Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis
Shuyan Zhang, Qing Yang Steve Wu, Melissa Hum, Jayakumar Perumal, Ern Yu Tan, Ann S. G. Lee, Jinghua Teng, U. S. Dinish, Malini Olivo
Abstract
-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.
Topics & Concepts
Breast cancerBiomarkerFingerprint (computing)CancerFourier transform infrared spectroscopyCharacterization (materials science)RNAComputational biologyChemistryMaterials scienceComputer scienceNanotechnologyMedicineArtificial intelligenceInternal medicineBiologyBiochemistryOpticsPhysicsGeneSpectroscopy Techniques in Biomedical and Chemical ResearchSpectroscopy and Chemometric AnalysesIdentification and Quantification in Food