Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy

期刊论文
出版模式：

开放获取
发表日期：

2024年1月27日

查看全文

Wei Guo,
Bing Li,
Wencai Xu,
Chen Cheng,
Chengyu Qiu,
Sai-kit Sam,
Jiang Zhang,
Xinzhi Teng,
Lingguang Meng,
Xiaoli Zheng,
Yuan Wang,
Zhaoyang Lou,
Ronghu Mao,
Hongchang Lei,
Yuanpeng Zhang,
Ta Zhou,
Aijia Li,
Jing Cai,
Hong Ge

摘要

Objective

This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs).

Methods

We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score.

Results

For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV).

Conclusion

Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.