Investigation the motion data clustering of lung tumor on its position estimation at external surrogates’ radiotherapy

Document Type : Computer Article

Author

Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Haftbagh Highway, Kerman, Iran

Abstract

Among thorax tumors, lung tumors move mainly due to respiration. In order to enhance the precision of radiotherapy, one solution is estimating tumor motion from external motion of chest wall and abdomen regions. For this aim, consistent prediction models are constructed and then implemented for real time tumor motion tracking. In these models, clustering of database extracted from tumor motion and chest wall motion has non-negligible effect which has been taken into account in this work.
In this investigation, motion database of fifteen patients with lung cancer who were treated by means of Cyberknife Synchrony system at Georgetown University hospital, has been used. Two subtractive and fuzzy C-means as common available clustering strategies have been employed in order to investigate their quantitative effects, in a comparative fashion.
Final analyzed results show that the average targeting error of prediction models (difference between tumor position estimated by model and actual position of tumor) over all patients are 6.5 and 7.5 mm implementing subtractive and fuzzy C-means clustering, respectively. Moreover, using fuzzy C-means algorithm, tumor tracking is done with more stability. Since, breathing phenomena has high degree of variations, motion data clustering has an important role on the accuracy of prediction model performance by determining model parameters while constructing at pre-treatment step and while updating the model during the treatment.

