On the use of published radiobiological parameters and the evaluation of NTCP models regarding lung pneumonitis in clinical breast radiotherapy

Parameters of the Lyman Model for Calculation of Normal-Tissue Complication Probability: A Systematic Literature Review

PURPOSE

The Lyman model is one of the most used radiobiological models for calculation of normal-tissue complication probability (NTCP). Since its introduction in 1985, many authors have published parameter values for the model based on clinical data of different radiotherapeutic situations. This study attempted to collect the entirety of radiobiological parameter sets published to date and provide an overview of the data basis for different variations of the model. Furthermore, it sought to compare the parameter values and calculated NTCPs for selected endpoints with sufficient data available.

METHODS AND MATERIALS

A systematic literature analysis was performed, searching for publications that provided parameters for the different variations of the Lyman model in the Medline database using PubMed. Parameter sets were grouped into 13 toxicity-related endpoint groups. For 3 selected endpoint groups (≤25% reduction of saliva 12 months after irradiation of the parotid, symptomatic pneumonitis after irradiation of the lung, and bleeding of grade 2 or less after irradiation of the rectum), parameter values were compared and differences in calculated NTCP values were analyzed.

RESULTS

A total of 509 parameter sets from 130 publications were identified. Considerable heterogeneities were detected regarding the number of parameters available for different radio-oncological situations. Furthermore, for the 3 selected endpoints, large differences in published parameter values were found. These translated into great variations of calculated NTCPs, with maximum ranges of 35.2% to 93.4% for the saliva endpoint, of 39.4% to 90.4% for the pneumonitis endpoint, and of 5.4% to 99.3% for the rectal bleeding endpoint.

CONCLUSIONS

The detected heterogeneity of the data as well as the large variations of published radiobiological parameters underline the necessity for careful interpretation when using such parameters for NTCP calculations. Appropriate selection of parameters and validation of values are essential when using the Lyman model.

Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation

Background and purpose

A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical instability and considers only the generalized mean dose (GMD) to an organ. Machine learning (ML) algorithms can potentially offer superior predictive power of the LKB model, and with fewer drawbacks. Here we examine the numerical characteristics and predictive power of the LKB model and compare these with those of ML.

Materials and methods

Both an LKB model and ML models were used to predict G2 Xerostomia on patients following RT for head and neck cancer, using the dose volume histogram of parotid glands as the input feature. Model speed, convergence characteristics and predictive power was evaluated on an independent training set.

Results

We found that only global optimization algorithms could guarantee a convergent and predictive LKB model. At the same time our results showed that ML models remained unconditionally convergent and predictive, while staying robust to gradient descent optimization. ML models outperform LKB in Brier score and accuracy but compare to LKB in ROC-AUC.

Conclusion

We have demonstrated that ML models can quantify NTCP better than or as well as LKB models, even for a toxicity that the LKB model is particularly well suited to predict. ML models can offer this performance while offering fundamental advantages in model convergence, speed, and flexibility, and so could offer an alternative to the LKB model that could potentially be used in clinical RT planning decisions.

Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning

Objective:

An automatic method for the optimization of importance factors was proposed to improve the efficiency of inverse planning.

Methods:

The automatic method consists of 3 steps: (1) First, the importance factors are automatically and iteratively adjusted based on our proposed penalty strategies. (2) Then, plan evaluation is performed to determine whether the obtained plan is acceptable. (3) If not, a higher penalty is assigned to the unsatisfied objective by multiplying it by a compensation coefficient. The optimization processes are performed alternately until an acceptable plan is obtained or the maximum iteration N_max of step (3) is reached.

Results:

Tested on 2 kinds of clinical cases and compared with manual method, the results showed that the quality of the proposed automatic plan was comparable to, or even better than, the manual plan in terms of the dose–volume histogram and dose distributions.

Conclusions:

The proposed algorithm has potential to significantly improve the efficiency of the existing manual adjustment methods for importance factors and contributes to the development of fully automated planning. Especially, the more the subobjective functions, the more obvious the advantage of our algorithm.

Developing of predictive models for pneumonitis with forward variable selection and LASSO logistic model for breast cancer patients treated with 3D-CRT

Purpose: To develop a multiple logistic regression model as normal tissue complication probability model by least absolute shrinkage and selection operator (LASSO) technique in breast cancer patients treated with three-dimensional conformal radiation therapy (3D-CRT), we focused on the changes of pulmonary function tests to achieve the optimal predictive parameters for the occurrence of symptomatic radiation pneumonitis (SRP).

Materials and methods: Dosimetric and spirometry data of 60 breast cancer patients were analyzed. Pulmonary function tests were done before RT, after completion of RT, 3, and 6 months after RT. Multiple logistic regression model was used to obtain the effective predictive parameters. Forward selection method was applied in NTCP model to determine the effective risk factors from obtained different parameters.

