Multiphysics Modeling and Optimization of Laser Therapy for Superficial Tumors: A Review Using COMSOL Multiphysics

Abstract

Laser-induced thermal therapy has emerged as a promising minimally invasive strategy for the treatment of superficial tumors, offering spatially controlled energy delivery and reduced damage to surrounding healthy tissue. However, treatment efficacy depends critically on precise control of laser parameters, tissue optical properties, and bioheat transfer mechanisms. Computational multiphysics modeling has therefore become an essential tool for understanding laser–tissue interactions and optimizing therapeutic protocols prior to clinical application. This review provides a structured synthesis of current research on the optimization of laser treatment for superficial tumors using multiphysics simulation frameworks, with particular emphasis on COMSOL Multiphysics® as a widely adopted finite-element modeling platform. The review analyzes the fundamental physical processes governing laser therapy, including optical absorption and scattering, heat transfer in perfused biological tissues, and thermally induced cellular damage. Established modeling approaches such as the Pennes bioheat equation, Beer–Lambert light attenuation, and Arrhenius damage kinetics are critically examined in relation to treatment planning, temperature prediction, and thermal dose control. Recent advances including nanoparticle-assisted photothermal therapy, image-guided laser ablation, and real-time thermal monitoring are also evaluated to demonstrate how computational modeling improves prediction of temperature distributions, enhances fluence control, and supports safer treatment margins. By integrating optical, thermal, and physiological processes within a unified simulation environment, multiphysics models enable improved treatment selectivity and reduced collateral tissue injury. Overall, this review highlights the central role of computational modeling in advancing precision laser therapy, identifies current limitations in model-based treatment planning, and outlines future research directions for improving clinical translation.