Radiomics in IOERT of Unilateral Breast Cancer as a Biological Dosimetry

Document Type : Original Paper

Authors

1 Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

2 Department of Radiology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA

3 Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

4 Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

5 Department of General Surgery, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

6 Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran

Abstract

Introduction: In this study, Radiomic features analysis of CT scan images of the irradiated breast compared to the contralateral breast after a 12 Gy boost radiation dose in IOERT was conducted to obtain radiation-sensitive indicators (parameters) biological markers or biological dosimeters.
Material and Methods: 35 contrast chest CT scans (with unilateral ductal carcinoma in situ (DCIS) who had undergone boost IOERT) were used in this study. The total number of 259 CT radiomic features (first-order, textural, gradient, and autoregressive model-based features) were extracted using Mazda software. The features that were significantly different in the two breasts were selected. A score was assigned to each of the features and the highest scores were characterized (according to the level of significant differences). The feature selection process was performed using the hybrid feature selection method.
Results: CT Texture analysis indicated that radiation dose causes significant changes in some radiomic features of the breast tissue. 
Conclusion: With more research in the future, we can fit the Delta-Radiomics values with the received radiation dose and achieve a biological dosimeter to detect low-dose radiation.

Keywords

Main Subjects

References

  1. Patyk, M, Silicki, J, Mazur, R, Kręcichwost, R, Sokołowska-Dąbek, D, Zaleska-Dorobisz, U. Radiomics–the value of the numbers in present and future radiology. Polish J. Radiol. 2018;83:171–
  2. Rizzo, S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.
  3. Miles KA. How to use CT texture analysis for prognostication of non-small cell lung cancer. Cancer Imaging. 2016;16(1):10.
  4. Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev. Precis. Med. Drug. Dev. 2016;1(2):207–
  5. Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS. Rapid review: radiomics and breast cancer. Breast cancer research and treatment. 2018 Jun;169(2):217-29.
  6. Zhao, B, Schwartz, L.H, Larson, S.M. Imaging surrogates of tumor response to therapy: anatomic and functional biomarkers. J. Nucl. Med. 2009;50(2):239–
  7. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer. 2012 Mar 1;48(4):441-6.
  8. Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non–small cell lung cancer associated with response and survival after chemoradiotherapy?. Journal of nuclear medicine. 2013 Jan 1;54(1):19-26.
  9. Mattonen SA, Johnson C, Palma DA, Rodrigues G, Louie AV, Senan S, et al. Radiomics versus physician assessment for the early prediction of local cancer recurrence after stereotactic radiotherapy for lung cancer. InMedical Imaging 2016: Computer-Aided Diagnosis. 2016; 9785: 359-66.
  10. Larue RT, Defraene G, De Ruysscher D, Lambin P, Van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. The British journal of radiology. 2017 Feb;90(1070):20160665.
  11. Gnep K, Fargeas A, Gutiérrez‐Carvajal RE, Commandeur F, Mathieu R, et al. Haralick textural features on T2‐weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. Journal of Magnetic Resonance Imaging. 2017 Jan;45(1):103-17.
  12. Choi W, Oh JH, Riyahi S, Liu CJ, Jiang F, Chen W, et al. Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer. Medical physics. 2018 Apr;45(4):1537-49.
  13. Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of radiomic features: a systematic review. Int. J. Radiat. Oncol. Biol. Phys. 2018;102(4):1143–
  14. Vallières M, Zwanenburg A, Badic B, Le Rest CC, Visvikis D, Hatt M. Responsible radiomics research for faster clinical translation. J. Nucl. Med. 2018;59(2):189–
  15. Bartoli M, Barat M, Dohan A, Gaujoux S, Coriat R, Hoeffel C, et al. CT and MRI of pancreatic tumors: an update in the era of radiomics. Japanese Journal of Radiology. 2020 Dec;38(12):1111-24.
