiomics for pathologically-proven computed tomography lung cancer prediction", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. Get it here. Karimi A.-H., Chung A.G., Shafiee M.J., Khalvati F., Haider M.A., Ghodsi A., and Wong A, "Discovery radiomics via a mixture of deep ConvNet sequencers for multi-parametric MRI prostate cancer classification", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. Get it here. Khalvati F., Zhang J., Wong A., and Haider M.A, "Bag of bags: Nested multi instance classification for prostate cancer detection", Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, 2017. Get it here. Cameron A., Modhafar A., Khalvati F., Lui D., Shafiee M.J., Wong A., and Haider M, "Multiparametric MRI prostate cancer analysis via a hybrid morphological-textural model", 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2014. Get it here.