Deep learning‐based tools to distinguish plan‐specific from generic deviations in EPID‐based in vivo dosimetry

Abstract

AbstractBackgroundDose distributions calculated with electronic portal imaging device (EPID)‐based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan‐specific deviations. Generic deviations are characteristic to a class of plans. Examples include limitations in EIVD dose reconstruction, inaccuracies in treatment planning system (TPS) calculations and systematic machine deviations. Plan‐specific deviations have an unpredictable character. Examples include discrepancies between the patient model used for dose calculation and the patient position or anatomy during delivery, random machine deviations, and data transfer, human or software errors. During the inspection work performed with traditional γ‐evaluation statistical methods: (i) generic deviations raise alerts that need to be inspected but that rarely lead to action as their root cause is usually understood and (ii) the detection of relevant plan‐specific deviations may be hindered by the presence of generic deviations.PurposeTo investigate whether deep learning‐based tools can help in identifying γ‐alerts raised by generic deviations and in improving the detectability of plan‐specific deviations.MethodsA 3D U‐Net was trained as an autoencoder to reconstruct underlying patterns of generic deviations in γ‐distributions. The network was trained for four treatment disease sites differently affected by generic deviations: volumetric modulated arc therapy (VMAT) lung (no known deviations), VMAT prostate (TPS inaccuracies), VMAT head‐and‐neck (EIVD limitations) and intensity modulated radiation therapy (IMRT) breast (large EIVD limitations). The network was trained with virtual non‐transit γ‐distributions: 60 train/10 validation for the VMAT sites and 30 train/10 validation for IMRT breast. It was hypothesized that in vivo γ‐distributions obtained in the presence of plan‐specific deviations would differ from those seen during training. For each disease site, the sensitivity of γ‐analysis and the network to detect (synthetically introduced) patient‐related deviations was compared by receiver operator characteristic analysis. The investigated deviations were patient positioning errors, weight gain or loss, and tumor volume changes. The clinical relevance was illustrated qualitatively with 793 in vivo clinical cases (141 lung, 136 head‐and‐neck, 209 prostate and 307 breast).ResultsError detectability of patient‐related deviations was better with the network than with γ‐analysis. The average area under the curve values over all sites were 0.86 ± 0.12(1SD) and 0.69 ± 0.25(1SD), respectively. Regarding in vivo clinical results, the percentage of cases differently classified by γ‐analysis and the network was 1%, 19%, 18% and 64% for lung, head‐and‐neck, prostate, and breast, respectively. In head‐and‐neck and breast cases, 45 γ‐only alerts were examined, of which 43 were attributed to EPID dose reconstruction limitations. For prostate, all 15 investigated γ‐only alerts were due to known TPS inaccuracies. All 59 investigated network alerts were explained by either patient‐related deviations or EPID acquisition incidents. Some patient‐related deviations detected by the network were not detected by γ‐analysis.ConclusionsDeep learning‐based tools trained to reconstruct underlying patterns of generic deviations in γ‐distributions can be used to (i) automatically identify false positives within the set of γ‐alerts and (ii) improve the detection of plan‐specific deviations, hence minimizing the likelihood of false negatives. The presented method provides clear additional value to the γ‐alert management process for large scale EIVD systems.