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Journal articles on the topic 'Tumour control probability'

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Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Bassler, Niels, Jakob Toftegaard, Armin Lühr, Brita Singers Sørensen, Emanuele Scifoni, Michael Krämer, Oliver Jäkel, Lise Saksø Mortensen, Jens Overgaard, and Jørgen B. Petersen. "LET-painting increases tumour control probability in hypoxic tumours." Acta Oncologica 53, no. 1 (September 10, 2013): 25–32. http://dx.doi.org/10.3109/0284186x.2013.832835.

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2

Ebert, M. A., and P. W. Hoban. "Some characteristics of tumour control probability for heterogeneous tumours." Physics in Medicine and Biology 41, no. 10 (October 1, 1996): 2125–33. http://dx.doi.org/10.1088/0031-9155/41/10/019.

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3

Tarnawski, Rafal, Krzysztof Skladowski, and Andrzej Swierniak. "How Treatment Gaps Effect Tumour Control Probability." IFAC Proceedings Volumes 33, no. 3 (March 2000): 161–65. http://dx.doi.org/10.1016/s1474-6670(17)35507-6.

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4

Dhawan, Andrew, Mohammad Kohandel, Richard Hill, and Sivabal Sivaloganathan. "Tumour Control Probability in Cancer Stem Cells Hypothesis." PLoS ONE 9, no. 5 (May 8, 2014): e96093. http://dx.doi.org/10.1371/journal.pone.0096093.

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5

Maler, A., and F. Lutscher. "Cell-cycle times and the tumour control probability." Mathematical Medicine and Biology 27, no. 4 (December 6, 2009): 313–42. http://dx.doi.org/10.1093/imammb/dqp024.

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6

Gong, J., M. M. Dos Santos, C. Finlay, and T. Hillen. "Are more complicated tumour control probability models better?" Mathematical Medicine and Biology 30, no. 1 (October 17, 2011): 1–19. http://dx.doi.org/10.1093/imammb/dqr023.

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7

Malinen, E. "SP-0207: Image-based radiobiological tumour control probability modelling." Radiotherapy and Oncology 119 (April 2016): S94. http://dx.doi.org/10.1016/s0167-8140(16)31456-6.

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8

Levin-Plotnik, D., and R. J. Hamilton. "Optimization of tumour control probability for heterogeneous tumours in fractionated radiotherapy treatment protocols." Physics in Medicine and Biology 49, no. 3 (January 16, 2004): 407–24. http://dx.doi.org/10.1088/0031-9155/49/3/005.

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9

Wiklund, Kristin, Iuliana Toma-Dasu, and Bengt K. Lind. "Impact of Dose and Sensitivity Heterogeneity on TCP." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/182935.

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This present paper presents an analytical description and numerical simulations of the influence of macroscopic intercell dose variations and intercell sensitivity variations on the probability of controlling the tumour. Computer simulations of tumour control probability accounting for heterogeneity in dose and radiation sensitivity were performed. An analytical expression for tumor control probability accounting for heterogeneity in sensitivity was also proposed and validated against simulations. The results show good agreement between numerical simulations and the calculated TCP using the proposed analytical expression for the case of a heterogeneous dose and sensitivity distributions. When the intratumour variations of dose and sensitivity are taken into account, the total dose required for achieving the same level of control as for the case of homogeneous distribution is only slightly higher, the influence of the variations in the two factors taken into account being additive. The results of this study show that the interplay between cell or tumour variation in the sensitivity to radiation and the inherent heterogeneity in dose distribution is highly complex and therefore should be taken into account when predicting the outcome of a given treatment in terms of tumor control probability.

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10

Cho, G. A., M. A. Ebert, L. Holloway, Z. Kuncic, C. Baldock, and D. I. Thwaites. "Radiation treatment dose optimisation using Poisson tumour control probability parameters." Journal of Physics: Conference Series 489 (March 24, 2014): 012047. http://dx.doi.org/10.1088/1742-6596/489/1/012047.

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11

Espensen, Charlotte A., Ane L. Appelt, Lotte S. Fog, Juliette Thariat, Anita B. Gothelf, Marianne C. Aznar, and Jens F. Kiilgaard. "Tumour control probability after Ruthenium-106 brachytherapy for choroidal melanomas." Acta Oncologica 59, no. 8 (May 15, 2020): 918–25. http://dx.doi.org/10.1080/0284186x.2020.1762925.

