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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gastrointestinal Cancers: Gastric and Esophageal Cancers
Volume 14 - 2024 |
doi: 10.3389/fonc.2024.1480466
Synergizing Traditional CT Imaging with Radiomics: A Novel Model for Preoperative Diagnosis of Gastric Neuroendocrine and Mixed Adenoneuroendocrine Carcinoma
Provisionally accepted- 1 Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- 2 HanDan Central Hospital, Handan, Hebei Province, China
- 3 GE Healthcare, Shanghai, Shanghai Municipality, China
- 4 Zhengding Country People's Hospital, Shijiazhuang, Hebei Province, China
Objective: To develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features. Methods: We retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong's test. The models' diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases. Results: Tumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P < 0.05). The clinical model's AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC
Keywords: Gastric Carcinoma, neuroendocrine carcinoma, mixed adenoneuroendocrine carcinoma, traditional X-ray computed tomography, Radiomics
Received: 14 Aug 2024; Accepted: 07 Oct 2024.
Copyright: © 2024 He, Yang, Ren, Wang, Li, You, Li, Li, Shi and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Sujun Yang, HanDan Central Hospital, Handan, Hebei Province, China
Ning Wang, Zhengding Country People's Hospital, Shijiazhuang, Hebei Province, China
Yu Li, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
Li Yang, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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