Machine Learning-Driven SERS Analysis Platform for Accurate and Rapid Diagnosis of Peritoneal Metastasis from Gastric Cancer.

BACKGROUND

Peritoneal metastasis (PM) is the most common form of distant metastasis in gastric cancer and is a major cause of mortality. Current diagnostic approaches suffer from low sensitivity, time-consuming procedures, and cannot provide real-time diagnostic information. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms has emerged as a promising tool for cancer diagnosis.

PATIENTS AND

METHODS

Raman spectra were collected from the peritoneal lavage fluid (PLF) of 120 patients with gastric cancer and analyzed using three machine learning models: principal component analysis-linear discriminant analysis (PCA-LDA), random forest (RF), and support vector machine (SVM). The sensitivity, specificity, accuracy, false positive rate, false negative rate, positive predictive value, and negative predictive value were calculated. Receiver operating characteristic curve analysis was used to assess the diagnostic performance.

RESULTS

The accuracy, sensitivity, and specificity of SERS analysis to determine PM with PCA-LDA were 95.7%, 87.0%, and 95.5%; with RF were 95.4%, 91.3%, and 96.0%; with SVM were 95.5%, 91.3%, and 96.0%. For exfoliative cytology, these parameters were 72.0%, 40.0%, and 100%. For computed tomography (CT) scan, these parameters were 72.5%, 57.9%, and 85.7%. In addition, the performance of these models (PCA-LDA, RF, and SVM) demonstrated high diagnostic accuracy, with area under the curve values of 96.9%, 92.1%, and 93.4%, respectively. The diagnostic performance of all models in diagnosing PM is significantly better than those of exfoliative cytology and CT imaging.

CONCLUSIONS

The integration of SERS with machine learning models provides a simple, convenient, and cost-effective tool for PLF, offering significant potential for improving the diagnosis of PM.