A pioneering method in tumor diagnosis: Bioheat transfer analysis via hybrid model consisting of finite differences time domain and genetic algorithms

Öz

Early diagnosis is important for cancer treatment, cure and survival. Tumors alter tissue thermal properties, blood perfusion and metabolic heat production, and skin surface temperature. This study proposes a hybrid FDTD–GA (finite difference time domain–genetic algorithm) model as a non-invasive screening and decision-support tool for the pre-diagnosis of tumorous tissue before conventional diagnostic methods. The proposed approach aims to reduce the risks associated with imaging and invasive techniques such as MRI, biopsy, PET, mammography, and CT, while also alleviating the workload of healthcare professionals. In addition, early-stage tumors that may not be detected by standard diagnostic tools can be identified. In the proposed model, FDTD and GA operate interconnectedly. The FDTD solves the Pennes bioheat transfer equation using measured skin surface temperature as a reference and computes tissue temperatures for GA. GA estimates blood perfusion and metabolic heat production for FDTD. Since tumorous tissues exhibit significantly higher blood perfusion and metabolic heat production than healthy tissues, these parameters are effective diagnostic indicators. The model converges to tissue parameters consistent with the skin temperature. The results indicate that metabolic heat production is the dominant factor in distinguishing tumorous tissue, and calculated tissue temperatures match the reference skin surface temperature.

Anahtar Kelimeler

• Pennes bioheat equation
• finite differences time domain
• genetic algorithms
• blood perfusion rate
• metabolic heat production

Tümör tanısında öncü bir yöntem: Zaman domeninde sonlu farklar ve genetik algoritmalar hibrit modeliyle biyoısı transfer analizi

Öz

Kanser tedavisinde erken tanı, iyileşme ve sağkalım süresi açısından çok önemlidir. Dokudaki bir tümör, doku sıcaklık profilini, kan perfüzyonunu, metabolik ısı üretimini ve cilt yüzey sıcaklığını değiştirir. Bu çalışmada, FDTD-GA (zaman domeninde sonlu farklar-genetik algoritmalar) hibrit modeliyle, diğer tanı yöntemlerinden önce tümörlü/sağlıklı doku ön tanısı hedeflenmiş, gereksiz yere hem MRI, biyopsi, PET, mamografi, BT gibi tanı yöntemlerinin olası zararlarının hem de hekim, sağlık çalışanı meşguliyetinin önüne geçilmesi amaçlanmıştır. Diğer tanı yöntemlerinin henüz tanıyamadığı erken evredeki tümör, FDTD-GA modeliyle farkedilebilir. Tümör, cilt yüzey sıcaklığını artırır. FDTD-GA modelinde, FDTD, Pennes biyoısı transfer denklemini cilt yüzey sıcaklığı referansıyla çözer. Literatürden alınan farklı dokuların fiziksel ve termal parametrelerine göre, FDTD-GA modeliyle, hem literatürde verilen kan perfüzyonu ve metabolik ısı değerlerine hem de tümörlü dokularda sağlıklı dokulardan çok daha yüksek kan perfüzyonu ve metabolik ısı değerleri bulunmasına göre tümörlü doku ön tanısı konulabilmiştir. Modelde, FDTD ve GA içiçe, birbirine bağlı çalışmaktadır. Her generasyonda, FDTD, GA için doku sıcaklıklarını hesaplar. GA, FDTD için kan perfüzyonu ve metabolik ısı üretim değerlerini bulur. Sonuçta, cilt yüzey sıcaklığına karşılık gelen doku kan perfüzyon oranı ve metabolik ısı değerleri bulunur. Tanıda, metabolik ısı üretiminin daha baskın olduğu söylenebilir. Tümörlü/sağlıklı doku tanılamalarında, cilt yüzey referans sıcaklığına neredeyse eşit doku sıcaklıklarına ulaşılmıştır.

Anahtar Kelimeler

• Pennes biyoısı denklemi
• zaman domeninde sonlu farklar
• genetik algoritmalar
• kan perfüzyon oranı
• metabolik ısı üretim

Kaynakça

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