Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning.

Purpose: To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC).

Methods: Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort,  = 216; test cohort,  = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images.

Optimal Size Criterion for Malignant Lymph Nodes and a Novel Lymph Node Clinical Staging System for Unresectable Esophageal Squamous Cell Carcinoma: Evaluation by Multislice Spiral Computed Tomography.

The current Chinese draft nodal clinical staging system for unresectable esophageal cancer is controversial. Our study aimed to propose a new diagnostic criterion for lymph node metastasis (LNM) detected by multislice spiral computed tomography (MSCT) in nonsurgically treated esophageal squamous cell carcinoma (ESCC) patients and then develop a novel lymph node (LN) clinical staging system for better individual prognostic prediction. The short-axis diameters of regional LNs were measured in 393 nonsurgical patients.