The following is a summary of “Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma,” published in the March 2024 issue of Oncology by Cai et al.

In the contemporary landscape of oncological treatment, addressing the geometrically intricate nature of nasopharyngeal carcinoma (NPC) with intensity-modulated radiation therapy (IMRT) necessitates meticulous planning, often fraught with challenges associated with manual trial-and-error methods. This paper introduces a novel approach to streamlining the IMRT planning process for patients with NPC by proposing an automatic plan generation technique leveraging fluence prediction, followed by meticulous plan refinement, enhancing planning efficiency and ensuring consistently high plan quality. 

Using a cohort comprising 38 patients with NPC undergoing nine-beam IMRT, the methodology integrates a sophisticated deep learning model trained to generate static field fluence maps, utilizing 3D computed tomography images and structure contours as input parameters. The automation of IMRT treatment planning is facilitated by employing the fluence maps’ generated doses, subject to slight adjustments for fine-tuning the plan. Subsequent evaluation of plan quality reveals significant improvements in conformity and homogeneity concerning planning target volumes (PTVs), albeit with minor disparities noted in PTV-1 conformity. Additionally, the automatic plans demonstrate notable enhancements in dosimetric metrics for organs at risk (OARs), particularly evidenced by reductions in maximum dose (D_max) for critical structures such as the brainstem and spinal cord, alongside diminished mean dose (D_mean) for the bilateral parotid glands, with statistical significance (P < 0.05). 

In conclusion, the study demonstrates the successful implementation of an automatic IMRT plan generation method tailored for patients with NPC, showcasing heightened planning efficiency and yielding comparable or superior plan quality to conventional clinical plans. The discernible improvements observed in pre- and post-plan refinement underscore the significance of optimizing dose objectives derived from predicted fluence maps to attain optimal automatic plans. This innovative approach holds promise in augmenting treatment planning workflows for NPC and underscores the potential of leveraging advanced technologies to enhance oncological care paradigms.

Source: ro-journal.biomedcentral.com/articles/10.1186/s13014-024-02401-0