Lung Tumor Growth Modeling in Patients with NSCLC Undergoing Radiotherapy

被引:3
|
作者
Ghita, Maria [1 ,2 ,3 ]
Chandrashekar, Vasudha [1 ]
Copot, Dana [1 ,2 ]
Billiet, Charlotte [4 ,5 ]
Verellen, Dirk [4 ,5 ]
Ionescu, Clara M. [1 ,2 ,6 ]
机构
[1] Univ Ghent, Res Grp Dynam Syst & Control, B-9052 Ghent, Belgium
[2] Flanders Make Consortium, EEDT Core Lab Decis & Control, B-9052 Ghent, Belgium
[3] Canc Res Inst Ghent, B-9052 Ghent, Belgium
[4] Iridium Canc Network GZA Hosp Sint Augustinus, Dept Radiat Oncol, B-2610 Antwerp, Belgium
[5] Antwerp Univ, Dept Radiotherapy, Fac Med & Hlth Sci, B-2610 Antwerp, Belgium
[6] Tech Univ Cluj Napoca, Dept Automat Control, Cluj Napoca 400114, Romania
来源
IFAC PAPERSONLINE | 2021年 / 54卷 / 15期
关键词
Mathematical modeling; lung cancer; stereotactic body radiation therapy; tumor dynamics; fractional calculus; CANCER;
D O I
10.1016/j.ifacol.2021.10.261
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes two modeling approaches to predict lung tumor dynamics as an effect of radiotherapy. Real clinical information of non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT) as the primary treatment method has been used for numerical simulations. The classical Gompertz model for tumor volume growth prediction was modified using a fractional parameter and combined with the linear-quadratic model to foresee the effect of SBRT on the targeted tumor. Another approach was implemented by following a pharmacokinetic-pharmacodynamic (PKPD) minimal compartmental model for single therapy with SBRT. Statistical analysis has been carried out to compare the two models. In terms of tumor growth prediction, obtained results indicated a decrease in the total tumor volume for both modeling approaches. A striking observation to emerge from the data comparison is the interesting perspective of fractional tools for further exploration in modeling tumor growth. Copyright (C) 2021 The Authors.
引用
收藏
页码:233 / 238
页数:6
相关论文
共 50 条
  • [1] Assessing Morphometric Complexity in Lung Parenchyma of NSCLC Patients with IPF Undergoing Radiotherapy
    Hwang, J.
    Kim, H.
    JOURNAL OF THORACIC ONCOLOGY, 2023, 18 (11) : S513 - S514
  • [2] Modeling of tumor growth undergoing virotherapy
    Dunia, Ricardo
    Edgar, Thomas F.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2011, 41 (10) : 922 - 935
  • [3] Characterization of the stability of respiration for patients undergoing motion-adaptive lung tumor radiotherapy
    Pokhrel, D.
    Murphy, M.
    MEDICAL PHYSICS, 2007, 34 (06) : 2368 - 2368
  • [4] Radiotherapy for oligometastatic tumor improved the prognosis of patients with non-small cell lung cancer (NSCLC)
    Gong, Hong-yun
    Wang, Yi
    Han, Guang
    Song, Qi-bin
    THORACIC CANCER, 2019, 10 (05) : 1136 - 1140
  • [5] Modeling and computer simulations of tumor growth and tumor response to radiotherapy
    Borkenstein, K
    Levegrün, S
    Peschke, P
    RADIATION RESEARCH, 2004, 162 (01) : 71 - 83
  • [6] Screening for malnutrition in lung cancer patients undergoing radiotherapy
    Barthelemy, Nicole
    Streel, Sylvie
    Donneau, Anne-Francoise
    Coucke, Philippe
    Albert, Adelin
    Guillaume, Michele
    SUPPORTIVE CARE IN CANCER, 2014, 22 (06) : 1531 - 1536
  • [7] Respiratory gating in patients with lung carcinoma undergoing radiotherapy
    Hubers, D.
    van Dieren, E. B.
    Woutersen, D. P.
    Slump, C. H.
    JOURNAL OF RADIOTHERAPY IN PRACTICE, 2022, 21 (04) : 519 - 528
  • [8] ENTERAL NUTRITION IN LUNG CANCER PATIENTS UNDERGOING RADIOTHERAPY
    Hill, A.
    Muir, L.
    JOURNAL OF THORACIC ONCOLOGY, 2012, 7 (09) : S171 - S172
  • [9] Screening for malnutrition in lung cancer patients undergoing radiotherapy
    Nicole Barthelemy
    Sylvie Streel
    Anne-Françoise Donneau
    Philippe Coucke
    Adelin Albert
    Michèle Guillaume
    Supportive Care in Cancer, 2014, 22 : 1531 - 1536
  • [10] Modeling and prediction of lung tumor motion for robotic assisted radiotherapy
    Ma, Lei
    Herrmann, Christian
    Schilling, Klaus
    2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9, 2007, : 189 - 194