Nomograms Involving HER2 for Predicting Lymph Node Metastasis in Early Gastric Cancer

Abstract

Objective: We aimed to establish a nomogram for predicting lymph node metastasis in early gastric cancer (EGC) involving human epidermal growth factor receptor 2 (HER2).Methods: We collected clinicopathological data of patients with EGC who underwent radical gastrectomy and D2 lymphadenectomy at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between January 2012 and August 2018. Univariate and multivariate logistic regression analysis were used to examine the relationship between lymph node metastasis and clinicopathological features. A nomogram was constructed based on a multivariate prediction model. Internal validation from the training set was performed using receiver operating characteristic (ROC) and calibration plots to evaluate discrimination and calibration, respectively. External validation from the validation set was utilized to examine the external validity of the prediction model using the ROC plot. A decision curve analysis was used to evaluate the benefit of the treatment.Results: Among 1,212 patients with EGC, 210 (17.32%) presented with lymph node metastasis. Multivariable analysis showed that age, tumor size, submucosal invasion, histological subtype, and HER2 positivity were independent risk factors for lymph node metastasis in EGC. The area under the ROC curve of the model was 0.760 (95% CI: 0.719–0.800) in the training set (n = 794) and 0.771 (95% CI: 0.714–0.828) in the validation set (n = 418). A predictive nomogram was constructed based on a multivariable prediction model. The decision curve showed that using the prediction model to guide treatment had a higher net benefit than using endoscopic submucosal dissection (ESD) absolute criteria over a range of threshold probabilities.Conclusion: A clinical prediction model and an effective nomogram with an integrated HER2 status were used to predict EGC lymph node metastasis with better accuracy and clinical performance.

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

“…Full-texts of the remaining 246 articles were read, and 205 articles were excluded for reasons specified in Figure 1 . Finally, a total of 41 studies were included ( 32 – 72 ).…”

“…There were 35 studies (85.4%) that were conducted in China ( 32 , 33 , 35 – 43 , 45 – 49 , 51 – 54 , 56 – 63 , 65 – 69 , 71 , 72 ), 5 (12.2%) in Korea ( 34 , 44 , 50 , 55 , 64 ) and 1 (2.4%) in Germany ( 70 ), with the publication date ranged from 2004 to 2022. The number of studies using ML for LNM prediction has gradually increased since 2018 ( Figure 2 ).…”

“…It is of critical application value in assisting disease-diagnosing and prognosis-predicting through processing massive and complex medical data ( 30 , 31 ). Currently, ML has been increasingly applied for LNM prediction in GC patients ( 32 – 72 ). However, different types of ML prediction models have great differences in both included predictors and calculation methods of the models ( 73 , 74 ), the results produced by different models are far from unanimous ( 75 ).…”

Tumor Characteristics Associated with Lymph Node Metastasis and Prognosis in Patients with ERBB2-Positive Gastric Cancer

“…In clinical practice, the lymph node metastasis of GC plays a crucial role in choosing subsequent treatment, especially for patients with early GC. ERBB-2 positivity has been shown as a high-risk factor for lymph node metastasis in patients with early GC [ 25 ]. In this study, the rate of lymph node metastasis in ERBB-2 positive patients was 67.2% (82/122), which is significantly higher than that in ERBB-2 negative patients, which was 48.4% (346/714).…”

Comparison between Endoscopic Submucosal Dissection and Surgery in Patients with Early Gastric Cancer

“…Although the total number of people with gastric cancer has decreased in recent years, the incidence rate is increasing among young people [ 1 ]. In China, there are about 478,508 new cases of gastric cancer each year, making it the third leading cause of cancer-related deaths [ 2 ]. Therefore, choosing the appropriate treatment remains critical to improving clinical outcomes.…”