Impact of nominal photon energies on normal tissue sparing in knowledge-based radiotherapy treatment planning for rectal cancer patients

Abstract

The interactive adjustment of the optimization objectives during the treatment planning process has made it difficult to evaluate the impact of beam quality exclusively in radiotherapy. Without consensus in the published results, the arbitrary selection of photon energies increased the probability of suboptimal plans. This work aims to evaluate the dosimetric impact of various photon energies on the sparing of normal tissues by applying a preconfigured knowledge-based planning (RapidPlan) model to various clinically available photon energies for rectal cancer patients, based on model-generated optimization objectives, which provide a comparison basis with less human interference. A RapidPlan model based on 81 historical VMAT plans for pre-surgical rectal cancer patients using 10MV flattened beam (10X) was used to generate patient-specific objectives for the automated optimization of other 20 patients using 6X, 8X, 10X (reference), 6MV flattening-filter-free (6F) and 10F beams respectively on a TrueBeam accelerator. It was observed that flattened beams produced very comparable target dose coverage yet the conformity index using 6F and 10F were clinically unacceptable (>1.29). Therefore, dose to organs-at-risk (OARs) and normal tissues were only evaluated for flattened beams. RapidPlan-generated objectives for 6X and 8X beams can achieve comparable target dose coverage as that of 10X, yet the dose to normal tissues increased monotonically with decreased energies. Differences were statistically significant except femoral heads. From the radiological perspective of view, higher beam energy is still preferable for deep seated tumors, even if multiple field entries such as VMAT technique can accumulate enough dose to the target using lower energies, as reported in the literature. In conclusion, RapidPlan model configured for flattened beams cannot optimize un-flattened beams before adjusting the target objectives, yet works for flattened beams of other energies. For the investigated 10X, 8X and 6X photons, higher energies provide better normal tissue sparing.

Reducing variability among treatment machines using knowledge‐based planning for head and neck, pancreatic, and rectal cancer

“…For this region, classical radiotherapy 27,28 VMAT for RC was also reported as a result with respect to RapidPlan. 21 They clarified that the investigated 10X, 8X, and 6X photons and higher energies provide better normal tissue sparing in RapidPlan. 21 Our findings suggested that higher beam energies were still advantageous in normal tissue sparing for deep-seated tumors of large volumes, even if many beam entries were used, such as in the VMAT technique.…”

“…Huang et al mentioned that target parameter in Rapid-Plan models configured for flattened beams cannot optimize unflattened beams without adjustments. 21 However, the authors did not validate to adjust optimization parameters. Castriconi et al mentioned that the validation step can enhance the robustness of the RapidPlan model to reproduce the plans with the same quality.…”

“…17 The statistical information in RapidPlan models varies according to the plan and treatment machine parameters due to the geometry-based expected dose (GED) calculation step. [18][19][20][21] Thus, it is important to understand model characteristics in RapidPlan in more detail. GED-related factors may include the relationship between the geometric and dosimetric features of the planning target volume (PTV) and OARs of the library plans.…”

“…Although knowledge‐based planning using RapidPlan ensures efficiency in clinical practice, the model parameters in RapidPlan affect the quality of the predicted DVHs 17 . The statistical information in RapidPlan models varies according to the plan and treatment machine parameters due to the geometry‐based expected dose (GED) calculation step 18–21 . Thus, it is important to understand model characteristics in RapidPlan in more detail.…”

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

“…Recently, AI-based technology was introduced for automated treatment planning [ [41] , [42] , [43] , [44] , [45] ], showing very promising clinical applications for several treatment sites and technical modalities. Mathematical algorithms for knowledge-based treatment planning and quality assurance with few manual interventions were applied [ [46] , [47] , [48] ] to predict achievable dose distributions for an individual patient’s anatomy and dose prescription based on a database of high-quality treatment plans.…”