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DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer

OBJECTIVES: To assess volumetric DCE-MRI radiomics nomogram in predicting response to neoadjuvant chemotherapy (nCT) in EC patients. METHODS: This retrospective analysis of a prospective study enrolled EC patients with stage cT1N + M0 or cT2-4aN0-3M0 who received DCE-MRI within 7 days before chemoth...

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Detalles Bibliográficos
Autores principales: Qu, Jinrong, Ma, Ling, Lu, Yanan, Wang, Zhaoqi, Guo, Jia, Zhang, Hongkai, Yan, Xu, Liu, Hui, Kamel, Ihab R., Qin, Jianjun, Li, Hailiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777517/
https://www.ncbi.nlm.nih.gov/pubmed/35201487
http://dx.doi.org/10.1007/s12672-022-00464-7
Descripción
Sumario:OBJECTIVES: To assess volumetric DCE-MRI radiomics nomogram in predicting response to neoadjuvant chemotherapy (nCT) in EC patients. METHODS: This retrospective analysis of a prospective study enrolled EC patients with stage cT1N + M0 or cT2-4aN0-3M0 who received DCE-MRI within 7 days before chemotherapy, followed by surgery. Response assessment was graded from 1 to 5 according to the tumor regression grade (TRG). Patients were stratified into responders (TRG1 + 2) and non-responders (TRG3 + 4 + 5). 72 radiomics features and vascular permeability parameters were extracted from DCE-MRI. The discriminating performance was assessed with ROC. Decision curve analysis (DCA) was used for comparing three different models. RESULTS: This cohort included 82 patients, and 72 tumor radiomics features and vascular permeability parameters acquired from DCE-MRI. mRMR and LASSO were performed to choose the optimized subset of radiomics features, and 3 features were selected to create the radiomics signature that were significantly associated with response (P < 0.001). AUC of combining radiomics signature and DCE-MRI performance in the training (n = 41) and validation (n = 41) cohort was 0.84 (95% CI 0.57–1) and 0.86 (95% CI 0.74–0.97), respectively. This combined model showed the best discrimination between responders and non-responders, and showed the highest positive and positive predictive value in both training set and test set. CONCLUSIONS: The radiomics features are useful for nCT response prediction in EC patients.