In:
Journal of Cancer Research and Clinical Oncology, Springer Science and Business Media LLC, Vol. 150, No. 2 ( 2024-01-27)
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1432-1335
DOI:
10.1007/s00432-023-05520-5
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2024
detail.hit.zdb_id:
1459285-X
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