Radiation-induced lymphopenia (RIL) is a frequent, and often considered unavoidable, side effect of radiation therapy (RT), whether or not chemotherapy is included. However, in the last few years several studies have demonstrated the detrimental effect of RIL on therapeutic outcomes, with conflicting findings concerning possible inferior patient survival. In addition, since immunotherapeutic treatment has become an integral part of cancer therapy, preserving the immune system is recognized as crucial. Given this background, various research groups have reported on different frameworks for modelling RIL, frequently based on different definitions of RIL itself, and discordant results have been reported. Our aim is to critically review the current literature on RIL modelling and summarize the different approaches recently proposed to improve the prediction of RIL after RT and aimed at immunity-sparing RT. A detailed description of these approaches will be outlined and illustrated through their applications as found in the literature from the last five years. Such a critical analysis represents the necessary starting step to develop an effective strategy that ultimately could harmonize the diverse modelling methods.
Keywords: Advanced machine learning; Blood irradiation model; Modelling; Radiation-induced lymphopenia; Voxel-based analysis.
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