Blood test using AI spots gastric cancer with 96% accuracy
Researchers have developed a blood screening method that detects gastric cancer earlier and more reliably than existing clinical markers, achieving near-perfect accuracy in early-stage cases. The breakthrough could reshape cancer diagnostics by offering a noninvasive alternative to invasive procedures, potentially reducing healthcare costs and improving patient outcomes in high-risk populations.
Originaltitel: GC–IMS-based analysis of serum volatile organic compounds for diagnosis of gastric cancer
Gastric cancer detection remains challenging due to the lack of noninvasive early diagnostic tools. This study investigates profiling of serum volatile organic compounds (VOCs) using gas chromatography-ion mobility spectrometry (GC-IMS) for gastric cancer screening. Serum samples were obtained from 277 participants, including 123 patients with gastric cancer, 38 patients with precancerous diseases (PD), and 116 healthy controls (HC). In the model development group, Kruskal-Wallis tests showed that the levels of 19 VOCs differed significantly among gastric cancer, PD, and HC groups (p < 0.05). Based on the VOCs that differed significantly, a support vector machine (SVM) model achieved the best performance among the six models tested. Using importance ranking and forward selection, 11 VOCs were selected for the final model, achieving 96.4% accuracy in the validation set and 92.9% in an independent test set, showing higher diagnostic accuracy than the traditional tumor marker carcinoembryonic antigen. The model also achieved 100% sensitivity and > 90% specificity for detecting early gastric cancer in both the validation and test sets. Collectively, our findings suggest that GC-IMS-based serum VOC profiling may offer a potential noninvasive approach for gastric cancer detection.