Comparing supervised and semi-supervised machine learning approaches in NTCP modeling to predict complications in head and neck cancer patients.

Background and purpose

Head and neck cancer (HNC) patients treated with radiotherapy often suffer from radiation-induced toxicities. Normal Tissue Complication Probability (NTCP) modeling can be used to determine the probability to develop these toxicities based on patient, tumor, treatment and dose characteristics. Since the currently used NTCP models are developed using supervised methods that discard unlabeled patient data, we assessed whether the addition of unlabeled patient data by using semi-supervised modeling would gain predictive performance.

Materials and methods

The semi-supervised method of self-training was compared to supervised regression methods with and without prior multiple imputation by chained equation (MICE). The models were developed for the most common toxicity outcomes in HNC patients, xerostomia (dry mouth) and dysphagia (difficulty swallowing), measured at six months after treatment, in a development cohort of 750 HNC patients. The models were externally validated in a validation cohort of 395 HNC patients. Model performance was assessed by discrimination and calibration.

Results

MICE and self-training did not improve performance in terms of discrimination or calibration at external validation compared to current regression models. In addition, the relative performance of the different models did not change upon a decrease in the amount of (labeled) data available for model development. Models using ridge regression outperformed the logistic models for the dysphagia outcome.

Conclusion

Since there was no apparent gain in the addition of unlabeled patient data by using the semi-supervised method of self-training or MICE, the supervised regression models would still be preferred in current NTCP modeling for HNC patients.

Informations de copyright

© 2023 The Authors.

Déclaration de conflit d'intérêts

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.A. Langendijk reports a relationship with Dutch Cancer Society: funding grants. J.A. Langendijk reports his department has research contracts with IBA, RaySearch, Siemens, Elekta, Leoni, and Mirada. J.A. Langendijk reports a relationship with Global Scientific Advisory Board of IBA, RayCare International Advisory Board of RaySearch that includes: board membership, consulting or advisory, and speaking and lecture fees. J.A. Langendijk reports a relationship with Netherlands Society for Radiation Oncology that includes: board membership. Funding AL is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 825162. This dissemination reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains.