Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model:...

Area of Science:

• Oncology
• Radiology
• Artificial Intelligence in Medicine

Background:

• Early recurrence in locally advanced gastric cancer (LAGC) is linked to poor prognosis.
• Accurate prediction of early recurrence is crucial for tailoring treatment strategies.
• Identifying predictive markers can improve patient management and outcomes.

Purpose of the Study:

• To develop a deep learning model (DLER) for predicting early recurrence in LAGC patients using preoperative CT images.
• To integrate clinical factors with the DLER model (DLER MLP) for enhanced prediction accuracy.
• To explore the biological basis associated with early recurrence predicted by the DLER MLP model.

Main Methods:

• A retrospective study involving 620 LAGC patients from three medical centers and TCIA.
• Development of a DLER model using DenseNet169 and multiphase 2.5D CT images.
• Integration of clinical factors into a multilayer perceptron (MLP) classifier (DLER MLP) for prediction.
• Performance evaluation using AUC, accuracy, sensitivity, and specificity; survival analysis using log-rank test; genetic analysis via RNA-sequencing.

Main Results:

• The DLER MLP model demonstrated superior performance in predicting early recurrence compared to DLER and clinical models across internal and external validation sets (AUCs ranging from 0.814 to 0.891).
• DLER MLP effectively stratified patients based on early recurrence-free survival, disease-free survival, and overall survival (P < 0.001).
• High DLER MLP scores correlated with upregulated tumor proliferation pathways (WNT, MYC, KRAS) and increased immune cell infiltration.

Conclusions:

• The DLER MLP model, utilizing CT images, is a valuable tool for predicting early recurrence in LAGC patients.
• This AI-driven approach can assist in optimizing treatment strategies and patient monitoring.
• The model's findings provide insights into the biological underpinnings of early recurrence in gastric cancer.