بررسی خوشه بندی داده های حرکتی تومور ریه روی تخمین مکان آن با استفاده از مدل پیش بین در پرتودرمانی مبتنی بر نشانگرهای خارجی

نوع مقاله : مقاله کامپیوتر

نویسنده

دانشکده علوم و فناوریهای نوین، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، اتوبان هفت باغ، کرمان، ایران

چکیده

در بین تومورهای بالاتنه، تومورهای ریه عمدتا تحت تاثیر تنفس حرکت می کنند. برای بالا بردن دقت پرتودرمانی یک راه حل این است که حرکت تومور را از روی حرکت خارجی قفسه سینه و ناحیه شکمی تخمین بزنیم. برای این منظور، مدلهای پیش‌بین سازگاری برای ردیابی زمان واقعی تومور ساخته و استفاده می‌گردند. در این مدلها، خوشه‌بندی داده‌های استخراج شده از حرکت تومور و قفسه سینه تاثیر بسزایی روی عملکرد مدل دارند که در این تحقیق مورد توجه قرار گرفته‌اند. در این ارزیابی، داده حرکتی پانزده بیمار دارای تومور ریه که توسط سیستم پرتودرمانی سایبرنایف در مرکز پزشکی دانشگاه جرج تاون درمان شدند، مورد استفاده قرار گرفته است. دو استراتژی رایج و موجود با نامهای افتراقی وC میانگین فازی در خوشه‌بندی داده‌های حرکتی استفاده شده تا تاثیر کمی هر کدام بصورت مقایسه ای بررسی گردد. آنالیز نهایی نتایج نشان می‌دهد که مقدار میانگین خطای هدف گیری مدل پیش‌بین یعنی فاصله بین مکان پیش بینی شده توسط مدل و مکان واقعی تومور، روی همه بیماران با اعمال روش خوشه‌بندی C میانگین فازی و خوشه‌بندی افتراقی به ترتیب 5/6 و 5/7 میلیمتر می‌باشد. بعلاوه، ردیابی مدل با اعمال روش خوشه‌بندی C میانگین فازی با پایداری بیشتری همراه است. از آنجایی که پدیده تنفس دامنه تغییرات بسیار بالایی دارد ، خوشه‌بندی داده‌های حرکتی نقش مهمی روی دقت عمکلرد مدل پیش‌بین با تعیین پارامترهای مدل در حین ساخت آن پیش از درمان و به روزرسانی مدل در حین درمان دارد.

کلیدواژه‌ها

عنوان مقاله [English]

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

نویسنده [English]

  • Ahmad Esmaili Torshabi
Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Haftbagh Highway, Kerman, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • data clustering
  • Radiotherapy
  • lung tumor
  • prediction model
  • fuzzy logic

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مدل سازی در مهندسی
دوره 20، شماره 68
فروردین 1401
صفحه 73-83

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  • تاریخ دریافت: 23 اردیبهشت 1400
  • تاریخ بازنگری: 16 شهریور 1400
  • تاریخ پذیرش: 24 شهریور 1400

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