Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer.
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Abstract
In the clinical management of advanced gastric cancer (AGC), preoperative identification of early recurrence after curative resection is essential. Thus, we aimed to create a CT-based radiomic model to predict early recurrence in AGC patients preoperatively.
We enrolled 669 consecutive patients (302 in the training set, 219 in the internal test set and 148 in the external test set) with clinicopathologically confirmed AGC from two centers. Radiomic features were extracted from preoperative diagnostic CT images. Machine learning methods were applied to shrink feature size and build a predictive radiomic signature. We incorporated the radiomic signature and clinical risk factors into a nomogram using multivariable logistic regression analysis. The area under the curve (AUC) of operating characteristics (ROC), accuracy, and calibration curves were assessed to evaluate the nomogram’s performance in discriminating early recurrence.
A radiomic signature, including three hand crafted features and six deep learning features, was significantly associated with early recurrence (p-value <0.0001 for all sets). In addition, clinical N stage, carbohydrate antigen 199 levels, carcinoembryonic antigen levels, and Borrmann type were considered useful predictors for early recurrence. The nomogram, combining all these predictors, showed powerful prognostic ability in the training set and two test sets with AUCs of 0.831 (95% CI, 0.786-0.876), 0.826 (0.772-0.880) and 0.806 (0.732-0.881), respectively. The predicted risk yielded good agreement with the observed recurrence probability.
By incorporating a radiomic signature and clinical risk factors, we created a radiomic nomogram to predict early recurrence in patients with AGC, preoperatively, which may serve as a potential tool to guide personalized treatment.
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