Session Item

A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. This model consists of three parameters, n, m, and D₅₀, such that

Where v_i and D_i are the relative volume fractions and corresponding dose bins of the differential dose volume histogram (DVH) of an organ. Despite its common use, the LKB model has some limitations preventing clinical deployment: 1) it can only consider dose to a single contoured structure 2) it can be numerically unstable during fitting 3) it is difficult to correct for batch effects.

In this study we examine the ability of the LKB model to predict Grade 2 Xerostomia in head and neck cancer patients. We also compare LKB performance to conventional machine learning (ML) algorithms such as logistic regression (LG), AdaBoost (AB), Decision Trees (DT), and Gradient Boost (GB).

We acquired parotid gland DVHs and demographic data (gender, age) from the Outcome H&N trial to act as the training set. Similarly, the same data of the PARSPORT trial to act as a test set.

An LKB model to predict G2 Xerostomia was fit on the training DVHs (constructed by inferring the DVH on extracted metrics), by varying parameters to maximize log-likelihood after an initial guess. The model was then evaluated on the test set and the area under the receiver operating curve characteristic curve (ROC-auc) was used as a metric. Several initial parameter guesses were tried in accordance with results from Burman et al. to test for convergence. The model fit was tried using both bounded and unbounded parameters. Similarly, AB, LG, DT, and GB models were also fit on the training set and their hyperparameters tuned, using patient dose metrics as features for fitting.

Initial guesses for n, m, and D₅₀ were loosely chosen based on values provided by Burman et al. for various organs. The results are summarized in Table 1.

The predictive performance of the models is summarized below in Figure 1. The converging LKB model using initial guesses of 0.7,0.18,46 (corresponding to parotid values)  was compared with the performance of the ML models. As can be seen, ML performs comparably to the LKB model even when the latter converges.

Our results show that ML algorithms outperform the LKB model in most cases, as they always converge and have good predictive capability. This is even though G2 xerostomia, largely dependent on the dose to the parotid gland (a parallel organ), is quite a well-suited situation for LKB modelling. ML models are simple to deploy with modern toolboxes and they have the additional benefit of being able to consider any features of interest that can contribute to patient toxicity. Further studies where these models are compared with LKB performance are needed, particularly in cases where the structure of interest has both serial and parallel components (e.g. the heart).