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12

Tom, Wolfgang A., and Jack F. Fowler. "On the inclusion of proliferation in tumour control probability calculations for inhomogeneously irradiated tumours." Physics in Medicine and Biology 48, no. 18 (September 4, 2003): N261—N268. http://dx.doi.org/10.1088/0031-9155/48/18/402.

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13

Dinkla, Anna M., Bradley R. Pieters, Kees Koedooder, Niek van Wieringen, Rob van der Laarse, Johan N. van der Grient, Coen R. Rasch, Caro C. Koning, and Arjan Bel. "Improved tumour control probability with MRI-based prostate brachytherapy treatment planning." Acta Oncologica 52, no. 3 (January 3, 2013): 658–65. http://dx.doi.org/10.3109/0284186x.2012.744875.

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14

Gonzalez, S. J., and D. G. Carando. "A general tumour control probability model for non-uniform dose distributions." Mathematical Medicine and Biology 25, no. 2 (May 25, 2008): 171–84. http://dx.doi.org/10.1093/imammb/dqn012.

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15

Buizza, G., S. Molinelli, E. D'Ippolito, G. Fontana, L. Anemoni, L. Preda, G. Baroni, F. Valvo, and C. Paganelli. "PV-0311 MRI-based tumour control probability model in particle therapy." Radiotherapy and Oncology 133 (April 2019): S159—S160. http://dx.doi.org/10.1016/s0167-8140(19)30731-5.

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16

Johnsson, Anders, Otilia Leon, Adalsteinn Gunnlaugsson, Per Nilsson, and Peter Höglund. "Determinants for local tumour control probability after radiotherapy of anal cancer." Radiotherapy and Oncology 128, no. 2 (August 2018): 380–86. http://dx.doi.org/10.1016/j.radonc.2018.06.007.

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17

O’Rourke, S. F. C., H. McAneney, and T. Hillen. "Linear quadratic and tumour control probability modelling in external beam radiotherapy." Journal of Mathematical Biology 58, no. 4-5 (September 30, 2008): 799–817. http://dx.doi.org/10.1007/s00285-008-0222-y.

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18

Bassler, N., O. Jäkel, M. Krämer, A. Lühr, J. Overgaard, L. Saksø Mortensen, E. Scifoni, B. Singers Sørensen, and J. Toftegaard. "15: Oxygen ions achieve better tumour control probability in hypoxic tumours than carbon ions do." Radiotherapy and Oncology 110 (February 2014): S7—S8. http://dx.doi.org/10.1016/s0167-8140(15)34036-6.

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19

Søvik, Åste, Eirik Malinen, Øyvind S. Bruland, Søren M. Bentzen, and Dag Rune Olsen. "Optimization of tumour control probability in hypoxic tumours by radiation dose redistribution: a modelling study." Physics in Medicine and Biology 52, no. 2 (December 29, 2006): 499–513. http://dx.doi.org/10.1088/0031-9155/52/2/013.

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20

Partridge, Mike, Alison Tree, Juliet Brock, Helen McNair, Elizabeth Fernandez, Niki Panakis, and Michael Brada. "Improvement in tumour control probability with active breathing control and dose escalation: A modelling study." Radiotherapy and Oncology 91, no. 3 (June 2009): 325–29. http://dx.doi.org/10.1016/j.radonc.2009.03.017.

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21

Webb, S. "Optimum parameters in a model for tumour control probability including interpatient heterogeneity." Physics in Medicine and Biology 39, no. 11 (November 1, 1994): 1895–914. http://dx.doi.org/10.1088/0031-9155/39/11/007.

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22

Daşu, Alexandru, Iuliana Toma-Daşu, and Mikael Karlsson. "The effects of hypoxia on the theoretical modelling of tumour control probability." Acta Oncologica 44, no. 6 (January 2005): 563–71. http://dx.doi.org/10.1080/02841860500244435.

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23

Haworth, Annette, Martin Ebert, David Waterhouse, David Joseph, and Gillian Duchesne. "Prostate implant evaluation using tumour control probability—the effect of input parameters." Physics in Medicine and Biology 49, no. 16 (July 31, 2004): 3649–64. http://dx.doi.org/10.1088/0031-9155/49/16/012.

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24

Uusijärvi, Helena, Peter Bernhardt, and Eva Forssell-Aronsson. "Tumour control probability (TCP) for non-uniform activity distribution in radionuclide therapy." Physics in Medicine and Biology 53, no. 16 (July 25, 2008): 4369–81. http://dx.doi.org/10.1088/0031-9155/53/16/010.

