Performance of Knowledge-Based Radiation Therapy Planning for the Glioblastoma Disease Site

Dosimetry and efficiency comparison of knowledge-based and manual planning using volumetric modulated arc therapy for craniospinal irradiation

BACKGROUND

Craniospinal irradiation (CSI) poses a challenge to treatment planning due to the large target, field junction, and multiple organs at risk (OARs) involved. The aim of this study was to evaluate the performance of knowledge-based planning (KBP) in CSI by comparing original manual plans (MP), KBP RapidPlan initial plans (RPI), and KBP RapidPlan final plans (RPF), which received further re-optimization to meet the dose constraints.

PATIENTS AND METHODS

Dose distributions in the target were evaluated in terms of coverage, mean dose, conformity index (CI), and homogeneity index (HI). The dosimetric results of OARs, planning time, and monitor unit (MU) were evaluated.

RESULTS

All MP and RPF plans met the plan goals, and 89.36% of RPI plans met the plan goals. The Wilcoxon tests showed comparable target coverage, CI, and HI for the MP and RPF groups; however, worst plan quality was demonstrated in the RPI plans than in MP and RPF. For the OARs, RPF and RPI groups had better dosimetric results than the MP group (P < 0.05 for optic nerves, eyes, parotid glands, and heart). The planning time was significantly reduced by the KBP from an average of 677.80 min in MP to 227.66 min (P < 0.05) and 307.76 min (P < 0.05) in RPI, and RPF, respectively. MU was not significantly different between these three groups.

CONCLUSIONS

The KBP can significantly reduce planning time in CSI. Manual re-optimization after the initial KBP is recommended to enhance the plan quality.

Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans

BACKGROUND

Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans.

MATERIAL AND METHODS

Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans.

RESULTS

The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients.

CONCLUSION

RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.

A novel automated planning approach for multi-anatomical sites cancer in Raystation treatment planning system

PURPOSE

To develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios.

METHODS

An automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared.

RESULTS

For target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA.

CONCLUSIONS

This study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.

Implementation of Machine Learning Models to Ensure Radiotherapy Quality for Multicenter Clinical Trials: Report from a Phase III Lung Cancer Study

The outcome of the patient and the success of clinical trials involving RT is dependent on the quality assurance of the RT plans. Knowledge-based Planning (KBP) models using data from a library of high-quality plans have been utilized in radiotherapy to guide treatment. In this study, we report on the use of these machine learning tools to guide the quality assurance of multicenter clinical trial plans. The data from 130 patients submitted to RTOG1308 were included in this study. Fifty patient cases were used to train separate photon and proton models on a commercially available platform based on principal component analysis. Models evaluated 80 patient cases. Statistical comparisons were made between the KBP plans and the original plans submitted for quality evaluation. Both photon and proton KBP plans demonstrate a statistically significant improvement of quality in terms of organ-at-risk (OAR) sparing. Proton KBP plans, a relatively emerging technique, show more improvements compared with photon plans. The KBP proton model is a useful tool for creating proton plans that adhere to protocol requirements. The KBP tool was also shown to be a useful tool for evaluating the quality of RT plans in the multicenter clinical trial setting.

A practical method to improve the performance of knowledge-based VMAT planning for endometrial and cervical cancer

PURPOSE

The aim of this work was to demonstrate a practical and effective method to improve the performance of RapidPlan (RP) model.

METHODS

203 consecutive clinical VMAT plans (P₀) for cervical and endometrial cancer were used to train an RP model (M₀). The plans were then reoptimized by M₀ to generate 203 new plans (P₁). Compared with P₀, 150 plans with a lower mean dose (MD) of bladder, rectum and PBM were selected from P₁ to configure a new RP model (M₁). A final RP model (M₂) was trained using plans in M₁ and the remaining 53 plans from P₁ (excluding OARs with worse MD) and the corresponding plans from P₀ (only including OARs with better MD). The models were validated on the mentioned 53 plans (closed-loop set) and 46 patient cohorts outside the training library (open-loop set). p < 0.05 was considered statistically significant.

RESULTS

For closed-loop validation, the difference of D_2%, D_98% and CI_95% between groups was of no statistical significance, the homogeneity index (HI) was lower in the groups of RP models (p < 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M₀, M₁ and M₂, except the MD of bowel in M₁ and MD of LFH in M₂. Similarly, for open-loop validation, there was no significant difference in D_2%, D_98% and HI between groups, but CI_95% was larger in the clinical group (p < 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M₀, M₁ and M₂, with the exception of bowel in M₁.

