Mechanistic modelling of radiotherapy-induced lung toxicity

Abstract

Objective: This work explores the biological basis of a mechanistic model of radiationinduced lung damage; uniquely, the model makes a connection between the cellular radiobiology involved in lung irradiation and the full three-dimensional distribution of radiation dose. Methods: Local tissue damage and loss of global organ function, in terms of radiation pneumonitis (RP), were modelled as different levels of radiation injury. Parameters relating to the former could be derived from the local dose-response function, and the latter from the volume effect of the organ. The literature was consulted to derive information on a threshold dose and volume-effect mechanisms. Results: Simulations of local tissue damage supported the alveolus as a functional subunit (FSU) which can be regenerated from a single surviving stem cell. A moderate interpatient variation in stem cell radiosensitivity (15%) resulted in a great variation in tissue response between 8 and 20 Gy. The threshold of FSU inactivation within a critical functioning volume leading to RP was found to be approximately 47% and the degree of health status variation (influencing the volume effect) in a population was estimated at 25%. Conclusion: This work has shown that it is possible to make sense of the way the lung responds to radiation by modelling RP mechanistically, from cell death to tissue damage to loss of organ function. Advances in knowledge: Simulations were able to provide parameter values, currently not available in the literature, related to the response of the lung to irradiation. Normal-tissue complication probability (NTCP) modelling is the key to exploiting new planning and treatment technology in radiotherapy to optimal effect. The models help to select the best treatment plan, in terms of total dose, fraction size and beam configuration, for the individual patient's anatomy. Although the potential of current NTCP models for feeding clinical experience into treatment planning is becoming increasingly evident [1][2][3][4][5], they are largely empirically based, providing little or no insight into the underlying processes involved. Moreover, they ignore the spatial distribution of the dose deposited in the organ at risk. By contrast, this work explores the biological basis for a mechanistic model which makes a connection with the real cellular radiobiology involved in lung irradiation and takes into account the full three-dimensional (3D) dose distribution.With conventional fractionation and prescribed radiation dose, lung tumour local control rates are of the order of 40% [6]; potential lung and oesophageal toxicity is generally put forward as the reason for such inadequate dosage. Therefore, it is imperative to understand how the lung responds to radiation, in order not to limit the dose to the tumour unnecessarily. Irradiation of the lungs can lead to radiation pneumonitis (RP), which usually develops around 4-6 weeks after treatment [7]. Patients experience fever, dyspnoea and cough and are treated with oral steroids. Mild or moderate sympto...

Extracting the normal lung dose–response curve from clinical DVH data: a possible role for low dose hyper-radiosensitivity, increased radioresistance

“…This work adopts a local damage/injury model similar to Jackson et al (1995) (see also Niemierko and Goitein (1993) and Rutkowska et al (2012)). It is assumed that lung is a parallel organ, and that radiation-induced loss of function can be modeled using a damage probability profile P ( D ), which gives the probability of sub-volume damage as a function of local dose D. ( P ( D ) is assumed to be independent of the subvolume location within the lungs.)…”

The performance of normal‐tissue complication probability models in the presence of confounding factors

“…As motivated in Ref. 25, the variation in health status was modeled by a standard deviation in the number of FSUs before irradiation (N FSU 0 ). This mechanistic model was used to generate pseudoclinical datasets with different characteristics, analogous to different patient populations given the same radiotherapy treatment.…”

“…This mechanistic model was used to generate pseudoclinical datasets with different characteristics, analogous to different patient populations given the same radiotherapy treatment. Parameter values corresponding to both pseudolung and pseudorectum 22,25 were used; see summary in Table I. By using these two parameter sets in the simulations, the mechanistic model produced data with different dose-volume response, inspired by the lung and the rectum.…”

“…Also the range of radiosensitivity measured in animals, and the relative contribution of a stochastic component and a deterministic component to the development of telangiectasia were considered. 25,26 The standard deviation for N FSU 0 , on the other hand, was estimated by adjusting its value until after repeated simulations a distribution of p-values for a correlation between a dose-volume parameter and outcome were similar to p-values published in clinical studies. 25,26 The lung dose distributions were generated as described in our previous work.…”

“…25,26 The standard deviation for N FSU 0 , on the other hand, was estimated by adjusting its value until after repeated simulations a distribution of p-values for a correlation between a dose-volume parameter and outcome were similar to p-values published in clinical studies. 25,26 The lung dose distributions were generated as described in our previous work. 22 In the case of the rectum, beams were T I. Parameters used in simulations: the LQ-model α/β-ratio, population mean α(ᾱ), population standard deviation (σ α ), the number of stem cells per FSU, the CFV, the FSU inactivation threshold within the CFV (FSU min ), the mean number of FSUs per voxel (N FSU 0 ), and the population standard deviation in the number of FSUs per voxel (σ FSU ).…”