  16. Yang F, Ford JC, Dogan N, Padgett KR, Breto AL, Abramowitz MC, et al. Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Translational andrology and urology. 2018 Jun;7(3):445.
  17. Jajroudi, M, Enferadi, M, Azar Homayoun, A, Reiazi, R. MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme. Magn. Reson. Imaging. 2022; 85: 222–
  18. Reitsamer R, Peintinger F, Sedlmayer F, Kopp M, Menzel C, Cimpoca W, et al. Intraoperative radiotherapy given as a boost after breast-conserving surgery in breast cancer patients. European Journal of Cancer. 2002 Aug 1;38(12):1607-10.
  19. Esposito E, Anninga B, Honey I, Ross G, Rainsbury D, Laws S, et al. Is IORT ready for roll-out?. ecancermedicalscience. 2015;9.
  20. Nounou MI, ElAmrawy F, Ahmed N, Abdelraouf K, Goda S, Syed-Sha-Qhattal H. Breast cancer: conventional diagnosis and treatment modalities and recent patents and technologies. Breast cancer: basic and clinical research. 2015 Jan;9:BCBCR-S29420.
  21. Lei J, Wang Y, Bi Z, Xue S, Ou B, Liu K. Intraoperative radiotherapy (IORT) versus whole-breast external beam radiotherapy (EBRT) in early stage breast cancer: results from SEER database. Japanese Journal of Radiology. 2020 Jan;38(1):85-92.
  22. Szczypinski, P, Strzelecki, M, Materka, A. MaZda - a Software for Texture Analysis. Proc. of ISITC. 2007:245–
  23. Szczypinski P, Strzelecki M, Materka A, Klepaczko A. MaZda-A software package for image texture analysis. Comput. Meth. Prog. Bio. 2009;94(1):66–
  24. Strzelecki M, Szczypinski P, Materka A, Klepaczko A. A software tool for automatic classification and segmentation of 2D/3D medical images. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2013 Feb 21;702:137-40.
  25. IBM Corp., IBM SPSS Modeler 18.0, https://www.ibm.com/support/pages/downloading-ibm-spss-modeler-180, 2019, Armonk, NY, USA.
  26. Hsu HH, Hsieh CW, Lu MD. Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications. 2011 Jul 1;38(7):8144-50.
  27. Cadenas JM, Garrido MC, MartíNez R. Feature subset selection filter–wrapper based on low-quality data. Expert Syst. Appl. 2013;40(16):6241–
  28. Hu, Z, Bao, Y, Xiong, T, Chiong, R. Hybrid filter–wrapper feature selection for short-term load forecasting. Eng. Appl. Artif. Intell. 2015;40:17–
  29. Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014– Jpn J Radiol. 2019;37:34–72.
  30. Alis D, Bagcilar O, Senli YD, Yergin M, Isler C, Kocer N, et al. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas. Japanese Journal of Radiology. 2020 Feb;38(2):135-43.
  31. Das S. Filters, wrappers, and a boosting-based hybrid for feature selection. ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning. 2001;74–
  32. Jantawan B, Tsai CF. A comparison of filter and wrapper approaches with data mining techniques for categorical variables selection. International Journal of Innovative Research in Computer and Communication Engineering. 2014;2(6):4501-8.
  33. Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019;19(1):281. Doi:10.1186/s12911-019-1004-8
  34. Cunliffe A, Armato III SG, Castillo R, Pham N, Guerrero T, Al-Hallaq HA. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. International Journal of Radiation Oncology* Biology* Physics. 2015 Apr 1;91(5):1048-56.
  35. Moran A, Daly ME, Yip SS, Yamamoto T. Radiomics-based assessment of radiation-induced lung injury after stereotactic body radiotherapy. Clinical lung cancer. 2017 Nov 1;18(6):e425-31.
  36. Abdollahi H, Mahdavi SR, Shiri I, Mofid B, Bakhshandeh M, Rahmani K. Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy. Journal of cancer research and therapeutics. 2019;15(8):S11-9.
  37. Abdollahi H, Tanha K, Mofid B, Razzaghdoust A, Saadipoor A, Khalafi L, et al. MRI radiomic analysis of IMRT-induced bladder wall changes in prostate cancer patients: a relationship with radiation dose and toxicity. Journal of Medical Imaging and Radiation Sciences. 2019 Jun 1;50(2):252-60.

 

 

 

 

 

Iranian Journal of Medical Physics
Volume 19, Issue 6
November and December 2022
Pages 322-328

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History

  • Receive Date: 19 November 2021
  • Revise Date: 03 April 2022
  • Accept Date: 06 April 2022

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