Real-time lipid patterns to classify viable and necrotic liver tumors

Area of Science:

• Oncology
• Biochemistry
• Analytical Chemistry

Background:

• Real-time molecular pattern analysis offers potential for rapid tumor diagnosis.
• Histopathology is the gold standard but can be time-consuming.
• Understanding tumor metabolic features is crucial for diagnosis and treatment.

Purpose of the Study:

• To evaluate rapid evaporative ionization mass spectrometry (REIMS) for classifying human liver tumors.
• To investigate the utility of REIMS-derived lipid patterns for distinguishing tumor types and metabolic states.
• To compare REIMS classification accuracy with gold standard histopathology.

Main Methods:

• Tissue samples were analyzed using rapid evaporative ionization mass spectrometry (REIMS).
• Multivariate statistical analysis, including principal component analysis-linear discriminant analysis, was applied to lipid patterns.
• REIMS classifications were compared against histopathological diagnoses.

Main Results:

• REIMS achieved 98.3% accuracy in classifying liver parenchyma, hepatocellular carcinoma (HCC), and metastatic adenocarcinoma (MAC).
• Lipid patterns differentiated primary (HCC) from metastatic (MAC) liver tumors.
• A specific ceramide pattern identified necrotic tumor tissue with 92.9% accuracy.

Conclusions:

• REIMS provides accurate, real-time classification of human liver tumors based on molecular lipid profiles.
• REIMS analysis of lipid patterns can distinguish primary from metastatic liver cancers and assess tumor viability.
• This technology holds promise for improving clinical decision-making in cancer precision medicine.