A TCP model for external beam treatment of intermediate‐risk prostate cancer

Walsh, Seán; Putten, Wil van der

doi:10.1118/1.4790469

Cited by 21 publications

(20 citation statements)

References 99 publications

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“…An average volume for the CTV (prostate) of 36 cm 3 was used for low and intermediate-risk patients 22 while an average volume for the CTV (prostate and seminal vesicles) of 72 cm 3 was used for high-risk patients, 39 both of these values also agree with local clinical measurements. Because the TCP model presented in Eq.…”

Section: B the Tumor Control Probability Modelmentioning

confidence: 96%

“…The TCP model uses the linear-quadratic (LQ) model of radiation effect coupled with Poisson statistics, 22 a modified version of that originally proposed by Munro and Gilbert. 30 The LQ model predicts the surviving fraction S(D) of a population of cells exposed to a total uniform dose D, where D is comprised of n fractions of uniform dose d. The linear and quadratic coefficients of the LQ model are α and β, respectively, describing inherent radiosensitivity.…”

Section: B the Tumor Control Probability Modelmentioning

confidence: 99%

“…Values obtained from a previous fit of this TCP model to X-EBRT treatments of intermediate-risk patients 22 were utilized here as fixed values: α = 0.25 Gy −1 , α/ β = 2.48 Gy, σ α = 11.3%, σ β = 12.9%, DIL volume = 10%, hypoxic fraction = 15%, and OER = 1.75. This approach 47 enables fitting of the DIL volume and the hypoxic fraction for low and high-risk prostate cancer patients as well as the RBE min and RBE max of both P-EBRT and C-EBRT along with the modification of the OER in the case of C-EBRT due to LET.…”

Section: D Fitting the Tumor Control Probability Modelmentioning

confidence: 99%

“…The TCP model is analytically intractable and consequently standard fitting techniques are inappropriate, and the model was therefore fitted using the Nelder-Mead (NM) simplex algorithm 48 (the same algorithm used in the previous fit of this TCP model to X-EBRT treatments of intermediate-risk patients 22 ). The NM simplex algorithm is a robust method for determining a local minimum of a function with several variables.…”

Section: D Fitting the Tumor Control Probability Modelmentioning

confidence: 99%

“…21 However, to our knowledge, no TCP model has been fit to prostate cancer outcome data treated by either P-EBRT or C-EBRT. This work presents a TCP model 22 prostate cancer patients and shows the capability of the model to accurately predict TCP using biologically realistic input parameters for radiosensitivity and clonogen density, as well as clonogen density distribution and tumor hypoxia status obtained from the fit presented below.…”

Section: Introductionmentioning

confidence: 99%

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A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

et al. 2016

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Section: B the Tumor Control Probability Modelmentioning

confidence: 96%

Section: B the Tumor Control Probability Modelmentioning

confidence: 99%

Section: D Fitting the Tumor Control Probability Modelmentioning

confidence: 99%

Section: D Fitting the Tumor Control Probability Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

et al. 2016

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Patient‐specific voxel‐level dose prescription for prostate cancer radiotherapy considering tumor cell density and grade distribution

et al. 2023

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Background: In prostate radiation therapy, recent studies have indicated a benefit in increasing the dose to intraprostatic lesions (IPL) compared with standard whole gland radiation therapy. Such approaches typically aim to deliver a target dose to the IPL(s) with no deliberate effort to modulate the dose within the IPL. Prostate cancers demonstrate intra-tumor heterogeneity and hence it is hypothesized that further gains in the optimal delivery of radiation therapy can be achieved through modulation of the dose distribution within the tumor. To account for tumor heterogeneity, biologically targeted radiation therapy (BiRT) aims to utilize a voxel-wise approach to IPL dose prescription by incorporating knowledge of the spatial distribution of tumor characteristics. Purpose:The aim of this study was to develop a workflow for generating voxelwise optimal dose prescriptions that maximize patient tumor control probability (TCP), and evaluate the feasibility and benefits of applying this workflow on a cohort of 62 prostate cancer patients. Method: The source data for this proof -of -concept study included high resolution histology images annotated with tumor location and grade. Image processing techniques were used to compute voxel-level cell density distribution maps. An absolute tumor cell distribution was calculated via linearly scaling according to published estimated tumor cell numbers. For the IPLs of each patient, optimal dose prescriptions were obtained via three alternative methods for redistribution of IPL boost doses according to maximization of TCP. The radiosensitivity uncertainties were considered using a truncated log-normally distributed linear radiosensitivity parameter (𝛼 k ) and compared with Gleason pattern (GP) dependent radiosensitivity parameters that were derived based on previously published methods. An ensemble machine learning method was implemented to identify patient-specific features that predict the TCP improvement resulting from dose redistribution relative to a uniform dose distribution. Results:The Gleason pattern-dependent radiosensitivity parameters were calculated for 20 published prostate cancer 𝛼∕𝛽 ratios. Optimal voxel-level dose prescriptions were generated for all 62 PCa patients. For all dose redistribution scenarios, the optimal dose distribution always shows a higher (or equivalent) TCP level than the uniform dose distribution. The applied random forest regressor could predict patient-specific TCP improvement with low root mean square error (≤1.5%) by using total tumor number, volume of IPLs and the standard deviation of tumor cell number among all voxels.

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Decision support systems for personalized and participative radiation oncology

Lambin

Zindler

Voorde

et al. 2017

Advanced Drug Delivery Reviews

120

103

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A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

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A TCP model for external beam treatment of intermediate‐risk prostate cancer

Abstract: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.

Cited by 21 publications

References 99 publications

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

Patient‐specific voxel‐level dose prescription for prostate cancer radiotherapy considering tumor cell density and grade distribution

Decision support systems for personalized and participative radiation oncology

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