Results: Symptomatic radiation pneumonitis was observed in five patients. Significant changes in pulmonary parameters have been observed at six months after RT. The parameters of mean lung dose (MLD), bridge separation (BS), mean irradiated lung volume (ILVmean), and the percentage of the ipsilateral lung volume that received dose of 20 Gy (IV20) introduced as risk factors using the LASSO technique for SRP in a multiple normal tissue complication probability model in breast cancer patients treated with 3D-CRT. The BS, central lung distance (CLD) and ILV in tangential field have obtained as 23.5 (20.9-26.0) cm, 2.4 (1.5-3.3) cm, and 12.4 (10.6-14.3) % of lung volume in radiation field in patients without pulmonary complication, respectively.

Conclusion: The results showed that if BS, CLD, and ILV are more than 23 cm, 2 cm, and 12%, respectively, so incidence of SRP in the patients will be considerable. Our multiple NTCP LASSO model for breast cancer patients treated with 3D-CRT showed that in order to have minimum probability of SRP occurrence, parameters of BS, IV20, ILV and especially MLD would be kept in minimum levels. Considering dose-volume histogram, the mean lung dose factor is most important parameter which minimizing it in treatment planning, minimizes the probability of SRP and consequently improves the quality of life in breast cancer patients.

A method to visualize the uncertainty of the prediction of radiobiological models

A method for quantitative visualization of the uncertainty in the predicted tumor control probability (TCP) and normal tissue complication probability (NTCP) in radiotherapy has been developed. Uncertainties of TCP and NTCP due to inter-individual variation of the underlying radiosensitivity parameters was simulated by sampling the prescribed dose from a uniform distribution and the radiosensitivity-parameters from a Gaussian distribution. The result is visualized as a scatter-plot superimposed to the population-based dose response curves using the prescribed dose as the common dosimetric variable. In addition, probability histograms are derived quantifying the probability of specific TCP- or NTCP-values for individual patients from the underlying population. The method is exemplified with a pleural mesothelioma case with the lung as organ at risk. A prescribed dose of 54 Gy together with radiosensitivity variations of 6% (tumor) and 10% (normal tissue) results in a TCP of 85% (range 68-94%, 90% confidence interval, CI) and an NTCP of 4% (range 3-6%, 90% CI), respectively. Increasing the radiosensitivity variation of the tumor to 15% and reducing the lung tolerance dose by 25% results in values of 84% (range 51-97%, 90% CI) for TCP and 9% (range 6-12%, 90% CI) for NTCP. Increasing the dose to 60 Gy leads to TCP- and NTCP-values of 93% (range 69-100%, 90% CI) and 12% (range 8-17%, 90% CI), respectively. The new method visualizes the uncertainty of TCP- and NTCP-values and hence of the therapeutic window. This can help the clinician to assess the treatment plan for the individual patient.

Combining the LKB NTCP model with radiosensitivity parameters to characterize toxicity of radionuclides based on a multiclonogen kidney model: a theoretical assessment

The Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) model is often used to estimate the damage level to normal tissue. However, it does not manifestly involve the influence of radiosensitivity parameters. This work replaces the generalized mean equivalent uniform dose (gEUD) with the equivalent uniform dose (EUD) in the LKB model to investigate the effect of a variety of radiobiological parameters on the NTCP to characterize the toxicity of five types of radionuclides. The dose for 50 % complication probability (D (50)) is replaced by the corresponding EUD for 50 % complication probability (EUD(50)). The properties of a variety of radiobiological characteristics, such as biologically effective dose (BED), NTCP, and EUD, for five types of radioisotope ((131)I, (186)Re, (188)Re, (90)Y, and (67)Cu) are investigated by various radiosensitivity parameters such as intrinsic radiosensitivity α, alpha-beta ratio α/β, cell repair half-time, cell mean clonogen doubling time, etc. The high-energy beta emitters ((90)Y and (188)Re) have high initial dose rate and mean absorbed dose per injected activity in kidney, and their kidney toxicity should be of greater concern if they are excreted through kidneys. The radiobiological effect of (188)Re changes most sharply with the radiobiological parameters due to its high-energy electrons and very short physical half-life. The dose for a probability of 50% injury within 5y (D (50/5)) 28 Gy for whole-kidney irradiation should be adjusted according to different radionuclides and different radiosensitivity of individuals. The D (50/5) of individuals with low α/β or low α, or low biological clearance half-time, will be less than 28 Gy. The 50 % complication probability dose for (67)Cu and (188)Re could be 25 Gy and 22 Gy. The same mean absorbed dose generally corresponds to different degrees of damage for tissues of different radiosensitivity and different radionuclides. The influence of various radiobiological parameters should be taken into consideration in the NTCP model.