Prediction of neoadjuvant therapy efficacy in gastric cancer: the interplay between biomarkers and radiomics and its potential for clinical translation

TL;DR

This review explores how biomarkers and radiomics can predict the effectiveness of neoadjuvant therapy in gastric cancer, aiming to improve personalized treatment strategies.

Contribution

The paper systematically reviews recent advancements in biomarkers and radiomics for predicting neoadjuvant therapy efficacy in gastric cancer.

Findings

Systemic immune-inflammation index (SII) is a low-cost, non-invasive marker that predicts treatment response and survival in gastric cancer patients.

Radiomics and deep learning models offer non-invasive methods to predict tumor response and survival risk by integrating clinical and radiological data.

Combining immune checkpoint inhibitors with therapies targeting Claudin 18.2 expands personalized treatment options for specific gastric cancer subtypes.

Abstract

Neoadjuvant therapy (NACT) for locally advanced gastric cancer (LAGC) plays a crucial role in improving surgical resection rates and patient prognosis. However, there is significant heterogeneity in patient responses to treatment, necessitating effective predictive tools for personalized therapy. This review systematically summarizes the latest research advancements in biomarkers and imaging models for predicting the efficacy of neoadjuvant treatment in gastric cancer. In the field of biomarkers, systemic immune-inflammation index (SII), microRNAs (miRNAs), and aspartate β-hydroxylase (ASPH) are molecular markers that influence chemotherapy sensitivity by modulating the tumor microenvironment or signaling pathways. Among them, SII, a low-cost and non-invasive inflammatory marker, has been shown to predict patient survival and treatment response. Differential expression of miRNAs (e.g., miR-7, miR-143) provides molecular evidence for evaluating the efficacy of neoadjuvant chemotherapy. ASPH, on the other hand, promotes chemotherapy resistance by activating the Notch/SRC pathway, making it a potential therapeutic target. Additionally, immune checkpoint inhibitors (ICIs) combined therapy has demonstrated a high pathological complete response rate in patients with high PD-L1 expression or the dMMR/MSI-H subtype. Clinical trials of Claudin 18.2-targeted therapies (e.g., Zolbetuximab) further expand personalized treatment options. Radiomics and deep learning models (e.g., DLDRN, DLCS), by integrating clinical data with radiological features, offer non-invasive methods to predict tumor response and survival risk, providing valuable support for clinical decision-making. This review aims to systematically collate the latest evidence on biomarkers and radiomics for predicting the efficacy of neoadjuvant therapy in gastric cancer. To achieve this objective, we focus on three core domains: (1) key biomarkers with clinical translational potential (such as SII, miRNA, PD-L1, etc.); (2) CT- and MRI-based radiomics predictive models; (3) Future prospects for multi-omics integration strategies. Despite the abundance of research in this field, this paper prioritizes the analysis and discussion of prospective or high-quality retrospective studies that include explicit efficacy prediction endpoints (such as pCR, TRG, AUC) to ensure the reliability of the evidence presented. This review emphasizes that multi-omics integrated predictive models and the clinical translation of targeted therapies represent critical directions for future research, aiming to optimize the neoadjuvant treatment strategies for locally advanced gastric cancer.

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