Design of MATLAB-based Radiomics Classifier Training Simulator Powered by Pyradiomics

Authors

  • Muhammed Selman Erel Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, 06010 Ankara, Turkey
  • Hadeel Aljobouri Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
  • Esra şengün Ermeydan Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, 06010 Ankara, Turkey.
  • Ilyas çankaya Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, 06010 Ankara, Turkey

DOI:

https://doi.org/10.29194/NJES.27020185

Keywords:

GUI, LUNG1, MATLAB, Machine Learning, Radiomics, TCIA

Abstract

Technically, medical imaging modalities are quantitative, qualitative, and semi-quantitative. Such modalities can generate meaningful and valuable quantitative and qualitative data. Correlating predictive outcomes with quantitative and qualitative data is a difficult process. Thanks to modern computational hardware and advanced machine learning algorithms, it is not a demanding job to perform predictive analysis by cultivating quantitative and qualitative data. Radiomics is a popular topic that studies quantitative data from medical images in order to obtain biologically meaningful information for diagnosis, prognosis, theragnosis, and decision support. Handcrafted radiomics is a process including features based on shape, pixel, and texture-related knowledge from medical scans. In the pursuit of advancing the field of radiomics, we have developed a cutting-edge radiomics training simulator, powered by MATLAB. This tool has been designed for those familiar with MATLAB, making it easy for them to transition into the fascinating world of radiomics. MATLAB's user-friendly interface and strong support in the engineering community provide an ideal platform for this simulator, ensuring aspiring radiomics learners have access to the resources they need for success. Throughout the paper, purpose, design details and methodology of the simulator are described.

Downloads

Download data is not yet available.

References

J. H. Scatliff and P. J. Morris, “From Röntgen to Magnetic Resonance Imaging,” North Carolina Medical Journal, vol. 75, no. 2. North Carolina Institute of Medicine, pp. 111–113, Mar. 2014.

M. M. Galloway, “Texture analysis using gray level run lengths,” Computer Graphics and Image Processing, vol. 4, no. 2. Elsevier BV, pp. 172–179, Jun. 1975.

A. P. Pentland, “Fractal-Based Description of Natural Scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, no. 6. Institute of Electrical and Electronics Engineers (IEEE), pp. 661–674, Nov. 1984.

M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 5. Institute of Electrical and Electronics Engineers (IEEE), pp. 1264–1274, 1989.

G. Thibault, J. Angulo, and F. Meyer, “Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 3. Institute of Electrical and Electronics Engineers (IEEE), pp. 630–637, Mar. 2014

J. J. M. van Griethuysen et al., “Computational Radiomics System to Decode the Radiographic Phenotype,” Cancer Research, vol. 77, no. 21. American Association for Cancer Research (AACR), pp. e104–e107, Oct. 31, 2017

“Welcome to pyradiomics documentation!” pyradiomics. https://pyradiomics.readthedocs.io/en/latest/index.html (accessed Jun. 18, 2023).

H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians, vol. 71, no. 3. Wiley, pp. 209–249, Feb. 04, 2021.

Adler I. Primary malignant growths of the lungs and bronchi. Longmans, Green, and Company; 1912.

F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 68, no. 6. Wiley, pp. 394–424, Sep. 12, 2018.

R. Doll, R. Peto, J. Boreham, and I. Sutherland, “Mortality in relation to smoking: 50 years’ observations on male British doctors,” BMJ, vol. 328, no. 7455. BMJ, p. 1519, Jun. 22, 2004

H. Asamura et al., “IASLC Lung Cancer Staging Project: The New Database to Inform Revisions in the Ninth Edition of the TNM Classification of Lung Cancer,” Journal of Thoracic Oncology, vol. 18, no. 5. Elsevier BV, pp. 564–575, May 2023

K. Zarogoulidis et al., “Treatment of non-small cell lung cancer (NSCLC),” Journal of Thoracic Disease, vol. 5, no. Suppl 4, pp. S389–S396, Sep. 2013