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25

Dawson, A., and T. Hillen. "Derivation of the Tumour Control Probability (TCP) from a Cell Cycle Model." Computational and Mathematical Methods in Medicine 7, no. 2-3 (2006): 121–41. http://dx.doi.org/10.1080/10273660600968937.

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In this paper, a model for the radiation treatment of cancer which includes the effects of the cell cycle is derived from first principles. A malignant cell population is divided into two compartments based on radiation sensitivities. The active compartment includes the four phases of the cell cycle, while the quiescent compartment consists of theG0state. Analysis of this active-quiescent radiation model confirms the classical interpretation of the linear quadratic (LQ) model, which is that a larger α/β ratio corresponds to a fast cell cycle, while a smaller ratio corresponds to a slow cell cycle. Additionally, we find that a large α/β ratio indicates the existence of a significant quiescent phase. The active-quiescent model is extended as a nonlinear birth–death process in order to derive an explicit time dependent expression for the tumour control probability (TCP). This work extends the TCP formula from Zaider and Minerbo and it enables the TCP to be calculated for general time dependent treatment schedules.

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26

Sandström, Helena, Alexandru Dasu, and Iuliana Toma-Dasu. "Radiobiological Framework for the Evaluation of Stereotactic Radiosurgery Plans for Invasive Brain Tumours." ISRN Oncology 2013 (December 29, 2013): 1–5. http://dx.doi.org/10.1155/2013/527251.

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This study presents a radiobiological formalism for the evaluation of the treatment plans with respect to the probability of controlling tumours treated with stereotactic radiosurgery accounting for possible infiltrations of malignant cells beyond the margins of the delineated target. Treatments plans devised for three anaplastic astrocytoma cases were assumed for this study representing cases with different difficulties for target coverage. Several scenarios were considered regarding the infiltration patterns. Tumour response was described in terms of tumour control probability (TCP) assuming a Poisson model taking into account the initial number of clonogenic cells and the cell survival. The results showed the strong impact of the pattern of infiltration of tumour clonogens outside the delineated target on the outcome of the treatment. The treatment plan has to take into account the existence of the possible microscopic disease around the visible lesion; otherwise the high gradients around the target effectively prevent the sterilisation of the microscopic spread leading to low probability of control, in spite of the high dose delivered to the target. From this perspective, the proposed framework offers a further criterion for the evaluation of stereotactic radiosurgery plans taking into account the possible infiltration of tumour cells around the visible target.

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27

Selvaraj, Jothybasu, Colin Baker, and Alan Nahum. "Impact of microscopic disease extension, extra-CTV tumour islets, incidental dose and dose conformity on tumour control probability." Australasian Physical & Engineering Sciences in Medicine 39, no. 2 (May 11, 2016): 493–500. http://dx.doi.org/10.1007/s13246-016-0446-x.

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28

Godói, M., and P. Nicolucci. "THEORETICAL ANALYSIS OF FLASH EFFECT ON TUMOUR CONTROL AND NORMAL TISSUE COMPLICATION PROBABILITY." Physica Medica 94 (February 2022): S116—S117. http://dx.doi.org/10.1016/s1120-1797(22)01711-2.

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29

Forouzannia, Farinaz, Sivabal Sivaloganathan, and Mohammad Kohandel. "A mathematical study of the impact of cell plasticity on tumour control probability." Mathematical Biosciences and Engineering 17, no. 5 (2020): 5250–66. http://dx.doi.org/10.3934/mbe.2020284.

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30

Her, E. J., H. M. Reynolds, C. Mears, S. Williams, C. Moorehouse, J. L. Millar, M. A. Ebert, and A. Haworth. "Radiobiological parameters in a tumour control probability model for prostate cancer LDR brachytherapy." Physics in Medicine & Biology 63, no. 13 (June 27, 2018): 135011. http://dx.doi.org/10.1088/1361-6560/aac814.

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31

Ye, Sung-Joon. "Monte Carlo based protocol for cell survival and tumour control probability in BNCT." Physics in Medicine and Biology 44, no. 2 (January 1, 1999): 447–61. http://dx.doi.org/10.1088/0031-9155/44/2/012.

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32

Zaider, M., and G. N. Minerbo. "Tumour control probability: a formulation applicable to any temporal protocol of dose delivery." Physics in Medicine and Biology 45, no. 2 (December 22, 1999): 279–93. http://dx.doi.org/10.1088/0031-9155/45/2/303.

