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There is substantial evidence that radiation-induced lymphopenia (RIL) is commonly associated with conventional radiotherapy (3DCRT, IMRT or VMAT).  It suppresses the immune system, which adversely impacts survival outcomes of (chemo)radiotherapy (CRT) and increases the risk of infections and normal tissue toxicities.  Radiation therapy may also reduce the diversity of the immune system even if it seems to have recovered partially or wholly after CRT as assessed based on total lymphocyte counts.  Such diversity is critical for protection against wide array pathogens.  There is also increasing evidence that the effectiveness of immunotherapy following CRT, which depends on the wellbeing of the immune system, may be abrogated by RIL.  

Proton therapy, intensity modulated proton therapy (IMPT) in particular, because of its compact dose distributions (smaller “dose bath”) compared to photon therapy, appears to cause less damage to the immune system.  To optimally mitigate immune suppression using dosimetric approaches with IMPT, or even with IMRT, it is essential to understand the dependence of RIL on underlying physical, biological and clinical factors, and develop “personalized” RIL models capable of predicting an individual patient’s risk.  Personalized models are likely to be more accurate compared with the one--fits-all models employed in conventional radiotherapy practice that use standard dose-volume constraints.   Further improvement in accuracy of models may be achieved with deep learning techniques compared to the traditional statistical techniques.  

Accurately predicted his/her RIL risk with such models may be among the important considerations for the selection of optimum treatment modality (protons or photons) for each patient.  Furthermore, the incorporation of personalized models in the criteria of optimization IMRT or IMPT may allow further mitigation of RIL.  Considering the power of IMPT due to the availability of an extra degree of freedom, that of energy, it may have a significant potential to achieve RIL risk reduction without compromising standard constraints on dose to tumor and normal tissues.