Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

Abstract

Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer

“…Similar to Wang et al [49], inconsistencies in standardized OAR prioritization affected the performance of our model. In line with time-savings noted by other groups for autoplanned nasopharynx [31,50,51] and oropharynx cancers [33,36], we confirmed time savings with AVI-planner. AVIplanner generated less complex plans (p<0.01) with fewer MU (p=0.03) compared to the clinically treated plans.…”

Dose prediction for cervical cancer VMAT patients with a full-scale 3D-cGAN-based model and the comparison of different input data on the prediction results

“…The relevant research is mainly carried out through knowledge-based projects (KBP) [11][12][13][14][15]. Some studies help physicists in planning designs by enumerating dose parameters and characteristics into the DVH model [16][17][18][19]. However, manual intervention is inevitable in these methods, on the other hand, the dose distribution of the undepicted tissue structure is ignored.…”

Development and Validation of Single-Optimization Knowledge-Based Volumetric Modulated Arc Therapy Model Plan in Nasopharyngeal Carcinomas

“…Hence, it can be a very helpful tool for improving the quality and efficiency of treatment planning in patients enrolled in clinical trials. 27 …”