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33

Pozo, M. B. Rios, R. E. Pacios, D. Guirado, I. Castillo, M. T. Delgado, R. Guerrero, and J. L. Garcia-Puche. "2046 Interruptions in fractionated radiotherapy: incidence, causes and impact in tumour control probability." European Journal of Cancer Supplements 7, no. 2 (September 2009): 164–65. http://dx.doi.org/10.1016/s1359-6349(09)70562-2.

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34

Wiklund, Kristin, Iuliana Toma-Dasu, and Bengt K. Lind. "The influence of dose heterogeneity on tumour control probability in fractionated radiation therapy." Physics in Medicine and Biology 56, no. 23 (November 15, 2011): 7585–600. http://dx.doi.org/10.1088/0031-9155/56/23/016.

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35

Lucas, S., O. Feron, B. Gallez, B. Masereel, C. Michiels, and T. Vander Borght. "Monte Carlo Calculation of Radioimmunotherapy with90Y-,177Lu-,131I-,124I-, and188Re-Nanoobjects: Choice of the Best Radionuclide for Solid Tumour Treatment by Using TCP and NTCP Concepts." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/284360.

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Radioimmunotherapy has shown that the use of monoclonal antibodies combined with a radioisotope like131I or90Y still remains ineffective for solid and radioresistant tumour treatment. Previous simulations have revealed that an increase in the number of90Y labelled to each antibody or nanoobject could be a solution to improve treatment output. It now seems important to assess the treatment output and toxicity when radionuclides such as90Y,177Lu,131I,124I, and188Re are used. Tumour control probability (TCP) and normal tissue complication probability (NTCP) curves versus the number of radionuclides per nanoobject were computed with MCNPX to evaluate treatment efficacy for solid tumours and to predict the incidence of surrounding side effects. Analyses were carried out for two solid tumour sizes of 0.5 and 1.0 cm radius and for nanoobject (i.e., a radiolabelled antibody) distributed uniformly or nonuniformly throughout a solid tumour (e.g., Non-small-cell-lung cancer (NSCLC)).90Y and188Re are the best candidates for solid tumour treatment when only one radionuclide is coupled to one carrier. Furthermore, regardless of the radionuclide properties, high values of TCP can be reached without toxicity if the number of radionuclides per nanoobject increases.

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36

Wright, Pauliina, Ludvig P. Muren, Morten Høyer, and Eirik Malinen. "Evaluation of adaptive radiotherapy of bladder cancer by image-based tumour control probability modelling." Acta Oncologica 49, no. 7 (September 13, 2010): 1045–51. http://dx.doi.org/10.3109/0284186x.2010.498431.

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37

Kallman, P., B. K. Lind, and A. Brahme. "An algorithm for maximizing the probability of complication-free tumour control in radiation therapy." Physics in Medicine and Biology 37, no. 4 (April 1, 1992): 871–90. http://dx.doi.org/10.1088/0031-9155/37/4/004.

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38

Ponce Bobadilla, Ana Victoria, Philip K. Maini, and Helen Byrne. "A stochastic model for tumour control probability that accounts for repair from sublethal damage." Mathematical Medicine and Biology: A Journal of the IMA 35, no. 2 (February 26, 2017): 181–202. http://dx.doi.org/10.1093/imammb/dqw024.

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39

Buizza, Giulia, Silvia Molinelli, Emma D'Ippolito, Giulia Fontana, Andrea Pella, Francesca Valvo, Lorenzo Preda, Roberto Orecchia, Guido Baroni, and Chiara Paganelli. "MRI-based tumour control probability in skull-base chordomas treated with carbon-ion therapy." Radiotherapy and Oncology 137 (August 2019): 32–37. http://dx.doi.org/10.1016/j.radonc.2019.04.018.

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40

Fleming, C., S. O'Keeffe, J. Armstrong, and B. McClean. "EP-1712: Increased tumour control probability (TCP) with inhomogeneous dose escalated distributions in NSCLC." Radiotherapy and Oncology 119 (April 2016): S800—S801. http://dx.doi.org/10.1016/s0167-8140(16)32963-2.

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41

Foley, D., B. McClean, and P. McBride. "EP-1660: Improvement in tumour control probability by adapting dose to daily OAR DVCs." Radiotherapy and Oncology 123 (May 2017): S902. http://dx.doi.org/10.1016/s0167-8140(17)32192-8.

