Dataset Information

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.

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.

SUBMITTER: Li Y

PROVIDER: S-EPMC7072015 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.

Li Yexing Y Cheng Zixuan Z Gevaert Olivier O He Lan L Huang Yanqi Y Chen Xin X Huang Xiaomei X Wu Xiaomei X Zhang Wen W Dong Mengyi M Huang Jia J Huang Yucun Y Xia Ting T Liang Changhong C Liu Zaiyi Z

Chinese journal of cancer research = Chung-kuo yen cheng yen chiu 20200201 1

<h4>Objective</h4>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.<h4>Methods</h4>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 gast ...[more]

PMID: 32194306

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Project description:PurposeWe developed and validated a CT-based radiomics nomogram to predict HER2 status in patients with adenocarcinoma of esophagogastric junction (AEG).MethodA total of 101 patients with HER2-positive (n=46) and HER2-negative (n=55) esophagogastric junction adenocarcinoma (AEG) were retrospectively analyzed. They were then randomly divided into a training cohort (n=70) and a verification cohort (n=31). The radiomics features were obtained from the portal phase of the CT enhanced scan. We used the least absolute shrinkage and selection operator (LASSO) logistic regression method to select the best radiomics features in the training cohort, combined them linearly, and used the radiomics signature formula to calculate the radiomics score (Rad-score) of each AEG patient. A multivariable logistic regression method was applied to develop a prediction model that incorporated the radiomics signature and independent risk predictors. The prediction performance of the nomogram was evaluated using the training and validation cohorts.ResultIn the training (P<0.001) and verification groups (P<0.001), the radiomics signature combined with seven radiomics features was significantly correlated with HER2 status. The nomogram composed of CT-reported T stage and radiomics signature showed very good predictive performance for HER2 status. The area under the curve (AUC) of the training cohort was 0.946 (95% CI: 0.919-0.973), and that of the validation group was 0.903 (95% CI: 0.847-0.959). The calibration curve of the radiomics nomogram showed a good degree of calibration. Decision-curve analysis revealed that the radiomics nomogram was useful.ConclusionThe nomogram CT-based radiomics signature combined with CT-reported T stage can better predict the HER2 status of AEG before surgery. It can be used as a non-invasive prediction tool for HER2 status and is expected to guide clinical treatment decisions in clinical practice, and it can assist in the formulation of individualized treatment plans.

Project description:Purpose To retrospectively identify the relationship between epidermal growth factor receptor (EGFR) mutation status, predominant histologic subtype, and computed tomographic (CT) characteristics in surgically resected lung adenocarcinomas in a cohort of Asian patients. materials and Methods This study was approved by the institutional review board, with waiver of informed consent. Preoperative chest CT findings were retrospectively evaluated in 385 surgically resected lung adenocarcinomas. A total of 30 CT descriptors were assessed. EGFR mutations at exons 18-21 were determined by using the amplification refractory mutation system. Multiple logistic regression analyses were performed to identify independent factors of harboring EGFR mutation status. The final model was selected by using the backward elimination method, and two areas under the receiver operating characteristic curve (ROC) were compared with the nonparametric approach of DeLong, DeLong, and Clarke-Pearson. Results EGFR mutations were found in 168 (43.6%) of 385 patients. Mutations were found more frequently in (a) female patients (P < .001); (b)those who had never smoked (P < .001); (c)those with lepidic predominant adenocarcinomas (P = .001) or intermediate pathologic grade (P < .001); (e) smaller tumors (P < .001); (f)tumors with spiculation (P = .019), ground-glass opacity (GGO) or mixed GGO (P < .001), air bronchogram (P = .006), bubblelike lucency (P < .001), vascular convergence (P = .024), thickened adjacent bronchovascular bundles (P = .027), or pleural retraction (P < .001); and (g) tumors without pleural attachment (P = .004), a well-defined margin (P = .010), marked heterogeneous enhancement (P = .001), severe peripheral emphysema (P = .002), severe peripheral fibrosis (P = .013), or lymphadenopathy (P = .028). The most important and significantly independent prognostic factors of harboring EGFR-activating mutation for the model with both clinical variables and CT features were those who had never smoked and those with smaller tumors, bubblelike lucency, homogeneous enhancement, or pleural retraction when adjusting for histologic subtype, pathologic grade, or thickened adjacent bronchovascular bundles. ROC curve analysis showed that use of clinical variables combined with CT features (area under the ROC curve = 0.778) was superior to use of clinical variables alone (area under the ROC curve = 0.690). Conclusion CT imaging features of lung adenocarcinomas in combination with clinical variables can be used to prognosticate EGFR mutation status better than use of clinical variables alone. (©) RSNA, 2016 Online supplemental material is available for this article.

Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

Dataset Information

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.

Publications

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.

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