C. Parmar, P. Grossmann, J. Bussink, P. Lambin, and H. J. W. L. Aerts, “Machine Learning methods for Quantitative Radiomic Biomarkers,” Scientific Reports, vol. 5, no. 1. Springer Science and Business Media LLC, Aug. 17, 2015

C. Parmar et al., “Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer,” Scientific Reports, vol. 5, no. 1. Springer Science and Business Media LLC, Jun. 05, 2015

W. Wu et al., “Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology,” Frontiers in Oncology, vol. 6. Frontiers Media SA, Mar. 30, 2016

Lambrecht, J. Textural Analysis of Tumour Imaging: A Radiomics Approach, 2017

A. Chaddad, C. Desrosiers, M. Toews, and B. Abdulkarim, “Predicting survival time of lung cancer patients using

radiomic analysis,” Oncotarget, vol. 8, no. 61. Impact Journals, LLC, pp. 104393–104407, Nov. 01, 2017

C. Haarburger, P. Weitz, O. Rippel, and D. Merhof, “Image-Based Survival Prediction for Lung Cancer Patients Using CNNS,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, Apr. 2019

Z. Shi et al., “Distributed radiomics as a signature validation study using the Personal Health Train infrastructure,” Scientific Data, vol. 6, no. 1. Springer Science and Business Media LLC, Oct. 22, 2019.

M. L. Welch et al., “Vulnerabilities of radiomic signature development: The need for safeguards,” Radiotherapy and Oncology, vol. 130. Elsevier BV, pp. 2–9, Jan. 2019

C. Haarburger et al., “Radiomic Feature Stability Analysis Based on Probabilistic Segmentations,” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, Apr. 2020

L. Ubaldi et al., “Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples,” Physica Medica, vol. 90. Elsevier BV, pp. 13–22, Oct. 2021

S. Marinov et al., “Radiomics software for breast imaging optimization and simulation studies,” Physica Medica, vol. 89. Elsevier BV, pp. 114–128, Sep. 2021

G. Pasini, F. Bini, G. Russo, A. Comelli, F. Marinozzi, and A. Stefano, “matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model,” Journal of Imaging, vol. 8, no. 8. MDPI AG, p. 221, Aug. 18, 2022.

M. Lei et al., “Benchmarking features from different radiomics toolkits / toolboxes using Image Biomarkers Standardization Initiative.” arXiv, 2020.

G. Pasini, F. Bini, G. Russo, A. Comelli, F. Marinozzi, and A. Stefano, “matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model,” Journal of Imaging, vol. 8, no. 8. MDPI AG, p. 221, Aug. 18, 2022.

M. Lei et al., “Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis,” Journal of Digital Imaging, vol. 34, no. 5. Springer Science and Business Media LLC, pp. 1156–1170, Sep. 20, 2021.

A. Bettinelli et al., “A Novel Benchmarking Approach to Assess the Agreement among Radiomic Tools,” Radiology, vol. 303, no. 3. Radiological Society of North America (RSNA), pp. 533–541, Jun. 2022.

E. Pfaehler, A. Zwanenburg, J. R. de Jong, and R. Boellaard, “RaCaT: An open source and easy to use radiomics calculator tool,” PLOS ONE, vol. 14, no. 2. Public Library of Science (PLoS), p. e0212223, Feb. 20, 2019.

A. C. Lorena et al., “Comparing machine learning classifiers in potential distribution modelling,” Expert Systems with Applications, vol. 38, no. 5. Elsevier BV, pp. 5268–5275, May 2011.

H. J. W. L. Aerts et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nature Communications, vol. 5, no. 1. Springer Science and Business Media LLC, Jun. 03, 2014.

Downloads

Published

29-08-2024

How to Cite

[1]
M. S. Erel, H. Aljobouri, E. şengün Ermeydan, and I. çankaya, “Design of MATLAB-based Radiomics Classifier Training Simulator Powered by Pyradiomics”, NJES, vol. 27, no. 2, pp. 185–192, Aug. 2024, doi: 10.29194/NJES.27020185.

Similar Articles

41-50 of 50

You may also start an advanced similarity search for this article.