Yexing Li1,2, Zixuan Cheng1,3, Olivier Gevaert4, Lan He1, Yanqi Huang1, Xin Chen1,5,6, Xiaomei Huang1,5, Xiaomei Wu1,3, Wen Zhang1,5,7, Mengyi Dong1,5, Jia Huang1,2, Yucun Huang1,5,8, Ting Xia1,3, Changhong Liang1,2, Zaiyi Liu1,2. 1. Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. 2. Graduate College, Shantou University Medical College, Shantou 515041, China. 3. School of Medicine, South China University of Technology, Guangzhou 510006, China. 4. Stanford Center for Biomedical Informatics Research, Department of Medicine, and Department of Biomedical Data Science, Stanford University, California 94305, USA. 5. Graduate College, Southern Medical University, Guangzhou 510515, China. 6. Department of Radiology, Guangzhou First People's Hospital, Guangzhou 510180, China. 7. Department of Radiology, Zhuhai Hospital of Traditional Chinese and Western Medicine, Zhuhai 519000, China. 8. Department of Radiology, The Fifth People's Hospital of Zhuhai, Zhuhai 519055, China.
Abstract
OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.
OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.
Authors: Olivier Gevaert; Jiajing Xu; Chuong D Hoang; Ann N Leung; Yue Xu; Andrew Quon; Daniel L Rubin; Sandy Napel; Sylvia K Plevritis Journal: Radiology Date: 2012-06-21 Impact factor: 11.105
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Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702