Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy

Sai Kit Lam, Yuanpeng Zhang, Jiang Zhang, Bing Li, Jia Chen Sun, Carol Yee Tung Liu, Pak Hei Chou, Xinzhi Teng, Zong Rui Ma, Rui Yan Ni, Ta Zhou, Tao Peng, Hao Nan Xiao, Tian Li, Ge Ren, Andy Lai Yin Cheung, Francis Kar Ho Lee, Celia Wai Yi Yip, Kwok Hung Au, Victor Ho Fun LeeAmy Tien Yee Chang, Lawrence Wing Chi Chan, Jing Cai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Purpose: To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC). Methods and Materials: Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models. Results: The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models. Conclusions: Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.

Original languageEnglish
Article number792024
JournalFrontiers in Oncology
Volume11
DOIs
Publication statusPublished - 31 Jan 2022

Keywords

  • adaptive radiotherapy
  • dosiomics
  • multiomics approach
  • nasopharyngeal carcinoma
  • radiomics

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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