CONCLUSION

The practical method of incorporating plan data of better-sparing OARs from both the clinical VMAT plans and the re-optimized plans could further improve the performance of the RP model.

RapidPlan hippocampal sparing whole brain model version 2-how far can we reduce the dose?

Whole-brain radiotherapy has been the standard palliative treatment for patients with brain metastases due to its effectiveness, availability, and ease of administration. Recent clinical trials have shown that limiting radiation dose to the hippocampus is associated with decreased cognitive toxicity. In this study, we updated an existing Knowledge Based Planning model to further reduce dose to the hippocampus and improve other dosimetric plan quality characteristics. Forty-two clinical cases were contoured according to guidelines. A new dosimetric scorecard was created as an objective measure for plan quality. The new Hippocampal Sparing Whole Brain Version 2 (HSWBv2) model adopted a complex recursive training process and was validated with five additional cases. HSWBv2 treatment plans were generated on the Varian Halcyon^TM and TrueBeam^TM systems and compared against plans generated from the existing (HSWBv1) model released in 2016. On the Halcyon^TM platform, 42 cases were re-planned. Hippocampal D_100% from HSWBv2 and HSWBv1 models had an average dose of 5.75 Gy and 6.46 Gy, respectively (p < 0.001). HSWBv2 model also achieved a hippocampal D_mean of 7.49 Gy, vs 8.10 Gy in HSWBv1 model (p < 0.001). Hippocampal D_0.03CC from HSWBv2 model was 9.86 Gy, in contrast to 10.57 Gy in HSWBv1 (p < 0.001). For PTV_3000, D_98% and D_2% from HSWBv2 model were 28.27 Gy and 31.81 Gy, respectively, compared to 28.08 Gy (p = 0.020) and 32.66 Gy from HSWBv1 (p < 0.001). Among several other dosimetric quality improvements, there was a significant reduction in PTV_3000 V_105% from 35.35% (HSWBv1) to 6.44% (HSWBv2) (p < 0.001). On 5 additional validation cases, dosimetric improvements were also observed on TrueBeam^TM. In comparison to published data, the HSWBv2 model achieved higher quality hippocampal avoidance whole brain radiation therapy treatment plans through further reductions in hippocampal dose while improving target coverage and dose conformity/homogeneity. HSWBv2 model is shared publicly.

Toward automatic beam angle selection for pencil-beam scanning proton liver Treatments: A deep learning-based approach

BACKGROUND

Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning however poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods.

PURPOSE

A deep learning-based approach to automatic beam angle selection is proposed for proton pencil-beam scanning treatment planning of liver lesions.

METHODS

We cast beam-angle selection as a multi-label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance based regularization and multi-label circular-smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners' clinical practice is employed in post-processing to improve the output of the model. Forty-nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI-selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge-based treatment planning.

RESULTS

For 7 of the 11 cases in the test set, AI-selected beam angles agreed with those chosen by human planners to within 20 degrees (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10 degrees of the human-selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI- and human-selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ-at-risk sparing.

CONCLUSIONS

This pilot study demonstrated the feasibility of a novel deep learning-based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human-selected beam angles. In tandem with auto-contouring and knowledge-based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy. This article is protected by copyright. All rights reserved.

Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer

This study aimed to investigate the feasibility of using a knowledge‐based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose–volume histogram (DVH) prediction models using a commercial knowledge‐based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose–volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper–lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re‐generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge‐based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically‐generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic.

The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer

Purpose

To develop a 3D‐Unet dose prediction model to predict the three‐dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization.

Methods

One hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D‐Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (D_mean), the received dose of 2 cm³ (D_2cc), the percentage volume of receiving 40 Gy dose of organs‐at‐risk (V₄₀), planning target volume (PTV) D_98%, and homogeneity index (HI), (d) dose–volume histograms (DVHs).

Results

The model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference.

Conclusion

A 3D‐Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.

Automatic Radiotherapy Planning for Glioblastoma Radiotherapy With Sparing of the Hippocampus and nTMS-Defined Motor Cortex

Background

Navigated transcranial magnetic stimulation (nTMS) of the motor cortex has been successfully implemented into radiotherapy planning by a number of studies. Furthermore, the hippocampus has been identified as a radiation-sensitive structure meriting particular sparing in radiotherapy. This study assesses the joint protection of these two eloquent brain regions for the treatment of glioblastoma (GBM), with particular emphasis on the use of automatic planning.

Patients and Methods

Patients with motor-eloquent brain glioblastoma who underwent surgical resection after nTMS mapping of the motor cortex and adjuvant radiotherapy were retrospectively evaluated. The radiotherapy treatment plans were retrieved, and the nTMS-defined motor cortex and hippocampus contours were added. Four additional treatment plans were created for each patient: two manual plans aimed to reduce the dose to the motor cortex and hippocampus by manual inverse planning. The second pair of re-optimized plans was created by the Auto-Planning algorithm. The optimized plans were compared with the “Original” plan regarding plan quality, planning target volume (PTV) coverage, and sparing of organs at risk (OAR).

Results

A total of 50 plans were analyzed. All plans were clinically acceptable with no differences in the PTV coverage and plan quality metrics. The OARs were preserved in all plans; however, overall the sparing was significantly improved by Auto-Planning. Motor cortex protection was feasible and significant, amounting to a reduction in the mean dose by &gt;6 Gy. The dose to the motor cortex outside the PTV was reduced by &gt;12 Gy (mean dose) and &gt;5 Gy (maximum dose). The hippocampi were significantly improved (reduction in mean dose: ipsilateral &gt;6 Gy, contralateral &gt;4.6 Gy; reduction in maximum dose: ipsilateral &gt;5 Gy, contralateral &gt;5 Gy). While the dose reduction using Auto-Planning was generally better than by manual optimization, the radiated total monitor units were significantly increased.

Conclusion

Considerable dose sparing of the nTMS-motor cortex and hippocampus could be achieved with no disadvantages in plan quality. Auto-Planning could further contribute to better protection of OAR. Whether the improved dosimetric protection of functional areas can translate into improved quality of life and motor or cognitive performance of the patients can only be decided by future studies.

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

Automatic VMAT technique to treat glioblastoma: A two years' experience

PURPOSE

The present work aims to guide the physicist in order to start automated planning for the VMAT treatment of glioblastoma multiforme (GBM) by giving a recipe that was set up and tested during a long-term (two years) evaluation.

METHODS

An automatic technique in AutoPlanning module of the Pinnacle3 (Philips Medical Systems, Fitchburg, WI) treatment planning system was created and validated by comparing dose distributions of automatic plans (APs) and manual plans (MPs) and by performing a blind AP-MP comparison on a cohort of 20 patients. Automatic technique was then applied to 145 patients and failures were recorded i.e. the number of times for which dose distributions produced by the automatic module were not suitable for treatment.

RESULTS

Each of the 20 APs considered in the validation step was clinically acceptable and proved to be better (15 cases) or equal (5 cases) respect to MPs. A statistically significant improvement in brain stem, optic pathways, cochleae, pituitary gland and scalp sparing was observed for APs, while no statistically significant differences were recorded in target coverage or plan parameters. For only 5 cases out of the 145 plans the operator intervention was needed in order to obtain a clinical acceptable plan, while for the remaining 140 plans the automatic created solution was suitable.

CONCLUSIONS

A straightforward automatic procedure has been created and tested in our clinic. The AutoPlanning technique proposed represents a reliable tool to improve treatment planning efficiency and the recipe, here presented, could be simply imported to every radiotherapy center.

Knowledge-based radiation treatment planning: A data-driven method survey

This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance

PURPOSE

Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines.

METHODS AND MATERIALS

Using clinical knowledge-based automated planning routines for prostate, prostatic fossa, head and neck, and hypofractionated lung disease sites in a commercial KBP solution, workflow efficiency was compared in terms of planning time in a pre-KBP (n = 145 plans) and post-KBP (n = 503) patient cohort. Post-KBP, planning was initialized with KBP (KBP-only) and subsequently manually refined (KBP + human). Differences in planning time were tested for significance using a 2-tailed Mann-Whitney U test (P < .05, null hypothesis: planning time unchanged). Post-refinement plan quality was assessed using site-specific dosimetric parameters of the original KBP-only plan versus KBP + human; 2-tailed paired t test quantified statistical significance (Bonferroni-corrected P < .05, null hypothesis: no dosimetric difference after refinement). If KBP + human significantly improved plans across the cohort, optimization objectives were changed to create an updated KBP routine (KBP'). Patients were replanned with KBP' and plan quality was compared with KBP + human as described previously.

RESULTS

KBP significantly reduced planning time in all disease sites: prostate (median: 7.6 hrs → 2.1 hrs; P < .001), prostatic fossa (11.1 hrs → 3.7 hrs; P = .001), lung (9.9 hrs → 2.0 hrs; P < .001), and head and neck (12.9 hrs → 3.5 hrs; P <.001). In prostate, prostatic fossa, and lung disease sites, organ-at-risk dose changes in KBP + human versus KBP-only were minimal (<1% prescription dose). In head and neck, KBP + human did achieve clinically relevant dose reductions in some parameters. The head and neck routine was updated (KBP'_HN) to incorporate dose improvements from manual refinement. The only significant dosimetric differences to KBP + human after replanning with KBP'_HN were in favor of the new routine.

CONCLUSIONS

KBP increased clinical efficiency by significantly reducing planning time. On average, human refinement offered minimal dose improvements over KBP-only plans. In the single disease site where KBP + human was superior to KBP-only, differences were eliminated by adjusting optimization parameters in a revised KBP routine.

Dosimetric Quality of Online Adapted Pancreatic Cancer Treatment Plans on an MRI-Guided Radiation Therapy System

Purpose

Stereotactic magnetic resonance image–guided adaptive radiation therapy (SMART) is an emerging technique that shows promise in the treatment of pancreatic cancer and other abdominopelvic malignancies. However, it is unknown whether the time-limited nature of on-table adaptive planning may result in dosimetrically suboptimal plans. The purpose of this study was to quantitatively address that question through systemic retrospective replanning of treated on-table adaptive pancreatic cancer cases.

Methods and Materials

Of 74 consecutive adapted fractions, 30 were retrospectively replanned based on deficiencies in planning target volume (PTV) and gross tumor volume (GTV) coverage or doses to organs-at-risk (OARs) that exceeded ideal constraints. Retrospective plans were created by adjusting dose-volume objectives in an iterative fashion until deemed optimized. The goal of replanning was to improve PTV/GTV coverage while keeping the dose to gastrointestinal OARs the same or lower or to reduce OAR doses while keeping PTV coverage the same or higher. The global maximum dose was required to be maintained within 2% of that of the treated adaptive plan to eliminate it as a confounding factor. A threshold of 5% improvement in PTV coverage or 5% decrease in OAR dose was used to define a clinically significant improvement.

Results

Of the 30 replans, 7 obtained at least 5% PTV coverage improvement. The average increase in PTV coverage for these plans was 11%. No plans were clinically significantly improved in terms of OAR sparing. Changes in beam-on time did not show any correlation. Statistical analysis via a linear mixed-effects model with a nested random effect suggested that both GTV and PTV coverage were improved over SMART process plans by 0.91 cc (P = .02) and 2.03 cc (P < .001), respectively.

Conclusions

Dosimetric plan quality of at least 10% of SMART fractions may be improved through more extensive replanning than is currently performed on-table. Further work is needed to develop an automated replanning workflow to streamline the in-depth replanning process to better fit into an on-table adaptive workflow.

An Artificial Intelligence-Based Full-Process Solution for Radiotherapy: A Proof of Concept Study on Rectal Cancer

BACKGROUND AND PURPOSE

To develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy.

MATERIALS AND METHODS

A full-process solution that integrates autosegmentation and automatic treatment planning was developed under a single deep-learning framework. A convolutional neural network (CNN) was used to generate segmentations of the target and the organs at risk (OAR) as well as dose distribution. A script in Pinnacle that simulates the treatment planning process was used to execute plan optimization. A total of 172 rectal cancer patients were used for model training, and 18 patients were used for model validation. Another 40 rectal cancer patients were used for an end-to-end evaluation for both autosegmentation and treatment planning. The PTV and OAR segmentation was compared with manual segmentation. The planning results was evaluated by both objective and subjective assessment.

RESULTS

The total time for full-process planning without contour modification was 7 min, and an additional 15 min may require for contour modification and re-optimization. The PTV DICE similarity coefficient was greater than 0.85 for all 40 patients in the evaluation dataset while the DICE indices of the OARs also indicated good performance. There were no significant differences between the auto plans and manual plans. The physician accepted 80% of the auto plans without any further operation.

CONCLUSION

We developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.

Clinical iterative model development improves knowledge-based plan quality for high-risk prostate cancer with four integrated dose levels

BACKGROUND

Manual volumetric modulated arc therapy (VMAT) treatment planning for high-risk prostate cancer receiving whole pelvic radiotherapy (WPRT) with four integrated dose levels is complex and time consuming. We have investigated if the radiotherapy planning process and plan quality can be improved using a well-tuned model developed through a commercial system for knowledge-based planning (KBP).

MATERIAL AND METHODS

Treatment plans from 69 patients treated for high-risk prostate cancer with manually planned VMAT were used to develop an initial KBP model (RapidPlan, RP). Prescribed doses were 50, 60, 67.5, and 72.5 Gy in 25 fractions to the pelvic lymph nodes, prostate and seminal vesicles, prostate gland, and prostate tumour(s), respectively. This RP model was in clinical use from July 2019 to February 2020, producing another set of 69 clinically delivered treatment plans for a new patient group, which were used to develop a second RP model. Both models were validated on an independent group of 40 patients. Plan quality was compared by D _98% and the Paddick conformity index for targets, mean dose (D _mean) and generalised equivalent uniform dose (gEUD) for bladder, bowel bag and rectum, and number of monitor units (MU).

RESULTS

Target coverage and conformity was similar between manually created and RP treatment plans. Compared to the manually created treatment plans, the final RP model reduced average D _mean and gEUD with 2.7 Gy and 1.3 Gy for bladder, 1.2 Gy and 0.9 Gy for bowel bag, and 2.7 Gy and 0.8 Gy for rectum, respectively (p < .05). For rectum, the interpatient variation (i.e., 95% confidence interval) of DVHs was reduced by 23%.

CONCLUSION

KBP improved plan quality and consistency among treatment plans for high-risk prostate cancer. Model tuning using KBP-based clinical plans further improved model outcome.

Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions

PURPOSE

To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions.

METHODS AND MATERIALS

Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures.

RESULTS

All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average.

CONCLUSIONS

All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.

Rolling out RapidPlan: What we've learnt

INTRODUCTION

RapidPlan (RP), a knowledge-based planning system, aims to consistently improve plan quality and efficiency in radiotherapy. During the early stages of implementation, some of the challenges include knowing how to optimally train a model and how to integrate RP into a department. We discuss our experience with the implementation of RP into our institution.

METHODS

We reviewed all patients planned using RP over a 7-month period following inception in our department. Our primary outcome was clinically acceptable plans (used for treatment) with secondary outcomes including model performance and a comparison of efficiency and plan quality between RP and manual planning (MP).

RESULTS

Between November 2017 and May 2018, 496 patients were simulated, of which 217 (43.8%) had an available model. RP successfully created a clinically acceptable plan in 87.2% of eligible patients. The individual success of the 24 models ranged from 50% to 100%, with more than 90% success in 15 (62.5%) of the models. In 40% of plans, success was achieved on the 1st optimisation. The overall planning time with RP was reduced by up to 95% compared with MP times. The quality of the RP plans was at least equivalent to historical MP plans in terms of target coverage and organ at risk constraints.

CONCLUSION

While initially time-consuming and resource-intensive to implement, plans optimised with RP demonstrate clinically acceptable plan quality, while significantly improving the efficiency of a department, suggesting RP and its application is a highly effective tool in clinical practice.

Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy

PURPOSE

To implement knowledge-based (KB) automatic planning for helical TomoTherapy (HTT). The focus of the first clinical implementation was the case of high-risk prostate cancer, including pelvic node irradiation.

METHODS AND MATERIALS

One hundred two HTT clinical plans were selected to train a KB model using the RapidPlan tool incorporated in the Eclipse system (v13.6, Varian Inc). The individually optimized KB-based templates were converted into HTT-like templates and sent automatically to the HTT treatment planning system through scripting. The full dose calculation was set after 300 iterations without any additional planner intervention. Internal (20 patients in the training cohort) and external (28 new patients) validation were performed to assess the performance of the model: Automatic HTT plans (KB-TP) were compared against the original plans (TP) in terms of organs at risk and planning target volume (PTV) dose-volume parameters and by blinded clinical evaluation of 3 expert clinicians.

RESULTS

KB-TP plans were generally better than or equivalent to TP plans in both validation cohorts. A significant improvement in PTVs and rectum-PTV overlap dosimetry parameters were observed for both sets. Organ-at-risk sparing for KB-TP was slightly improved, which was more evident in the external validation group and for bladder and bowel. Clinical evaluation reported KB-TP to be better in 60% of cases and worse in 10% compared with TP (P < .05).

CONCLUSIONS

The fully KB-based automatic planning workflow was successfully implemented for HTT planning optimization in the case of high-risk patients with prostate cancer.

