P2051 - Artificial Intelligence-Based Radiomics for Non-Invasive Detection of Lymph-Node Metastasis in Gastric Cancer: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Award: ACG Outstanding Research Award in the Stomach and Spleen Category

Award: ACG Presidential Poster Award

Zainab Hussein, ¹, Mohamed A. B. Elnaggar, MD², Ahmed Bahnasy, MD³, Mohamed Yasser Elnaggar, MBBCH, MSc⁴, Mark Nasseem, MBBCh⁵, Basant Elsayed, MBBCh⁶, Areeba Mariam Mehmood, ⁷, Ahmed Ahmed, ⁸, Ismail Elkhattib, MBBCh⁹
1Faculty of Medicine, Minia University, Minia, Al Minya, Egypt; 2Hartford Healthcare, Hartford, CT; 3Mayo Clinic, Rochester, MN; 4Mansoura University, Mansoura, Ad Daqahliyah, Egypt; 5Ain Shams University, Cairo, Al Qahirah, Egypt; 6Ain Shams University, Cairo, Ad Daqahliyah, Egypt; 7Dr.Faisal Masood Teaching Hospital,Sargodha, Punjab, Punjab, Pakistan; 8Al-Azhar University Faculty of Medicine Damietta, Damietta, Dumyat, Egypt; 9University of Nebraska, Hartford, CT

Introduction: Accurate preoperative lymph node staging remains a critical challenge in gastric cancer management, as conventional imaging cannot reliably detect micrometastases. Radiomics, which extracts subvisual quantitative features from standard imaging modalities, utilizes deep learning and artificial intelligence (AI) to decode metastatic patterns undetectable by human observers. This meta-analysis evaluates its diagnostic accuracy for lymph node metastasis detection.

Methods: A PRISMA-guided systematic review of PubMed, Embase, Scopus, and CENTRAL was conducted for original studies until April 2025. Eligibility criteria included studies that developed or validated radiomic models using AI/deep learning tools for non-invasive LNM detection in gastric cancer across various imaging modalities. Bivariate random-effects models pooled sensitivity, specificity, and AUC with 95% CIs. Heterogeneity was quantified using I² statistics. (PROSPERO ID: CRD420251064088)

Results: Analysis of 28 studies (9,433 patients) revealed a pooled sensitivity of (0.80, 95% CI [0.76-0.84]; I²=89.6%, p < 0.0001) and specificity of (0.83, 95% CI [0.80-0.85]; I²=90.8%, p < 0.0001). For AUC evaluation across 39 studies (14,002 patients), the pooled AUC was (0.85, 95% CI [0.84-0.87]; I²=97.3%, p < 0.0001). Substantial heterogeneity primarily reflected variation in segmentation methodology, though uniform lymph node size thresholds improved consistency. The QUADAS-2 tool assessed methodological quality, revealing low bias risk in most studies.

Discussion: Radiomics demonstrates clinically meaningful accuracy for nodal metastasis detection, enabling diagnostic de-escalation through reduced reliance on invasive staging, personalized surgical planning via informed lymphadenectomy decisions, and neoadjuvant therapy optimization for high-probability metastasis cases. Standardization of feature extraction and multicenter prospective validation integrating endoscopic ultrasound remains essential before clinical implementation. This technology promises to transform gastric cancer staging from anatomical approximation to quantitative precision oncology.


Figure: Random effects model and Prediction interval (Area under the curve "AUC" )


Figure: Sensitivity and Specificity blots (Random effects model and prediction interval)

Disclosures:
Zainab Hussein indicated no relevant financial relationships.
Mohamed A. Elnaggar indicated no relevant financial relationships.
Ahmed Bahnasy indicated no relevant financial relationships.
Mohamed Yasser Elnaggar indicated no relevant financial relationships.
Mark Nasseem indicated no relevant financial relationships.
Basant Elsayed indicated no relevant financial relationships.
Areeba Mariam Mehmood indicated no relevant financial relationships.
Ahmed Ahmed indicated no relevant financial relationships.
Ismail Elkhattib indicated no relevant financial relationships.

Zainab Hussein, ¹, Mohamed A. B. Elnaggar, MD², Ahmed Bahnasy, MD³, Mohamed Yasser Elnaggar, MBBCH, MSc⁴, Mark Nasseem, MBBCh⁵, Basant Elsayed, MBBCh⁶, Areeba Mariam Mehmood, ⁷, Ahmed Ahmed, ⁸, Ismail Elkhattib, MBBCh⁹. P2051 - Artificial Intelligence-Based Radiomics for Non-Invasive Detection of Lymph-Node Metastasis in Gastric Cancer: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy, ACG 2025 Annual Scientific Meeting Abstracts. Phoenix, AZ: American College of Gastroenterology.