Abstract
Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status.Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was utilized to select features and build radiomics signature. Multivariable logistic regression analysis was utilized to develop the model. We integrated the radiomics signature, clinical T and N stage, and other independent clinicopathologic variables, and this was presented as a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor of LN metastasis. The nomogram showed good discrimination and calibration.Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status.
Highlights
Gastric cancer (GC) is one of the most common malignant tumors and the second leading cause of cancerrelated deaths worldwide [1]
The radiomics signature was significantly associated with pathological lymph node (LN) stage in training and validation cohorts
Multivariable logistic analysis found the radiomics signature was an independent predictor of LN metastasis
Summary
Gastric cancer (GC) is one of the most common malignant tumors and the second leading cause of cancerrelated deaths worldwide [1]. Accurate evaluation of lymph node metastasis (LNM) status in GC patients is vital for prognosis and treatment decisions [2,3,4]. Surgeons think of endoscopic resection as the best choice for early GC without LNM, on account of more postoperative complication and mortality of D2 gastrectomy [4]. Accurate preoperative predictions of LNM status are vital for GC patients, especially at the early stage. Recent studies showed that several serum markers (e.g., serum human apurinic/apyrimidinic endonuclease 1, circulating microRNAs) could preoperatively predict LNM in GC, but these biomarkers still need further validation and are not a part of standard clinical practice [8]. To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status
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