Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option.

ABSTRACT:

Purpose

A reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients who received radiotherapy. 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. Furthermore, we aim to adopt a learning with rejection option (LRO) strategy to boost the reliability of Delta-mCOM model by rejecting prediction for samples with high prediction uncertainties.

Methods

In this retrospective study, we collected PET/CT image and clinical data from 224 HNC patients who received radiotherapy (RT) at our institution. We calculated the differences between radiomics features extracted from PET/CT images acq

SUBMITTER: Wang K 

PROVIDER: S-EPMC10121744 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature