Several early investigations used the distribution of the morphological [2–4]or texture [5] features of the contours to detect a contouring error by finding significant

to contouring and dose accumulation accuracy and thus negatively impacts plan quality. Image registration QA is conducted mainly at two levels: (i) at the commissioning stage for the registration system and (ii) at a patient-specific level as part of radiotherapy workflow, when image fusion is required for target delineation or plan adaptation. Patient-specific evaluation of image registration is challenging due to a lack of ground truth [17], sources of uncertainties such as anatomical changes or image quality [18], and a lack of consensus on evaluation methods [19]. Consequently, QA practices vary between institutions [19, 20], many of which use qualitative visual inspection in a clinical setting [20]. Although visual inspection is an important element of the patient-specific registration QA, as recommended by TG-132 [15], it is not standardized and relies on the expertise level of a user [16]. Quantitative assessment can be done by identifying and calculating the displacement between homologous anatomical landmarks or con­tours, or examination of a deformation vector field (DVF) [16]. AI can help in the extraction of homologous structures, which would have been a labor-intensive process if done manually. For this purpose, in addition to the existing image transformation methods [21, 22], the use of machine learning such as the decision tree [23] or deep learning [24] has been investigated. AI can also play an important role in QA of deformable registration and can augment the DVF-based metrics that were already proposed as quantitative QA [25–27]. For example, Neylon et al [28] trained a supervised neural network model to calculate the registration error in the physical distance from the registered image and a biomechanical model. Similarly, CNN-based supervised models were used to predict registration accuracy from patches of images [29, 30]. Smolders et al [31] trained a deep-learning-based DVF uncertainty prediction model, which can be used for comparing different registration algorithms or estimating uncertainty in accumulated dose.

Use of dose prediction models for automatic plan quality QA comes with caveats: in order to achieve precise and accurate prediction and identify more suboptimal plans, it is important to train a model from the carefully selected high-quality plans with consistent OARs sparing [34]. Also, as pointed out in [34, 38], automatic QA does not always align with physicians ’ judgment, due to the subjective nature of plan quality review where physicians have diverse preferences over target coverage and OAR sparing.

Error detection in chart review can be seen as anomaly detection—identification of the samples that deviate from the rest of the data. A qualitative approach applies data exploration techniques, such as clustering and principal component analysis (PCA), to visually identify outliers. An example is the study by Azmandian et al [47] who used the data exploration techniques to detect a gross error in the beam parameters for prostate four-field box treatments (figure 12.4).

Several studies highlight the limitation of PSQA, especially in its reliance on GPR, due to lack of sensitivity [78, 79], correlation with dose to targets and OARs [80, 81], or independent audits [82]. One of the criticisms is the use of a fixed GPR threshold for pass/fail, based on empirical evidence that the GPR from PSQA does not follow the Gaussian distribution [70, 83] as assumed for the TG-119 recom­mendation [58]. AI can be used to overcome the traditional one-size-fits-all approach to better ‘explain’ the 2D gamma distribution or GPR, by unraveling complex relationships between PSQA results and various sources of uncertainties, such as calculation engines, delivery systems, and detectors. For example, CNN has been used to predict MLC errors from features derived from EPID images [84]ora3D detector array [

This neural network-based method was extended to detect other types of errors, such as phantom set-up errors [86], monitoring unit scaling errors [87], or MLC modeling in TPS [88].

Some investigators attempted to directly predict two-dimensional gamma dis­tributions by framing them as a deep-learning-based image synthesis problem. For example, Matsuura et al [89] used a generative adversarial network (GAN) to predict gamma distribution for EPID-based PSQA using the calculated fluence map as input (figure 12.7). Similarly, Mahdavi et al [90] created an artificial neural network (ANN)-based model to convert measured EPID fluences to the equivalent 2D gamma agreement between TPS and 2D array measurement.

There are considerations when training or evaluating the machine-learning-based PSQA models. First, the data (whether GPR or voxels with gamma > 1) the models are trained on are highly skewed towards passing. To address the class imbalance, a large sample size is needed for model training, or remedies such as oversampling (SMOTE) have to be taken. Second, the model should be interpretable so that root causes for poor dose agreement are fed back to the planning or machine QA process as actionable information.