Characterization of Laser-Induced Photothermal Vibration for Young’s Modulus Imaging toward Computer-Aided Detection

The stiffness of tumor cells has a significant influence on invasion and metastasis strategies. In this study, we developed a novel detection method, called laser resonance frequency analysis (L-RFA), for soft tissue tumors in physical oncology. In addition, we evaluated the characteristics of the laser-induced photo-thermal elastic wave (LIPTEW) obtained by L-RFA using agarose gels with different stiffnesses to simulate soft tissues. The LIPTEW diagnosis based on the audible wave range indicated a great potential too, which allows for the measurement of the stiffness of single cells while maintaining organ geometry. In particular, we observed vibrations with high spatial resolution of less than one-tenth of the laser irradiation spot size. From the obtained results, our proposed machine learning method achieved high accuracy and precision, with coefficient of determination R2 = 0.950. The characterization of the LIPTEW on the L-RFA to predict single cell stiffness could be a milestone for future studies on physical oncology, soft-tissue tumor stiffness diagnoses, and medical imaging technologies.