3D deep learning Normal Tissue Complication Probability model to predict late xerostomia in head and neck cancer patients.

Researchers

Journal

Modalities

Models

Abstract

Conventional normal tissue complication probability (NTCP) models for head and neck cancer (HNC) patients are typically based on single-value variables, which for radiation-induced xerostomia are baseline xerostomia and mean salivary gland doses. This study aims to improve the prediction of late xerostomia by utilizing 3D information from radiation dose distributions, CT imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL).An international cohort of 1208 HNC patients from two institutes was used to train and twice validate DL models (DCNN, EfficientNet-v2, and ResNet) with 3D dose distribution, CT scan, organ-at-risk segmentations, baseline xerostomia score, sex, and age as input. The NTCP endpoint was moderate-to-severe xerostomia 12 months post-radiotherapy. The DL models’ prediction performance was compared to a reference model: a recently published xerostomia NTCP model that used baseline xerostomia score and mean salivary gland doses as input. Attention maps were created to visualize the focus regions of the DL predictions. Transfer learning was conducted to improve the DL model performance on the external validation set.All DL-based NTCP models showed better performance (AUCtest=0.78 – 0.79) than the reference NTCP model (AUCtest=0.74) in the independent test. Attention maps showed that the DL model focused on the major salivary glands, particularly the stem cell-rich region of the parotid glands. DL models obtained lower external validation performance (AUCexternal=0.63) than the reference model (AUCexternal=0.66). After transfer learning on a small external subset, the DL model (AUCtl, external=0.66) performed better than the reference model (AUCtl, external=0.64).DL-based NTCP models performed better than the reference model when validated in data from the same institute. Improved performance in the external dataset was achieved with transfer learning, demonstrating the need for multicenter training data to realize generalizable DL-based NTCP models.Copyright © 2024. Published by Elsevier Inc.