The T stage of esophageal cancer can be effectively predicted by muscularis propria thickness and muscularis propria + mucosa thickness under ultrasonic gastroscopy

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach

BACKGROUND

Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives.

METHOD

We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation.

RESULTS

Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability.

CONCLUSION

ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.

Collaborative multidisciplinary management and expertise of cT2-3 locally advanced operable esophageal squamous cell carcinoma: a report of two cases

Background

The accurate clinical staging of esophageal squamous cell carcinoma (ESCC) is pivotal for guiding treatment strategies. However, the current precision in staging for clinical T (cT)2 and cT3 stages remains unsatisfactory. This article discusses the role of multidisciplinary teams (MDTs) in the clinical staging and formulation of neoadjuvant treatment strategies for locally advanced operable ESCC. These challenges underscore the importance of precise staging in the decision-making process for appropriate therapeutic interventions.

Case Description

Through the lens of two patient case studies with locally advanced resectable ESCC, the article showcases the intricate process of treatment planning undertaken by MDTs. It captures a range of expert perspectives from Japan, China, Hong Kong (China), Korea, the USA, and Europe, focusing on the challenges of differentiating between cT2 and cT3 stages of the disease, which is a critical determinant in the management and therapeutic approach for patients.

Conclusions

The article concludes that the accurate staging of ESCC is a cornerstone in determining the most suitable treatment strategies. It underscores the vital role that MDTs play in both clinical staging and the decision-making process for treatment. Highlighting the limitations in current diagnostic methods, the article emphasizes the urgent need for advanced research and the refinement of diagnostic tools to improve the precision of staging, particularly between the cT2 and cT3 stages. It suggests that future research should consider whether a reclassification of these stages could be warranted to enhance treatment planning and outcomes for patients with ESCC.