Validation of the clinical applicability of knowledge-based planning models in single-isocenter volumetric-modulated arc therapy for multiple brain metastases

PURPOSE

To validate the clinical applicability of knowledge-based (KB) planning in single-isocenter volumetric-modulated arc therapy (VMAT) for multiple brain metastases using the k-fold cross-validation (CV) method.

METHODS

This study comprised 60 consecutive patients with multiple brain metastases treated with single-isocenter VMAT (28 Gy in five fractions). The patients were divided randomly into five groups (Groups 1-5). The data of Groups 1-4 were used as the training and validation dataset and those of Group 5 were used as the testing dataset. Four KB models were created from three of the training and validation datasets and then applied to the remaining Groups as the fourfold CV phase. As the testing phase, the final KB model was applied to Group 5 and the dose distributions were calculated with a single optimization process. The dose-volume indices (DVIs), modified Ian Paddick Conformity Index (mIPCI), modulation complexity scores for VMAT plans (MCSv), and the total number of monitor units (MUs) of the final KB plan were compared to those of the clinical plan (CL) using a paired Wilcoxon signed-rank test.

RESULTS

In the fourfold CV phase, no significant differences were observed in the DVIs among the four KB plans (KBPs). In the testing phase, the final KB plan was statistically equivalent to the CL, except for planning target volumes (PTVs) D_2% and D_50% . The differences between the CL and KBP in terms of the PTV D_99.5% , normal brain, and D_max to all organs at risk (OARs) were not significant. The KBP achieved a lower total number of MUs and higher MCSv than the CL with no significant difference.

CONCLUSIONS

We demonstrated that a KB model in a single-isocenter VMAT for multiple brain metastases was equivalent in dose distribution, MCSv, and total number of MUs to a CL with a single optimization.

Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer

PURPOSE

To implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer.

MATERIAL AND METHODS

The protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV₁), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTV_ART) and to PTV₁ respectively. PTV_ART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA).

RESULTS

The resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V_30Gy and V_40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel.

CONCLUSIONS

The KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique

PURPOSE

Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided by an established in-house KBP system. The purpose of this work was to assess dosimetric, mechanical, and delivery properties of plans designed with a common KBP method for prostate cases treated via volumetric modulated arc therapy (VMAT).

METHODS

Thirty-one prostate patients previously treated with VMAT were replanned with an in-house KBP method based on the overlap volume histogram. VMAT plan complexities of the KBP plans and the reference clinical plans were quantified via monitor units, modulation complexity scores, the edge metric, and average leaf motion per degree of gantry rotation. Each set of plans was delivered to the same diode array and agreement between computed and measured dose distributions was evaluated using the gamma index. Varying percent dose-difference (1-3%) and distance-to-agreement (1 mm to 3 mm) thresholds were assessed for gamma analyses.

RESULTS

Knowledge-based planning (KBP) plans achieved average reductions of 6.4 Gy (P < 0.001) and 8.2 Gy (P < 0.001) in mean bladder and rectum dose compared to reference plans, while maintaining clinically acceptable target dose. However, KBP plans were significantly more complex than reference plans in each evaluated metric (P < 0.001). KBP plans also showed significant reductions (P < 0.05) in gamma passing rates at each evaluated criterion compared to reference plans.

CONCLUSIONS

While KBP plans had significantly reduced bladder and rectum dose, they were significantly more complex and had significantly worse quality assurance outcomes than reference plans. These results suggest caution should be taken when implementing an in-house KBP technique.

Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches

Purpose

Intensity‐Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results.

Methods

In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here.

Results

The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation.

Conclusions

The number of KBP‐related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi‐institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology.

Real-time interactive planning for radiotherapy of head and neck cancer with volumetric modulated arc therapy

Background and purpose

Planning complex radiotherapy treatments can be inefficient, with large variation in plan quality. In this study we evaluated plan quality and planning efficiency using real-time interactive planning (RTIP) for head and neck (HN) volumetric modulated arc therapy (VMAT).

Materials and methods

RTIP allows manipulation of dose volume histograms (DVHs) in real-time to assess achievable planning target volume (PTV) coverage and organ at risk (OAR) sparing. For 20 HN patients previously treated with VMAT, RTIP was used to minimize OAR dose while maintaining PTV coverage. RTIP DVHs were used to guide VMAT optimization. Dosimetric differences between RTIP-assisted plans and original clinical plans were assessed. Five blinded radiation oncologists indicated their preference for each PTV, OAR and overall plan. To assess efficiency, ten patients were planned de novo by experienced and novice planners and a RTIP user.

Results

The average planning time with RTIP was <20 min, and most plans required only one optimization. All 20 RTIP plans were preferred by a majority of oncologists due to improvements in OAR sparing. The average maximum dose to the spinal cord was reduced by 10.5 Gy (from 49.5 to 39.0 Gy), and the average mean doses for the oral cavity, laryngopharynx, contralateral parotid and submandibular glands were reduced by 3.5 Gy (39.1-35.7 Gy), 6.8 Gy (42.5-35.7 Gy), 1.7 Gy (17.0-15.3 Gy) and 3.3 Gy (22.9-19.5 Gy), respectively.

Conclusions

Incorporating RTIP into clinical workflows may increase both planning efficiency and OAR sparing.

Automated Closed- and Open-Loop Validation of Knowledge-Based Planning Routines Across Multiple Disease Sites

PURPOSE

Knowledge-based planning (KBP) clinical implementation necessitates significant upfront effort, even within a single disease site. The purpose of this study was to demonstrate an efficient method for clinicians to assess the noninferiority of KBP across multiple disease sites and estimate any systematic dosimetric differences after implementation. We sought to establish these endpoints in a plurality of previously treated patients (validation set) with both closed-loop (training set overlapping validation set) and open-loop (independent training set) KBP routines.

METHODS AND MATERIALS

We identified 53 prostate, 24 prostatic fossa, 54 hypofractionated lung, and 52 head and neck patients treated with volumetric modulated arc therapy in the year directly preceding our clinic's broad adoption of RapidPlan (Varian Medical Systems, Palo Alto, CA). Using the Varian Eclipse Scripting API, our program takes as input a list of patients, then performs semiautomated structure matching, fully automated RapidPlan-driven optimization, and plan comparison. All plans were normalized to the planning target volume (PTV) D_95% = 100%. Dose metric differences (ΔD_x = D_x,clinical - D_x,KBP) were computed for standard PTV and organ-at-risk (OAR) dose-volume histogram parameters across disease sites. A 2-tailed paired t test quantified statistical significance (P < .001).

RESULTS

Statistically significant organ dose-volume histogram improvements were observed in the KBP cohort: the rectum, bladder, and penile bulb in prostate/prostatic fossa; and the larynx, esophagus, cricopharyngeus, parotid glands, and cochlea in head and neck. No OAR dose metric was statistically worse in any KBP sample. PTV ΔD_1% increases in prostatic fossa were deemed acceptable given organ-sparing gains. PTV ΔD_1% and internal target volume ΔD_99% increase for the lung was by design owing to the prescription normalization variance in the pre-KBP lung sample.

CONCLUSIONS

Our automated method showed multiple disease sites' KBP routines to be noninferior to manual planning, with statistically significant superiority in some aspects of OAR sparing. This method is applicable to any institution implementing either closed-loop or open-loop KBP autoplanning routines.

Intensity Modulated Radiation Therapy Versus Volumetric Arc Radiation Therapy in the Treatment of Glioblastoma-Does Clinical Benefit Follow Dosimetric Advantage?

Purpose

Volumetric modulated arc therapy (VMAT) has been shown by multiple planning studies to hold dosimetric advantages over intensity modulated radiation therapy (IMRT) in the management of brain tumors, including glioblastoma (GBM). Although promising, the clinical impact of these findings has not been fully elucidated.

Methods and Materials

We retrospectively reviewed consecutive patients with a pathologic-confirmed diagnosis of GBM who were treated between 2014 and 2015, a period that encompassed the transition from IMRT to VMAT at a single institution. After surgery, radiation with VMAT consisted of 2 to 3 coplanar arcs with or without an additional noncoplanar arc or IMRT with 5 to 6 gantry angles with concurrent and adjuvant temozolomide. Actuarial analyses were performed using the Kaplan Meier method.

Results

A total of 88 patients treated with IMRT (n = 45) and VMAT (n = 43) were identified. Patients were similar in terms of age, sex, performance status, extent of resection, and the high dose target volume. At a median follow-up time of 27 months (range, .7-32.3 months), the overall survival, freedom from progression, and freedom from new or worsening toxicity rates were not different between the 2 treatment groups (log-rank: P = .33; .87; and .23, respectively). There was no difference in incidences of alopecia, erythema, nausea, worsening or new onset fatigue, or headache during radiation, or temozolomide dose reduction for thrombocytopenia or neutropenia (all P > .05). Patterns of failure were different with more out of field failures in the IMRT group (P = .02). The mean time of treatment (TOT) was significantly reduced by 29% (P < .01) with VMAT (mean TOT: 10.3 minutes) compared with IMRT (mean TOT: 14.6 minutes).

