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Artificial Intelligence in Adaptive Radiation Therapy
12.2.3 On-treatment QA
A patient undergoing treatment is monitored by physics, typically as weekly chart
review. Weekly chart checks involve monitoring of treatment records and documentation which are recorded in the oncology information system (OIS) [91]. The
literature is scant on the use of AI to assist in that scope of weekly chart review,
apart from the proposed software solutions [92, 93]. Although it is the physician’s
responsibility, the review of daily imaging and registration accuracy and learning the
dose implication of positioning error or anatomical changes is a task that can benefit
from AI. Simple models were developed to detect gross alignment error in vertebral
bodies [94, 95]. The same group implemented a CNN-based model that can detect
not only vertebral body misalignment [96] but also soft tissue changes [97].
In vivo dosimetry is another form of on-treatment monitoring. EPID-based in vivo
dosimetry, which captures exit radiation through a patient during treatment (transit
dose), has drawn much interest due to its convenience and high spatial resolution.
The EPID images can be compared to the fluence from the treatment plan in a
similar manner to pre-treatment PSQA. Wolfs et al built a hidden Markov model
[98] and CNN-based [99] prediction system that can detect anatomical or positional
change by analysing transit EPID images. Alternatively, EPID can be backprojected
into 3D dose in patient geometry, which enables direct comparison of target or OAR
dose against planned dose [100]. DL has been applied to enhance the accuracy of the
reconstructed dose for an MR-linac to correct for the effect of the magnetic field
[101]. The same group modeled the generic deviation in a reconstructed in vivo dose
using DL in order to increase the sensitivity of in vivo dosimetry in patient-related
sources of deviations [102](figure 12.8).
12.3 Treatment delivery systems and instruments
12.3.1 Machine commissioning
Zhao et al [103] introduced a machine-learning-based approach to model linac beam
data, streamlining the processes of linac commissioning and QA. The model, trained
with 43 Varian TrueBeam beam data, sets encompassing PDDs and profiles across
various energies and field sizes from different institutions. Figure 12.9 shows the
workflow for model building.
Using a 10 × 10 cm
developed for predicting beam specific PDDs and profiles for different field sizes.
The predictions for PDDs exhibited a mean absolute percent relative error (%RE)
ranging from 0.19% to 0.35% across various beam energies, with a maximum mean
absolute %RE of 0.93%. In profile prediction, the mean absolute %RE was within
the range of 0.66%–0.93%, and the maximum absolute %RE was 3.76%.
Figure 12.10 shows the comparison between the ground truth and the predicted
PDDs and profiles of 4 × 4 cm
potential in simplifying the linac commissioning procedure, offering time and
resource efficiency while enhancing the accuracy of the commissioning process. It
is particularly promising for its ability to address uncertainties, with the largest
2
field as the input, a multivariate regression model was
2
and 30 × 30 cm2fields. Notably, this method showed
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 12.8. Example of anatomical changes that can be detected by 3D-U-Net based in vivo dose
reconstruction prediction. Column (a): overlay between simulation and cone-beam CT demonstrating
anatomical changes. Column (b): dose agreement in a 3D gamma map between an in vivo reconstructed
dose and TPS. Column (c): U-Net reconstructed gamma map, highlighting patient-specific dose deviations.
Column (d): U-Net reconstructed gamma map factoring out patient-specific changes, showing only generic
(TPS or detector related) dose deviation. (Reproduced from [
Copyright 2023 American Association of Physicists in Medicine.)
102] with permission from John Wiley & Sons.
observed in the build-up region for PDD predictions and at the field penumbra for
profile predictions.
Liu et al [104] from the same research group refined the data acquisition process
for linac beam data through the application of implicit neural representation
(NeRP) learning, as illustrated in figure 12.11. This aimed to enhance the accuracy
of beam data collection verification and streamline the linac commissioning and QA
procedure.
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 12.9. Workflow for model training and prediction of PDD and profiles. (Reproduced with permission
from [
103]. Copyright 2020 Elsevier.)
The authors incorporated prior knowledge of beam data into a multilayer
perceptron network, achieved by learning the NeRP from a vendor-provided
‘golden’ beam dataset. This network underwent training to align with clinical
beam data collected at a specific field size. Subsequently, it demonstrated the
capability to predict beam data accurately for other field sizes. To assess prediction
accuracy, the authors compared the network-predicted beam data with measurements obtained from water tanks across 14 clinical linacs. They found that the linac
beam data predicted by the model exhibited strong agreement with water tank
measurements (averagely > 95% passing rates at 1%/1 mm criteria and < 0.6% mean
absolute errors). Figure 12.12 shows beam profile predictions against the ground
truth data. Moreover, the model unveiled instances of measurement errors by
identifying inconsistent beam predictions when trained with correct versus erroneous
data samples. These discrepancies were characterized by a GPR < 90%. It is
concluded that the model verifies beam data collection accuracy and holds promise
of simplifying commissioning and QA processes by minimizing the number of
required measurements without compromising the quality of medical physics
service.
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 12.10. Example of PDD (A)–(C) and profile (D)–(F) prediction of a 4 × 4 cm2field (yellow dots) and a
30 × 30 cm
(ground truth) are depicted as black lines. (Reproduced with permission from [
Figure 12.11. Workflow of NeRP learning for linac beam modeling and prediction. (Reproduced from [104]
with permission from John Wiley & Sons. Copyright 2023 American Association of Physicists in Medicine.)
2
field (blue dots) with 10 × 10 cm2field as input (red line). The measured PDDs and profiles
103]. Copyright 2020 Elsevier.)
Wagner et al [105] introduced a machine-learning approach to expedite the
modeling process for M6 CyberKnife integrated in Moderato. A machine-learning
algorithm was trained to find electron beam parameters for other M6 devices. The
algorithm simulated dose curves with varying spot size and energy, optimizing its
performance through cross-validation, and validating its accuracy with measurements from other institutions equipped with M6 CyberKnife devices. The agreement
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 12.12. Example beam profile predictions for 6 MV and 6FFF beam at 30 × 30 cm2field size. Enhanced
consistency in water tank measurements was achieved by fitting the prior-embedded network to sparse beam
data, indicated by green circles, rather than using direct ‘golden’ beam data. (Reproduced from [
permission from John Wiley & Sons. Copyright 2023 American Association of Physicists in Medicine.)
104] with
in the Monte Carlo model was achieved for a monoenergetic electron beam of 6.75
MeV with a Gaussian spatial distribution of 2.4 mm full-width-at-half-maximum
(FWHM). Clinical plan dose distributions from Moderato exhibited an agreement
within 2% with the TPS, and film measurements further corroborated the precision
of the model. During cross-validation of the prediction algorithm, minimal mean
absolute errors of 0.1 MeV and 0.3 mm were observed for beam energy and spot
size, respectively. The prediction agreements were within 3% with measurements,
except for one device where differences up to 6% were detected. This approach can
expedite the modeling of new machines within Monte Carlo systems, offering
efficiency and reliability in the intricate process of medical physics modeling.
12.3.2 Machine QA
Numerous investigations have explored the diverse applications of machine learning
in linac QA [106, 107]. These studies have delved into various avenues, encompassing models constructed from beam data commissioning, as detailed in the preceding
section, to models derived from delivery log files [108–110]. Additionally, machinelearning models have been built using proton fields [111, 112], addressing image
artifacts [113], and implementing automated QA through electronic portal imaging
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 12.13. Results from SPC using symmetry data from open field beam (left) and EDW data (right).
(Reproduced with permission from [
117]. Copyright 2023 IOP Publishing Ltd.)
device (EPID) images [114]. This multifaceted exploration underscores the versatility of machine learning across various aspects of linac QA, highlighting its
potential to enhance efficiency and accuracy in QA processes.
Chan et al [115] utilized five years of daily linac QA data for visual analysis and
correlation studies among dosimetric parameters and the researchers gained insights
into the intricacies of the data. Subsequently, they developed a convolutional neural
network (time-series modeling) to predict beam symmetry using the same daily QA
dataset [116]. The same research group further utilized the daily QA data, but to
include both open fields and enhanced dynamic wedge (EDW) measurements [117].
They employed statical process control and autoregressive integrated moving
average modeling to predict linac target failure. The EDW mechanism, characterized by nonuniform magni fication factors within its wedge-directed beam profiles,
played a pivotal role in their analysis. This nonuniformity introduced sensitivity to
changing beam properties induced by a degrading target. Figure 12.13 illustrates
two occurrences of target failures that can be effectively predicted from the daily
symmetry data. The comprehensive approach contributes insights into the prediction of linac performance.
