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

ABSTRACT:

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. Mod

SUBMITTER: Samant P 

PROVIDER: S-EPMC9984444 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature