The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction - Lu, Chenghao; Liu, Lu; Yin, Minyue; Lin, Jiaxi; Zhu, Shiqi; Gao, Jingwen; Qu, Shuting; Xu, Guoting; Liu, Lihe; Zhu, Jinzhou; Xu, Chunfang (Frontiers in Medicine, 2024)
1) Background: Lymph node metastasis (LNM) is thought to be an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning algorithms in predicting lymph node metastasis (LNM) in Siewert typeⅡT1 AEG. (2) Methods: A total of 878 patients with Siewert type ⅡT1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop LNM prediction models. The performance of predictive models was assessed by accuracy, sensitivity, specificity, the area under the receiver operating characteristic curve (AUC), etc. (3) Results: Patients with LNM had a higher proportion of males, poor degree of differentiation and submucosal infiltration, with statistical differences. The deep learning (DL) model had relatively good accuracy (0.713), and sensitivity (0.868) among the five models. Moreover, it had the highest AUC (0.781) and sensitivity (1.000) in the test set. (4) Conclusion: The DL model showed good predictive performance among five automated machine learning models, which suggests the advantage of automated machine learning in modeling LNM prediction in patients with Siewert typeⅡT1 AEG.