Conclusions

For GBM, treatment with VMAT results in similar oncologic and toxicity outcomes compared with IMRT and may improve resource utilization by reducing TOT. VMAT should be considered a potential radiation modality for patients with GBM.

Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations

Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.

An interactive plan and model evolution method for knowledge-based pelvic VMAT planning

Purpose

To test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed‐loop evolution process.

Methods and materials

Eighty‐one manual plans (P₀) that were used to configure an initial rectal RapidPlan model (M₀) were reoptimized using M₀ (closed‐loop), yielding 81 P₁ plans. The 75 improved P₁ (P₁₊) and the remaining 6 P₀ were used to configure model M₁. The 81 training plans were reoptimized again using M₁, producing 23 P₂ plans that were superior to both their P₀ and P₁ forms (P₂₊). Hence, the knowledge base of model M₂ composed of 6 P₀, 52 P₁₊, and 23 P₂₊. Models were tested dosimetrically on 30 VMAT validation cases (P_v) that were not used for training, yielding P_v(M₀), P_v(M₁), and P_v(M₂) respectively. The 30 P_v were also optimized by M_{2_new} as trained by the library of M₂ and 30 P_v(M₀).

Results

Based on comparable target dose coverage, the first closed‐loop reoptimization significantly (P < 0.01) reduced the 81 training plans’ mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly (P < 0.01) in the second closed‐loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open‐loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M₁ (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M₂ (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M₀. However, mean dose to femoral head increased by 0.81 Gy/6.64% (M₁) and 0.91 Gy/7.46% (M₂) than using M₀. The overfitting problem was relieved by applying model M_{2_new}.

Conclusions

The RapidPlan model and its constituent plans can improve each other interactively through a closed‐loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Functional-guided radiotherapy using knowledge-based planning

BACKGROUND AND PURPOSE

There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring consistent plan quality across multiple planners. Knowledge-based planning (KBP) is positioned to address both concerns.

MATERIAL AND METHODS

A KBP model was created from 30 previously planned functional-guided lung patients. Standard organs at risk (OAR) in lung radiotherapy and a ventilation contour delineating areas of high ventilation were included. Model validation compared dose-metrics to standard OARs and functional dose-metrics from 20 independent cases that were planned with and without KBP.

RESULTS

A significant improvement was observed for KBP optimized plans in V20Gy and mean dose to functional lung (p = 0.005 and 0.001, respectively), V20Gy and mean dose to total lung minus GTV (p = 0.002 and 0.01, respectively), and mean doses to esophagus (p = 0.005).

CONCLUSION

The current work developed a KBP model for functional-guided radiotherapy. Modest, but statistically significant, improvements were observed in functional lung and total lung doses.

Cerebral cortex dose sparing for glioblastoma patients: IMRT versus robust treatment planning

BACKGROUND

To date, patients with glioblastoma still have a bad median overall survival rate despite radiation dose-escalation and combined modality treatment. Neurocognitive decline is a crucial adverse event which may be linked to high doses to the cortex. In a planning study, we investigated the impact of dose constraints to the cerebral cortex and its relation to the organs at risk for glioblastoma patients.

METHODS

Cortical sparing was implemented into the optimization process for two planning approaches: classical intensity-modulated radiotherapy (IMRT) and robust treatment planning. The plans with and without objectives for cortex sparing where compared based on dose-volume histograms (DVH) data of the main organs at risk. Additionally the cortex volume above a critical threshold of 28.6 Gy was elaborated. Furthermore, IMRT plans were compared with robust treatment plans regarding potential cortex sparing.

RESULTS

Cortical dose constraints result in a statistically significant reduced cerebral cortex volume above 28.6 Gy without negative effects to the surrounding organs at risk independently of the optimization technique. For IMRT we found a mean volume reduction of doses beyond the threshold of 19%, and 16% for robust treatment planning, respectively. Robust plans delivered sharper dose gradients around the target volume in an order of 3 - 6%. Aside from that the integration of cortical sparing into the optimization process has the potential to reduce the dose around the target volume (4 - 8%).

CONCLUSIONS

We were able to show that dose to the cerebral cortex can be significantly reduced both with robust treatment planning and IMRT while maintaining clinically adequate target coverage and without corrupting any organ at risk. Robust treatment plans delivered more conformal plans compared to IMRT and were superior in regards to cortical sparing.