Deep Learning Integration of Endoscopic Ultrasound Features and Serum Data Reveals <i>LTB4</i> as a Diagnostic and Therapeutic Target in ESCC.
Authors
Affiliations (6)
Affiliations (6)
- Chifeng Graduate Training Base, Jinzhou Medical University, Chifeng, People's Republic of China.
- Department of Laboratory Medicine, The Second Affiliated Hospital of Chifeng University, Chifeng, People's Republic of China.
- Department of Thoracic Surgery, Guilin People's Hospital, Guilin, People's Republic of China.
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany.
- Department of Thoracic Surgery, The First Affiliated Hospital of Guilin Medical University, Guilin, People's Republic of China.
- School of Basic Medical Sciences, Chifeng University, Chifeng, Inner Mongolia, People's Republic of China.
Abstract
<b><i>Background:</i></b> Early diagnosis and accurate prediction of treatment response in esophageal squamous cell carcinoma (ESCC) remain major clinical challenges due to the lack of reliable and noninvasive biomarkers. Recently, artificial intelligence-driven endoscopic ultrasound image analysis has shown great promise in revealing genomic features associated with imaging phenotypes. <b><i>Methods:</i></b> A prospective study of 115 patients with ESCC was conducted. Deep features were extracted from endoscopic ultrasound using a ResNet50 convolutional neural network. Important features shared across three machine learning models (NN, GLM, DT) were used to construct an image-derived signature. Plasma levels of leukotriene B4 (<i>LTB4</i>) and other inflammatory markers were measured using enzyme-linked immunosorbent assay. Correlations between signature and inflammation markers were analyzed, followed by logistic regression and subgroup analyses. <b><i>Results:</i></b> The endoscopic ultrasound image-derived signature, generated using deep learning algorithms, effectively distinguished esophageal cancer from normal esophageal tissue. Among all inflammatory markers, <i>LTB4</i> exhibited the strongest negative correlation with the image signature and showed significantly higher expression in the healthy control group. Multivariate logistic regression analysis identified <i>LTB4</i> as an independent risk factor for ESCC (odds ratio = 1.74, <i>p</i> = 0.037). Furthermore, <i>LTB4</i> expression was significantly associated with patient sex, age, and chemotherapy response. Notably, higher <i>LTB4</i> levels were linked to an increased likelihood of achieving a favorable therapeutic response. <b><i>Conclusions:</i></b> This study demonstrates that deep learning-derived endoscopic ultrasound image features can effectively distinguish ESCC from normal esophageal tissue. By integrating image features with serological data, the authors identified <i>LTB4</i> as a key inflammation-related biomarker with significant diagnostic and therapeutic predictive value.