Keywords

References

[1] R. L. Siegel , K. D. Miller , and A. Jemal, “Cancer statistics”, A Cancer Journal for Clinician, Vol. 70, 2020, pp. 7–30.
[2] H. Sung , J. Ferlay, and R. L. Siegel “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries”, A Cancer Journal for Clinician, Vol. 71, No. 3, May 2021, pp. 209-249.
[3] C. Mattiuzzi, and G. G. Lippi, “Current Cancer Epidemiology”, Journal of Epidemiology and Global Health, Vol. 9, No. 4, December 2019, pp. 217-222.
[4] I. Dagogo-Jack, and A.T. Shaw, “Tumour heterogeneity and resistance to cancer therapies”, Nature Review Clinical Oncology, Vol. 15, No. 2, 2018, pp. 81–94.
[5] Y. Yagawa, K. Tanigawa, and Y. Kobayashi, “Cancer immunity and therapy using hyperthermia with immunotherapy, radiotherapy, chemotherapy, and surgery”, Journal of Cancer Metastasis and Treatment, Vol. 3, 2017, pp. 218–230.
[6] A. Badey, A. Barateaue, and N. Delaby,  “Overview of adaptive radiotherapy in 2019: From implementation to clinical use”, Cancer Radiotherapie, Vol. 23, No. 6-7, 2019, pp. 581-591.
[7] A. Webster, A. L. Appelt, and G. Eminowicz. “Image-Guided Radiotherapy for Pelvic Cancers: A Review of Current Evidence and Clinical Utilisation”, Clinical Oncology, Vol. 32, No. 12, 2020, pp. 805-816.
[8] T. A. Moo, R. Sanford, and C. Dang, “Overview of Breast Cancer Therapy”, PET Clinics, Vol. 13, No. 3. July 2018, pp. 339-354.
[9] A. Pashazadeh, A. Boese, and M. Friebe, “Radiation therapy techniques in the treatment of skin cancer: an overview of the current status and outlook”, The Journal of Dermatological Treatment, Vol. 30, No. 8, December 2019, pp. 831-839.
[10] S. Sampath, “Treatment: Radiation Therapy”, Cancer Treatment and Research, Vol. 170, 2016, pp. 105-18.
[11] A. Bujold, T. Craig, and D. Jaffray, “Image-guided radiotherapy: has it influenced patient outcomes?”, Seminars in Radiation Oncology, Vol. 22, No. 1, 2012, pp. 50–61.
[12] M. F. K. Benveniste, J. Welsh, and M.C.B Godoy, “New era of radiotherapy: An update in radiation induced lung disease”, Clinical Radiology, Vol. 68, No. 6, 2013, pp. 275–290.
[13] V. Grégoire, M. Guckenberger, and K. Haustermans, “Image guidance in radiation therapy for better cure of cancer”, Molecular Oncology, Vol. 14, 2020, pp. 1470-1491.
[14] J. Dhont, S. V. Harden, and L. Y. S. Chee, “Image-guided Radiotherapy to Manage Respiratory Motion: Lung and Liver”, Clinical Oncology, Vol. 32, No. 12, 2020, pp. 792-804.
[15] J. Murray, C. Griffin, and S. Gulliford, “A randomised assessment of image guided radiotherapy within a phase 3 trial of conventional or hypo-fractionated high dose intensity modulated radiotherapy for prostate cancer”, Radiotherapy Oncology, Vol. 142, 2020, pp. 62-71.
[16] M. C. Mesías, J. Boda-Heggemann, J. Thoelking,  F. Lohr, F. Wenz, and H. Wertz, “Quantification and assessment of interfraction setup errors based on cone beam CT and determination of safety margins for radiotherapy”, PLoS ONE., Vol. 11, No. 3, 2016, pp. 1-8.
[17] M. R. Gaddy, J. Unkelbach, and D. Papp, “Robust spatiotemporal fractionation schemes in the presence of patient setup uncertainty”, Medical Physics, Vo. 46, No. 7, July 2019, pp. 2988-3000. 
[18] K. M. Jason, D. Tejan, and W. S. James, “Advances in the use of motion management and image guidance in radiation therapy treatment for lung cancer”, Journal of Thoracic Disease, Vol. 10, 2018, pp. 2437–2450.
[19] J. Hanley, M. M. Debois, and D. Mah, “Deep inspiration breath-hold technique for lung tumors: The potential value of target immobilization and reduced lung density in dose escalation”, Intentional Journal of Radiation Oncology Biology Physics, Vol. 45, 1999, pp. 603-611.
[20] D. Mah, J. Hanley, and K.E. Rosenzweig, “Technical aspects of the deep inspiration breath-hold technique in the treatment of thoracic cancer”. Intentional Journal of Radiation Oncology Biology Physics, Vol. 48, 2000, pp. 1175-1185.
[21] K. E. Rosenzweig, J. Hanley, D. Mah, G. Mageras, M. Hunt, S. Toner, C. Burman, C. C. Ling, B. Mychalczak, Z. Fuks, and S. A. Leibel,  “The deep inspiration breath-hold technique in the treatment of inoperable non-small-cell lung cancer”, Intentional Journal of Radiation Oncology Biology Physics, Vol. 48, 2000, pp. 81–87.
[22] J. S. Ginn, D. Ruan, and D. A. Low, “Multislice motion modeling for MRI-guided radiotherapy gating”, Medical Physics, Vol. 46, No. 2, February 2019, pp. 465-474. 
[23] J. S. Ginn, D. A. Low, and J. M. Lamb, “A motion prediction confidence estimation framework for prediction-based radiotherapy gating”, Medical Physics, Vol. 47, No. 8, August 2020, pp. 3297-3304.
[24] S. A. Oh, J. W. Yea, and S. K. Kim, “Optimal Gating Window for Respiratory-Gated Radiotherapy with Real-Time Position Management and Respiration Guiding System for Liver Cancer Treatment”, Scientific Reports, Vol. 9, No. 1, March 2019, pp. 4384. 
[25] R. Thiyagarajan, S. N. Sinha, and R. Ravichandran, “Respiratory gated radiotherapy-pretreatment patient specific quality assurance”, Journal of Medical Physics, Vol. 41, No. 1, 2016, pp. 65–70.
[26] J. T. Booth, V. Caillet, and N. Hardcastle, “The first patient treatment of electromagnetic-guided real time adaptive radiotherapy using MLC tracking for lung. SABR”, Radiotherapy and Oncology, Vol. 121, No. 1, 2016, pp. 19-25.
[27] H. Shirato, S. Shimizu, and T. Shimizu, “Real-time tumor-tracking radiotherapy”, Lancet, Vol. 353, 1999, pp. 1331-1332.
[28] Y. Kamino, K. Takayama, and M. Kokubo, “Development of a four-dimensional image-guided radiotherapy system with a gimbaled X-ray head”, Intentional Journal of Radiation Oncology Biology Physics, Vol. 66, No. 1, 2006, pp. 271-278.
