  A reliable locoregional recurrence (LRR) prediction model is important for
the personalized management of head and neck cancers (HNC) patients. This work
aims to develop a delta-radiomics feature-based multi-classifier,
multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC
LRR prediction and adopting a learning with rejection option (LRO) strategy to
boost the prediction reliability by rejecting samples with high prediction
uncertainties. In this retrospective study, we collected PET/CT image and
clinical data from 224 HNC patients. We calculated the differences between
radiomics features extracted from PET/CT images acquired before and after
radiotherapy as the input features. Using clinical parameters, PET and CT
radiomics features, we built and optimized three separate single-modality
models. We used multiple classifiers for model construction and employed
sensitivity and specificity simultaneously as the training objectives. For
testing samples, we fused the output probabilities from all these
single-modality models to obtain the final output probabilities of the
Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric
uncertainties when predicting with Delta-mCOM model and identified patients
associated with prediction of higher reliability. Predictions with higher
epistemic uncertainty or higher aleatoric uncertainty than given thresholds
were deemed unreliable, and they were rejected before providing a final
prediction. Different thresholds corresponding to different low-reliability
prediction rejection ratios were applied. The inclusion of the delta-radiomics
feature improved the accuracy of HNC LRR prediction, and the proposed
Delta-mCOM model can give more reliable predictions by rejecting predictions
for samples of high uncertainty using the LRO strategy.
