Treatment planning optimisation based on imaging tumour proliferation and cell density

Daşu, Alexandru

doi:10.1080/02841860802251583

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Algorithms have been proposed to include information regarding the local density of clonogenic cells and their proliferation rate [65] and they could be easily combined with the algorithms targeting tumour hypoxia. Further improvements of the cure rates could also be achieved by monitoring the tumour and adapting the treatment to the clinically observed variations in radiation responsiveness as proposed for example for the BIOART approach [33].…”

Section: Discussionmentioning

confidence: 99%

Dose prescription and optimisation based on tumour hypoxia

2009

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INTRODUCTION. Tumour hypoxia is an important factor that confers radioresistance to the affected cells and could thus decrease the tumour response to radiotherapy. The development of advanced imaging methods that quantify both the extent and the spatial distribution of the hypoxic regions has created the premises to devise therapies that target the hypoxic regions in the tumour. MATERIALS AND METHODS. The present study proposes an original method to prescribe objectively dose distributions that focus the radiation dose to the radioresistant tumour regions and could therefore spare adjacent normal tissues. The effectiveness of the method was tested for clinically relevant simulations of tumour hypoxia that take into consideration dynamics and heterogeneity of oxygenation. RESULTS AND DISCUSSION. The results have shown that highly heterogeneous dose distributions may lead to significant improvements of the outcome only for static oxygenations. In contrast, the proposed method that involves the segmentation of the dose distributions and the optimisation of the dose prescribed to each segment to account for local heterogeneity may lead to significantly improved local control for clinically-relevant patterns of oxygenation. The clinical applicability of the method is warranted by its relatively easy adaptation to functional imaging of tumour hypoxia obtained with markers with known uptake properties.

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Section: Discussionmentioning

confidence: 99%

Dose prescription and optimisation based on tumour hypoxia

2009

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“…Dose-response parameter estimates were chosen from the existing clinical literature (Williams et al 1985, Fowler 1990, 2001, Fowler 2007, 2008, Yang and Xing 2005a, Ho et al 2009, Qi et al 2012. Head-and-neck tumors were assumed to exhibit α = 0.35 − Gy 1 and β = 0.035 − Gy 2 under well-oxygenated conditions.…”

Section: Resultsmentioning

confidence: 99%

“…As in Malinen et al (2006) and Sovik et al (2007b), we ignore the effect of tumor proliferation for notational simplicity; it can, however, be incorporated without any changes to our methodology. This holds because, according to the standard tumor proliferation model based on tumor doubling times, the proliferation term does not depend on radiation dose (Williams et al 1985, Fowler 1990, 2001, Fowler 2007, 2008, Yang and Xing 2005a, Ho et al 2009, Qi et al 2012. Consequently, the proliferation term factors out of our objective function as a constant multiplier and does not affect the optimal fluence-maps.…”

Section: Oermentioning

confidence: 93%

“…FMISO was the first radiopharmaceutical employed to quantitatively image hypoxia in human tumors with PET (Koh et al 1992, Rasey et al 1996, Foo et al 2004. Similarly, PET imaging with the 18 F-2-deoxyglucose (FDG) tracer, and MRSI with choline/citrate ratios can be used to assess the spatial distribution of tumor cell density (and hence to indirectly locate radioresistant tumor regions of high cell density) (Zhou et al 2004, Yang and Xing 2005b, Stewart and Li 2007, Dasu 2008. Research is currently ongoing to enhance the accuracy and precision of such functional imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

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A theoretical stochastic control framework for adapting radiotherapy to hypoxia

2016

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Hypoxia, that is, insufficient oxygen partial pressure, is a known cause of reduced radiosensitivity in solid tumors, and especially in head-and-neck tumors. It is thus believed to adversely affect the outcome of fractionated radiotherapy. Oxygen partial pressure varies spatially and temporally over the treatment course and exhibits inter-patient and intra-tumor variation. Emerging advances in non-invasive functional imaging offer the future possibility of adapting radiotherapy plans to this uncertain spatiotemporal evolution of hypoxia over the treatment course. We study the potential benefits of such adaptive planning via a theoretical stochastic control framework using computer-simulated evolution of hypoxia on computer-generated test cases in head-and-neck cancer. The exact solution of the resulting control problem is computationally intractable. We develop an approximation algorithm, called certainty equivalent control, that calls for the solution of a sequence of convex programs over the treatment course; dose-volume constraints are handled using a simple constraint generation method. These convex programs are solved using an interior point algorithm with a logarithmic barrier via Newton's method and backtracking line search. Convexity of various formulations in this paper is guaranteed by a sufficient condition on radiobiological tumor-response parameters. This condition is expected to hold for head-and-neck tumors and for other similarly responding tumors where the linear dose-response parameter is larger than the quadratic dose-response parameter. We perform numerical experiments on four test cases by using a first-order vector autoregressive process with exponential and rational-quadratic covariance functions from the spatiotemporal statistics literature to simulate the evolution of hypoxia. Our results suggest that dynamic planning could lead to a considerable improvement in the number of tumor cells remaining at the end of the treatment course. Through these simulations, we also gain insights into when and why dynamic planning is likely to yield the largest benefits.

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“…In comparison to hypoxia, little attention has been paid to developing models for the inclusion of proliferation information into treatment planning. To the best of our knowledge, there is only one study in which the possibility of including quantitative imaging of tumour proliferation and cell density into the radiobiological evaluation and optimisation of treatment planning was theoretically explored [ 29 ].…”

Section: Pet Imaging Of Key Tumour Phenotypes and Corresponding Momentioning

confidence: 99%

Towards Multidimensional Radiotherapy: Key Challenges for Treatment Individualisation

Toma-Daşu

Daşu

2015

Computational and Mathematical Methods in Medicine

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Functional and molecular imaging of tumours have offered the possibility of redefining the target in cancer therapy and individualising the treatment with a multidimensional approach that aims to target the adverse processes known to impact negatively upon treatment result. Following the first theoretical attempts to include imaging information into treatment planning, it became clear that the biological features of interest for targeting exhibit considerable heterogeneity with respect to magnitude, spatial, and temporal distribution, both within one patient and between patients, which require more advanced solutions for the way the treatment is planned and adapted. Combining multiparameter information from imaging with predictive information from biopsies and molecular analyses as well as in treatment monitoring of tumour responsiveness appears to be the key approach to maximise the individualisation of treatment. This review paper aims to discuss some of the key challenges for incorporating into treatment planning and optimisation the radiobiological features of the tumour derived from pretreatment PET imaging of tumour metabolism, proliferation, and hypoxia and combining them with intreatment monitoring of responsiveness and other predictive factors with the ultimate aim of individualising the treatment towards the maximisation of response.

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Treatment planning optimisation based on imaging tumour proliferation and cell density

Abstract: To cite this article: Alexandru Daşu (2008) Treatment planning optimisation based on imaging tumour proliferation and cell density, Acta Oncologica, 47:7, 1221Oncologica, 47:7, -1228

Cited by 8 publications

References 31 publications

Dose prescription and optimisation based on tumour hypoxia

Dose prescription and optimisation based on tumour hypoxia

A theoretical stochastic control framework for adapting radiotherapy to hypoxia

Towards Multidimensional Radiotherapy: Key Challenges for Treatment Individualisation

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