## 5 Conclusions  
In this study, we evaluated the vibrational properties of agarose samples simulating soft-tissue tumors with different [PERSON]'s moduli by L-RFA using a sweep pulse excitation method and validated a machine learning-based analysis method. It was found that the difference in [PERSON]'s moduli is reflected in the difference in the vibration frequency spectra by utilizing the forced excitation generated by laser-induced elastic waves. Using the explanatory variables extracted from the L-RFA data, a machine learning-based analysis was performed to achieve a high [PERSON]'s modulus prediction accuracy. Moreover, we found that it is critical to use more averaged data for the prediction of the [PERSON]'s moduli of soft materials that are susceptible to disturbances. This stiffness evaluation by L-RFA revealed that a high spatial resolution below the irradiation spot size of the excitation laser can be obtained by observing higher-order vibration modes. Diagnosis using the L-RFA technique was achieved by irradiating two laser beams, one to generate LIPTEW and the other to measure it. The laser beam can be easily and freely guided using an optical fiber and can be easily mounted on endoscopic instruments. This setup shows excellent potential to achieve in vivo measurements in situ. In addition, highly accuracy was demonstrated by the regression estimation of [PERSON]'s modulus with a coefficient of determination R\({}^{2}\) of 0.950, using machine learning-based SVR analysis. The ability to handle continuous values, rather than a simple hard/soft classification problem, leads to stiffness mapping and the acquisition of stiffness spectra, which can make a significant contribution to CADe. The stiffness measurement technique using L-RFA demonstrates significant potential for achieving cellular stiffness while preserving the shape of the organ. This method can facilitate the development of imaging techniques for elastic moduli by performing 2D scanning. In addition, machine learning-based analysis techniques for large amounts of L-RFA data are essential for CADe. The LIPTEW generation and measurement technique in L-RFA is a quantitative, miniaturized, and precise assay of the physician's palpation technique, using a sweep pulse excitation scheme. The proposed scheme is noninvasive with very low laser power, and thus, it allows for high cell viability measurements, which we expect to be studied in the future. In vitro and in vivo measurements of organ tumors using L-RFA will allow future studies to move into a more concrete verification phase for clinical use. Simultaneously, LIPTEW was generated by a 405 nm LD, which is a wavelength that is strongly absorbed on the organ surface. In the future, by using near-infrared wavelengths in the region of low absorption and through scattering on the living body, it will be possible to verify the approach for tumor cells that are located at a depth of several millimeters, which are not visible on the surface. Additionally, in this demonstration, each measurement took 2 s. However, because the averaging of 10 measurements was necessary to improve the estimation accuracy by machine learning, a measurement time of 20 s per point is required. Therefore, the throughput is sufficiently high compared to the conventional pipette aspiration method, AFM, and optical tweezers. However, the throughput is comparable to the latest improved technique [17], which is an in vitro method. To clarify the superiority of the measurement method using L-RFA in the future, an improvement of the data cleansing technique is also necessary. The demonstrated performance of L-RFA represents a milestone and can serve as a basis for future studies on physical oncology, soft-tissue tumor stiffness diagnoses, and medical imaging technologies. [PERSON] was responsible for all the procedures, laser system construction, data gathering, analysis, and writing of the manuscript. [PERSON] and [PERSON] were responsible for data analysis. All authors have read and agreed to the published version of the manuscript.