[29] N. Katoh, H. Onishi, and Y. Uchinami, “Real-Time Tumor-Tracking Radiotherapy and General Stereotactic Body Radiotherapy for Adrenal Metastasis in Patients With Oligometastasis”, Technology in Cancer Research and Treatment, Vol. 17, January 2018, pp. 1-9.
[30] P. J. Keall, G. S. Mageras, and J. M. Balter, “The Management of Respiratory Motion in Radiation Oncology report of AAPM task group 76”, Medical Physics, Vol. 33, 2006, pp. 3874-3900.
[31] Q. Xu, R. J. Hamilton, and R. A. Schowengerdt, “Lung tumor tracking in fluoroscopic video based on optical flow”, Medical Physics, Vol. 35, 2008, pp. 5351–5359.
 [32] M. Inoue, H. Shiomi, and H. Iwata, “Development of system using beam's eye view images to measure respiratory motion tracking errors in image-guided robotic radiosurgery system”, Journal of Applied Clinical Medical Physics, Vol. 16, No. 1, 2015, pp. 100-111. 
[33] M. Mafi, and S. M. Moghadam, “Real-time prediction of tumor motion using a dynamic neural network”, Medical and Biological Engineering and Computing, Vol. 58, No. 3, March 2020, pp. 529-539.
[34] Y. Akino, H. Shiomi, and I. Sumida, “Impacts of respiratory phase shifts on motion-tracking accuracy of the CyberKnife Synchrony™ Respiratory Tracking System”, Medical Physics, Vol. 46, No. 9, September 2019, pp. 3757-3766.
[35] E. W. Pepin, H. Wu, and Y. Zhang, “Correlation and prediction uncertainties in the cyberknife synchrony respiratory tracking system”, Medical Physics, Vol. 38, No. 7, 2011, pp. 4036-4044.
 [36] F. Ernst, R. Bruder, and A. Schlaefer, “Correlation between external and internal respiratory motion: a validation study”, International Journal of Computer Assisted Radiology and Surgery, Vol. 7, No. 3, 2012, pp. 483-492.
[37] K. Poels, J. Dhont, and D. Verellen, “A comparison of two clinical correlation models used for real-time tumor tracking of semi-periodic motion: A focus on geometrical accuracy in lung and liver cancer patients”, Radiotherapy and Oncology, Vol. 115, No. 3, 2015, pp. 419-424.
[38] M. Inoue, K. Okawa, and J. Taguchi, “Factors affecting the accuracy of respiratory tracking of the image-guided robotic radiosurgery system”, Japanese Journal of Radiology, Vol. 37, No. 10, October 2019, pp. 727-734.
[39] Y. Seppenwoolde, R. I. Berbeco, and S. Nishioka, “Accuracy of tumor motion compensation algorithm from a robotic respiratory tracking system: a simulation study”, Medical Physics, Vol. 34, 2007, pp. 2774-2784.
[40] A. Esmaili Torshabi, A. Pella, and M. Riboldi, “Targeting accuracy in real-time tumor tracking via external surrogates: a comparative study”, Technology in Cancer Research and Treatment, Vol. 9, 2010, pp. 551–562.
[41] A. Esmaili Torshabi, M. Riboldi, and A. A. Imani Fooladi, “An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates”, Journal of Applied Clinical Medical Physics Vol. 14, 2013, pp. 102-114.
[42] A. Esmaili Torshabi, “Investigation the Robustness of Adaptive Neuro-Fuzzy Inference System for Tracking of Moving Tumors in External Radiotherapy”, Australian Physical and Engineering Sciences in Medicine, Vol. 37, 2014, pp. 771-778.
[43] S. Nankali, A. Esmaili Torshabi, and P. Samadi Miyandoab, “A feasibility study on ribs as anatomical landmarks for motion tracking of lung and liver tumors at external beam radiotherapy”, Technology in Cancer Research and Treatment, Vol. 16, No. 1, 2017, pp. 99-111.
 [44] S. Nankali, A. Esmaili Torshabi, and P. Samadi Miandoab, “Investigation on performance accuracy of different external surrogates in real time tumor tracking at external beam radiotherapy”, Frontiers in Biomedical Technologies, Vol. 2, No. 2, 2015, pp. 381-387.
[45] S. Nankali, A. Esmaili Torshabi, P. Samadi Miandoab, and A. Baghizadeh, “Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy, a virtual phantom study”, Journal of Applied Clinical Medical Physics, Vol. 16, No. 1, 2016, pp. 221-233.
[46] L. Ghorbanzadeh, and A. Esmaili Torshabi, “Investigation the performance of adaptive neuro-fuzzy inference system for brain tumor delineation using expectation maximization cluster method; a feasibility study”, Frontiers in Biomedical Technologies, Vol. 3, 2016, pp. 8-19.
[47] A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review”, ACM Computing Surveys, Vol. 31, 1999, pp. 264-323.
[48]  طاهره علی حیدری بیوکی و حسن خادمی زارع،" بهبود روش تحلیل پوششی داده‌ها به منظور خوشه‌بندی مشتریان اعتباری بانک ها"، مجله مدلسازی در مهندسی، دوره 13، شماره 41، تابستان 1394، صفحه 59-74.
[49] L. A. Zadeh, “Fuzzy Sets”, Information and Control, Vol. 8, 1965, pp. 338-353.
[50] L. A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, 1973, pp. 28-44.
[51] L. A. Zadeh, “Fuzzy algorithm”, Information and Control, Vol. 12, 1968, pp. 94-102.
[52]  پانیذ تیموری، مهدی مزینانی و راحیل حسینی، "ارائه یک مدل هوشمند قطعه‌بندی مبتنی بر منطق فازی و تبدیل موجک گسسته در تصاویر دیجیتالی جهت شناسایی سرطان معده"، مجله مدلسازی در مهندسی، دوره 18، شماره 63، زمستان 1399، صفحه 131-150.
[53] هادی سلطانی زاده و نگارین جوادی،" شناسایی آسیبهای پوستی با استفاده از الگوریتم فازی"، مجله مدلسازی در مهندسی، سال هفدهم، شماره 59، زمستان 1398، صفحه 277-285.
[54] T. Takagi, and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control”, IEEE Transactions on systems, Man and Cybernetics, Vol. 15, 1985, pp. 116-132.
Journal of Modeling in Engineering
Volume 20, Issue 68
March 2022
Pages 73-83

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History

  • Receive Date: 13 May 2021
  • Revise Date: 07 September 2021
  • Accept Date: 15 September 2021

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