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Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy.
Guo, Wei; Li, Bing; Xu, Wencai; Cheng, Chen; Qiu, Chengyu; Sam, Sai-Kit; Zhang, Jiang; Teng, Xinzhi; Meng, Lingguang; Zheng, Xiaoli; Wang, Yuan; Lou, Zhaoyang; Mao, Ronghu; Lei, Hongchang; Zhang, Yuanpeng; Zhou, Ta; Li, Aijia; Cai, Jing; Ge, Hong.
Afiliación
  • Guo W; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Li B; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Xu W; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Cheng C; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Qiu C; Department of Medical Informatics, Nantong University, Nantong, China.
  • Sam SK; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
  • Zhang J; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
  • Teng X; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
  • Meng L; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Zheng X; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Wang Y; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Lou Z; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Mao R; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Lei H; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
  • Zhang Y; Department of Medical Informatics, Nantong University, Nantong, China.
  • Zhou T; School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
  • Li A; Zhengzhou University School of Medicine, Zhengzhou, China.
  • Cai J; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
  • Ge H; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China. zlyygehong0199@zzu.edu.cn.
J Cancer Res Clin Oncol ; 150(2): 39, 2024 Jan 27.
Article en En | MEDLINE | ID: mdl-38280037
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Esofágicas / Fístula Esofágica / Radioterapia de Intensidad Modulada Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Cancer Res Clin Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Esofágicas / Fístula Esofágica / Radioterapia de Intensidad Modulada Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Cancer Res Clin Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania