A mathematical framework for virtual IMRT QA using machine learning

Abstract

The Virtual QA process predicts IMRT passing rates with a high likelihood, allows the detection of failures due to setup errors, and it is sensitive enough to detect small differences between matched Linacs.

IMRT QA using machine learning: A multi‐institutional validation

“…Figure 4 shows that after approximately 80 data patients, no further improvement is obtained on average in the testing error. This number is roughly half the number obtained by Valdes et al20 confirming that if the data are more homogeneous, that is, for Institution 2, only data from one Linac were analyzed, a smaller number of data points are needed to obtain a stable model.…”

“…With our validation results, Virtual IMRT QA can predict gamma passing rates accurately within 3.5% as maximum empirical error observed. In addition, our previous work showed that 200 patient‐specific QA plans and results are required to train the virtual QA model to achieve an accurate prediction 20. The required training QA data should be readily available assuming a measurement‐based clinical patient‐specific QA program is in place, and should not impose additional measurement burden on the practicing physicist.…”

“…In this study, we have used the formalism as described by Valdes et al20 From eq. 9, however, it is clear that contributions from the different metrics were assumed to be linear (multiplication of a constant vector by the vector describing the plan characteristics), that is, no interaction terms between the different characteristics were considered.…”

“…Poisson regression with Lasso regularization was trained using the new dataset acquired at MSKCC to learn the relation between the plan characteristics and each passing rate. Other details can be found in the original publication on the development of the Virtual IMRT QA method 20. Figure 1 summarizes the entire workflow of the construction and validation of the predictive IMRT QA model.…”

“…This process is time consuming and not altogether instructive due to the myriad of sources that affect a passing result. In an earlier work, a machine learning algorithm, Virtual IMRT QA , was developed that can predict IMRT QA passing rates and identify underlying sources of errors not otherwise apparent 20. The algorithm identified the correlation between the IMRT plan complexity metrics and gamma passing rates and was validated on a single planning/delivery platform.…”