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42

Lechner, A., M. Blaickner, S. Gianolini, K. Poljanc, H. Aiginger, and D. Georg. "Targeted radionuclide therapy: theoretical study of the relationship between tumour control probability and tumour radius for a32P/33P radionuclide cocktail." Physics in Medicine and Biology 53, no. 7 (March 18, 2008): 1961–74. http://dx.doi.org/10.1088/0031-9155/53/7/011.

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43

Baliga, Sujith, Madhur K. Garg, Jana Fox, Shalom Kalnicki, Patrick A. Lasala, Mary R. Welch, Wolfgang A. Tomé, and Nitin Ohri. "Fractionated stereotactic radiation therapy for brain metastases: a systematic review with tumour control probability modelling." British Journal of Radiology 90, no. 1070 (February 2017): 20160666. http://dx.doi.org/10.1259/bjr.20160666.

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44

Baker, C., M. Chandrasekaran, J. Uzan, and A. Nahum. "PO-0908 TUMOUR CONTROL PROBABILITY MODELLING DEMANDS PARAMETERS CONSISTENT WITH THE TREATMENT PLANNING DOSE ALGORITHM." Radiotherapy and Oncology 103 (May 2012): S358. http://dx.doi.org/10.1016/s0167-8140(12)71241-0.

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45

Wiklund, K., I. Toma-Dasu, and B. K. Lind. "EP-1491 THE IMPACT OF DOSE HETEROGENEITY IN FRACTIONATED RADIATION THERAPY ON TUMOUR CONTROL PROBABILITY." Radiotherapy and Oncology 103 (May 2012): S570. http://dx.doi.org/10.1016/s0167-8140(12)71824-8.

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46

Farrow, H., C. Bakers, and A. Nahum. "486 Dependence of tumour control probability on the distribution of clonogen number over a population." Radiotherapy and Oncology 76 (September 2005): S209. http://dx.doi.org/10.1016/s0167-8140(05)81462-8.

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47

Dutreix, J., M. Tubiana, and A. Dutreix. "An approach to the interpretation of clinical data on the tumour control probability-dose relationship." Radiotherapy and Oncology 11, no. 3 (March 1988): 239–48. http://dx.doi.org/10.1016/0167-8140(88)90006-0.

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48

Booth, Jeremy T., and Sergei F. Zavgorodni. "The effects of radiotherapy treatment uncertainties on the delivered dose distribution and tumour control probability." Australasian Physics & Engineering Sciences in Medicine 24, no. 2 (June 2001): 71–78. http://dx.doi.org/10.1007/bf03178349.

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49

Staleska, Miss Ivona Zlatkova, and Mr Pavel Stavrev. "FORMULAS FOR CALCULATING THE BIOLOGICALLY EQUIVALENT DOSE BASED ON THE IDEA OF TUMOUR CONTROL PROBABILITY." Physica Medica 104 (December 2022): S170—S171. http://dx.doi.org/10.1016/s1120-1797(22)02529-7.

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50

Gasparini, Alessandro, and Keith Humphreys. "Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data." Statistical Methods in Medical Research 31, no. 5 (February 1, 2022): 862–81. http://dx.doi.org/10.1177/09622802211072496.

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We propose a framework for jointly modelling tumour size at diagnosis and time to distant metastatic spread, from diagnosis, based on latent dynamic sub-models of growth of the primary tumour and of distant metastatic detection. The framework also includes a sub-model for screening sensitivity as a function of latent tumour size. Our approach connects post-diagnosis events to the natural history of cancer and, once refined, may prove useful for evaluating new interventions, such as personalised screening regimes. We evaluate our model-fitting procedure using Monte Carlo simulation, showing that the estimation algorithm can retrieve the correct model parameters, that key patterns in the data can be captured by the model even with misspecification of some structural assumptions, and that, still, with enough data it should be possible to detect strong misspecifications. Furthermore, we fit our model to observational data from an extension of a case-control study of post-menopausal breast cancer in Sweden, providing model-based estimates of the probability of being free from detected distant metastasis as a function of tumour size, mode of detection (of the primary tumour), and screening history. For women with screen-detected cancer and two previous negative screens, the probabilities of being free from detected distant metastases 5 years after detection and removal of the primary tumour are 0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. We also study the probability of having latent/dormant metastases at detection of the primary tumour, estimating that 33% of patients in our study had such metastases.

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