Abstract
Abstract
Purpose
Accurate preoperative diagnosis of lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC) is crucial for determining treatment strategies, including organ-sparing therapies. We aimed to develop and validate a deep learning (DL) model using rotational maximum-intensity projection (MIP) 18 F-FDG PET images to improve the prediction of LN metastasis.
Materials and methods
This retrospective study included 185 patients with ESCC (146 receiving neoadjuvant chemotherapy) who underwent preoperative imaging using a silicon photomultiplier PET scanner. A convolutional neural network (CNN) was developed using six rotational MIP images (angles from − 60° to 90°). The architecture utilized a ResNet-50 backbone with weight sharing across views. The ground truth for LN metastasis was established via postoperative histopathology, including Grade 3 pathological response as positive. The performance of the model in the test set (
n
= 36) was compared with that of radiologist reports and SUVmax analysis using the area under the receiver operating characteristic curve (AUC) and diagnostic accuracy.
Results
The CNN model achieved an AUC of 0.82 (95% CI 0.57–0.94), which was higher but not significantly different from the SUVmax method (0.77,
p
> 0.05). Although the differences were not statistically significant (
p
> 0.05), the CNN model achieved the highest absolute diagnostic accuracy of 86% at the optimal threshold, compared with the SUVmax-based method (67%), the clinical data model (75%), and radiologist reports (69%). Notably, while the CNN model exhibited a lower specificity (75%) than radiologist reports (92%), it demonstrated high sensitivity (92%) compared to radiologist (58%) and SUVmax (63%), effectively serving as a diagnostic safety net.
Conclusion
Our PET-based CNN model using rotational MIP images demonstrated comparable diagnostic accuracy and higher sensitivity for LN metastasis in ESCC than the conventional methods. This proof-of-concept highlights its potential as a preoperative tool for optimizing treatment selection and minimizing understaging.