Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a...

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• This study evaluates the accuracy of artificial intelligence (AI) models, clinical models (CM), and integrated models (IM) for predicting neoadjuvant chemotherapy (NACT) response in gastric cancer (GC) patients.
• The research aims to identify the diagnostic capabilities of AI models and compare their performance against CM and IM using head-to-head comparative studies.

Approach:

• A systematic literature search was conducted across PubMed, Web of Science, Cochrane Library, and Embase until September 5, 2023.
• Nine studies involving 3313 patients were included for AI model analysis, with seven head-to-head studies comprising 2699 patients.
• Study quality was assessed using QUADAS-2 criteria, and diagnostic accuracy was analyzed using forest plots, HSROC curves, meta-regression, and Deeks' funnel plots.

Key Points:

• AI models demonstrated a pooled sensitivity (SEN) of 0.75 and specificity (SPE) of 0.77 across nine studies.
• In head-to-head comparisons, AI models showed pooled SEN of 0.77 and SPE of 0.79.
• The integrated model (IM) exhibited higher pooled SEN (0.83) but similar SPE (0.69) compared to AI models, with no significant statistical difference noted in pairwise comparisons.
• Subgroup analyses indicated that factors like cohort type, cut-off value, and gold standard choice influence model performance.

Conclusions:

• AI models are viable for predicting NACT response in GC patients, with CT-based deep learning models showing particular sensitivity.
• While IMs offer higher sensitivity, AI models provide comparable specificity, suggesting their potential utility in clinical decision-making.
• Further large-scale, rigorously designed diagnostic accuracy and head-to-head comparative studies are recommended to validate these findings.