Non-invasive characterization of tumors using Bayesian inference and Virtual Element Method.

BACKGROUND

Accurate and non-invasive detection of tumor cells remains a major challenge in biomedical engineering and clinical diagnostics. Traditional imaging methods often face limitations in resolution, accessibility, or invasiveness. We propose a computational framework combining Bayesian inference with the Virtual Element Method (VEM) to address the inverse problem of tumor characterization using surface temperature measurements.

METHODS

The forward thermal response of biological tissues was modeled using Pennes' bioheat equation, with skin surface temperature distributions as measurable data. Three test scenarios were designed: (1) detecting and quantifying a single, small, elliptical tumor using the Metropolis-Hastings (M-H) algorithm, (2) identification of a cluster of non-elliptical-shaped fragments using M-H algorithm and (3) simultaneous estimation of the number, locations, and sizes of multiple tumors using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm; and assessing the robustness of both inference strategies under varying levels of simulated measurement noise.

RESULTS

In scenarios 1 and 2, the M-H algorithm successfully detected and quantified the single tumor and cluster of tumor cells, demonstrating reliability for localized anomalies. In scenario 3, the RJMCMC algorithm accurately estimated multiple tumor parameters simultaneously, demonstrating the framework's capability to address complex multi-tumor scenarios. Both inference approaches exhibited strong robustness across varying noise levels, ensuring reliable tumor detection and characterization under modeling and measurement noise.

CONCLUSION

The integration of Bayesian inference with the VEM provides a flexible and powerful computational framework for non-invasive tumor detection and characterization. This approach shows strong potential for enhancing thermal-based tumor detection by offering improved reliability and adaptability for clinical diagnostics. Moreover, unlike traditional optimization-based inverse methods, which provide only point estimates, the proposed Bayesian framework yields credible intervals for all inferred parameters, enabling uncertainty quantification, particularly valuable for clinical interpretation.