# Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images

## TL;DR

A deep learning model using CT and PET images can diagnose lymph node metastasis in esophageal cancer as well as experts.

## Contribution

A multimodal deep learning model for diagnosing lymph node metastasis in esophageal cancer using CT and PET images is developed and validated.

## Key findings

- The deep learning model achieved an AUC of 0.81 with multimodal imaging.
- The model's performance was comparable to that of experienced experts (AUC 0.84).

## Abstract

This study aimed to assess the performance of a deep learning model using multimodal imaging for detecting lymph node metastasis in esophageal cancer in comparison to expert assessments.

A retrospective analysis was performed for 521 lymph nodes from 167 patients with esophageal cancer who underwent esophagectomy. Deep learning models were developed based on multimodal imaging, including non-contrast-enhanced computed tomography, contrast-enhanced computed tomography, and positron emission tomography imaging. The diagnostic performance was evaluated and compared with expert assessments using a receiver operating characteristic curve analysis.

The area under the receiver operating characteristic curve values for the deep learning model were 0.81 with multimodal imaging, 0.73 with non-contrast-enhanced computed tomography, 0.72 with contrast-enhanced computed tomography, and 0.75 with positron emission tomography were calculated. The area under the curve of the deep learning model (0.81) demonstrated diagnostic performance comparable to that of experienced experts (area under the curve, 0.84; P = 0.62, DeLong’s test).

The multimodal deep learning model using computed tomography and positron emission tomography demonstrated performance comparable to that of experts in diagnosing the presence of lymph node metastasis, a key prognostic factor in esophageal cancer, suggesting its potential clinical utility.

The online version contains supplementary material available at 10.1007/s00595-025-03152-5.