12.3.3 Dosimetry tool QA
Chang et al [118] presented a deep-learning hierarchical neural network (HNN)
method to calibrate the EBT3 film with better calibration accuracy than the
conventional R-NOD method. They used the Keras functional application program
interface to build an HNN, with the inputs of net optical densities, pixel values, and
inverse transmittances to reveal the delivered dose and train the neural network with
deep learning. About the aging effect, the percentage error of the HNN method is
within 4% and proved to be unaffected, while the averaged percentage error of the
conventional R-NOD method is about 6.8% (figure 12.14). This new technique can
be improved by updating the new calibration data into the HNN training system
whenever physicists perform the recalibration. Based on collecting calibration data
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 12.14. Percentage differences between the calculated dose and the delivered dose for the verification test
of Lot A and Lot B films. (Reproduced from [
118]. CC BY 4.0.)
with the HNN method, physicists could require less calibration time and reduce film
usage.
Zhuang et al [119] described the development of an ANN approach for processing
EBT3 films from various batches without requiring specific calibration for each
batch. Utilizing PyTorch, researchers constructed a feed-forward ANN model that
transforms pixel values from scanned images across different batches into the
corresponding absorbed dose. To facilitate this, films exposed to x-ray doses from
different batches were scanned in transmission mode using an Epson 11000XL
scanner, serving both for model training and validation. The dose map generated by
the TPS was employed as the target output for the ANN model. To assess the
method’s effectiveness and its adaptability, a cross-validation study was conducted.
The ANN model, once trained, was used to convert scanned images into dose maps,
demonstrating a high level of consistency with the dose maps calculated by TPS. For
films exposed using the sliding window technique, the mean square errors (MSEs)
were below 16.0 cGy for the training batches and under 18 cGy for the testing
batches. In the case of patient IMRT films, the γ (3%, 3 mm) for comparison
between the dose maps from the ANN and TPS exceeded 97.5% for the training set
and 97.0% for the testing set. These results indicate the potential of this method to
accurately convert pixel values to absorbed doses for EBT3 films without needing
batch-specific calibrations, suggesting its applicability to other scenarios.
Avanzo et al [120] developed a machine-learning model to forecast skin dose from
targeted intraoperative (TARGIT) treatment, facilitating the timely implementation
of strategies to mitigate the risk of excessive skin dose. The process involved feature
selection of predictors of in vivo skin dose, followed by the training of various
machine-learning models using in vivo dosimetry results. The evaluation was
conducted through tenfold cross-validation, with rankings based on both roots
mean square error (RMSE) and adjusted correlation coefficient of true versus
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Artificial Intelligence in Adaptive Radiation Therapy
predicted values (adj-R2). The identified predictors strongly correlated with in vivo
dosimetry, including factors such as the distance of skin from source, depth-dose in
water at the depth of the applicator in the breast, utilization of a replacement source,
and irradiation time. Among the models, support vector regression (SVR) emerged
as the most effective, achieving an RMSE of 0.746 (95% confidence intervals, 0.737,
0.756) and an adj-R2 of 0.481 (95% CI 0.468, 0.494) during the tenfold crossvalidation. This SVR model, trained on in vivo dosimetry results, holds significant
practicality. The authors concluded that it can be employed to predict skin dose
during the patient set-up for TARGIT, enabling the timely adoption of strategies to
prevent excessive skin dose, thus enhancing the overall safety and efficacy of the
procedure.
12.4 Summary
Quality assurance plays a crucial role in ensuring the accuracy of radiotherapy
planning, delivery, and instrumentation within the clinical workflow. This critical
process places a substantial demand on clinical physicists’ resources. The integration
of AI into QA brings forth a twofold enhancement. First, AI serves to accentuate
components with heightened susceptibility to failure, enabling physicists to allocate
their attention more effectively towards those vulnerabilities. By pinpointing these
areas of concern, AI augments the specificity of QA process, facilitating a more
targeted and efficient utilization of resources. Second, AI possesses the capability to
discover intricate system errors that may elude human detection due to their
complexity. The analysis of various influencing factors is a forte of AI, enabling it
to discern anomalies that might otherwise remain hidden, reinforcing the overall
reliability of the QA process. In summary, the incorporation of AI into QA efforts
contributes to an elevated level of efficiency and standardization within the QA
program, advancing the precision, safety, and consistency of radiotherapy practices.
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