AUTHOR=Huang Liebin , Feng Bao , Li Yueyue , Liu Yu , Chen Yehang , Chen Qinxian , Li Changlin , Huang Wensi , Xue Huimin , Li Xuehua , Zhou Tao , Li Ronggang , Long Wansheng , Feng Shi-Ting 

TITLE=Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.638362

DOI=10.3389/fonc.2021.638362

ISSN=2234-943X

ABSTRACT=Objective: Accurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence(LR) of GC after radical resection.
Materials and Methods: 342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the accuracy of different models were assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). 
Results: The radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.833 in the training cohort, 0.840 in the internal validation cohort, and 0.732 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage and bile acid duodenogastric reflux) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.881, 0.889 and 0.792 in the three cohorts, respectively. DCA confirmed the clinical utility of the radiomics nomogram.
Conclusion: The CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.