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Knowledge‐based automated planning for oropharyngeal cancer
Author(s) -
Babier Aaron,
Boutilier Justin J.,
McNiven Andrea L.,
Chan Timothy C.Y.
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12930
Subject(s) - pipeline (software) , radiation treatment planning , computer science , benchmarking , inverse , data mining , nuclear medicine , artificial intelligence , mathematics , radiation therapy , medicine , radiology , marketing , business , programming language , geometry
Purpose The purpose of this study was to automatically generate radiation therapy plans for oropharynx patients by combining knowledge‐based planning (KBP) predictions with an inverse optimization (IO) pipeline. Methods We developed two KBP approaches, the bagging query (BQ) method and the generalized principal component analysis‐based (gPCA) method, to predict achievable dose–volume histograms (DVHs). These approaches generalize existing methods by predicting physically feasible organ‐at‐risk (OAR) and target DVHs in sites with multiple targets. Using leave‐one‐out cross validation, we applied both models to a large dataset of 217 oropharynx patients. The predicted DVHs were input into an IO pipeline that generated treatment plans (BQ and gPCA plans) via an intermediate step that estimated objective function weights for an inverse planning model. The KBP predictions were compared to the clinical DVHs for benchmarking. To assess the complete pipeline, we compared the BQ and gPCA plans to both the predictions and clinical plans. To isolate the effect of the KBP predictions, we put clinical DVHs through the IO pipeline to produce clinical inverse optimized (CIO) plans. This approach also allowed us to estimate the complexity of the clinical plans. The BQ and gPCA plans were benchmarked against the CIO plans using DVH differences and clinical planning criteria. Iso‐complexity plans (relative to CIO) were also generated and evaluated. Results The BQ method tended to predict that less dose is delivered than what was observed in the clinical plans while the gPCA predictions were more similar to clinical DVHs. Both populations of KBP predictions were reproduced with inverse plans to within a median DVH difference of 3 Gy. Clinical planning criteria for OARs were satisfied most frequently by the BQ plans (74.4%), by 6.3% points more than the clinical plans. Meanwhile, target criteria were satisfied most frequently by the gPCA plans (90.2%), and by 21.2% points more than clinical plans. However, once the complexity of the plans was constrained to that of the CIO plans, the performance of the BQ plans degraded significantly. In contrast, the gPCA plans still satisfied more clinical criteria than both the clinical and CIO plans, with the most notable improvement being in target criteria. Conclusion Our automated pipeline can successfully use DVH predictions to generate high‐quality plans without human intervention. Between the two KBP methods, gPCA plans tend to achieve comparable performance as clinical plans, even when controlling for plan complexity, whereas BQ plans tended to underperform.