@article{Sergeevna_Grigorievich_Nikolaevna_Viktorovna_Ruslanovich_Viktorovich_2023, title={Estimation of treatment efficiency of head-and-neck cancer based on tumour control probability model}, volume={4}, url={https://ojs.southfloridapublishing.com/ojs/index.php/jdev/article/view/2022}, DOI={10.46932/sfjdv4n1-018}, abstractNote={<p>External beam radiotherapy is widely used for the treatment of the locally advanced head-and-neck cancer (LAHNC). Analysis of the developed treatment plans based on tumour control probability (TCP) models could help to estimate expected treatment results of the developed plans and to find optimal treatment schemes with respect to total dose, fractional dose and overall treatment time (OTT). In this study, the simultaneous integrated boost VMAT (SIB-VMAT) plans and sequential boost VMAT (SEQ-VMAT) plans were developed based on the anatomical data of 11 patients. Methods and Material: The data of 11 patients with LAHNC (larynx, oropharynx and oral cavity) were used. For each patient two treatment plans were developed, SIB-VMAT (70 Gy to tumour, 50 Gy to lymph nodes, 25 fractions) and SEQ-VMAT (70 Gy to tumour, 50 Gy to lymph nodes, 35 fractions). The developed plans were analysed using the Niemierko’s TCP model with Maciejewski’s parameters (TCD<sub>50</sub> = 70.26 Gy at 49-days OTT) taking into account dose-volume histograms and OTT. Results: The developed plans resulted in high clinical treatment volume (CTV) conformity (98%-98%) for all patients, except one. The average TCP value of SIB-VMAT was equal to 99.9% due to very short OTT. The average value of TCP for SEQ-VMAT was equal to 61.0%. For one patient, the both SIB-VMAT and SEQ-VMAT plans showed zero expected efficiency due to CTV coverage 95%-95%. Conclusions: Use of TCP models allows analysis of treatment plans for each particular patient and development of different treatment schemes with increase of the total dose value, fractional dose and shortening of OTT.</p>}, number={1}, journal={South Florida Journal of Development}, author={Sergeevna, Sukhikh Evgeniya and Grigorievich, Sukhikh Leonid and Nikolaevna, Sutygina Yana and Viktorovna, Verkhoturova Vera and Ruslanovich, Sagov Islam and Viktorovich, Rozanov Vladimir}, year={2023}, month={Feb.}, pages={248–263} }