Knowledge-based Planning Model Research Articles

Knowledge-based versus deep learning based treatment planning for breast radiotherapy

Background and PurposeTo improve radiotherapy (RT) planning efficiency and plan quality, knowledge-based planning (KBP) and deep learning (DL) solutions have been developed. We aimed to make a direct comparison of these models for breast cancer planning using the same training, validation, and testing sets. Materials and MethodsTwo KBP models were trained and validated with 90 RT plans for left-sided breast cancer with 15 fractions of 2.6 Gy. The versions either used the full dataset (non-clean model) or a cleaned dataset (clean model), thus eliminating geometric and dosimetric outliers. Results were compared with a DL U-net model (previously trained and validated with the same 90 RT plans) and manually produced RT plans, for the same independent dataset of 15 patients. Clinically relevant dose volume histogram parameters were evaluated according to established consensus criteria. ResultsBoth KBP models underestimated the mean heart and lung dose equally 0.4 Gy (0.3–1.1 Gy) and 1.4 Gy (1.1–2.8 Gy) compared to the clinical plans 0.8 Gy (0.5–1.8 Gy) and 1.7 Gy (1.3–3.2 Gy) while in the final calculations the mean lung dose was higher 1.9–2.0 Gy (1.5–3.5 Gy) for both KPB models. The U-Net model resulted in a mean planning target volume dose of 40.7 Gy (40.4–41.3 Gy), slightly higher than the clinical plans 40.5 Gy (40.1–41.0 Gy). ConclusionsOnly small differences were observed between the estimated and final dose calculation and the clinical results for both KPB models and the DL model. With a good set of breast plans, the data cleaning module is not needed and both KPB and DL models lead to clinically acceptable results.

Effects of model size and composition on quality of head-and-neck knowledge-based plans.

Knowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions. The six models differed in training size and plan composition: The KBPFull (n=203 plans), KBP101 (n=101), KBP50 (n=50), and KBP25 (n=25) were trained with plans from two head-and-neck physicians. KBPA and KBPB each contained n=101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000-7000cGy by a third physician was re-planned with all KBP models for validation. Standard head-and-neck dosimetric parameters were used to compare resulting plans. KBPFull plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBPFull plans were presented to another physician for blind review. Dosimetric comparison of KBPFull against KBP101 , KBP50 , and KBP25 investigated the effect of model size. Finally, KBPA versus KBPB tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t-test (p<0.05). Compared to manual plans, KBPFull significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBPFull plan over the manual plan in 20/39 cases. Dosimetric differences between KBPFull , KBP101 , KBP50 , and KBP25 plans did not exceed 187cGy on aggregate, except for the cochlea. Further, average differences between KBPA and KBPB were below 110cGy. Overall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models.

Knowledge Based Planning for Lung SBRT: Model Transferability between Treatment Systems and Calculation Algorithms

Knowledge based planning (KBP) is known to be effective and clinically feasible in achieving high quality treatment plans with less variability between planners and reduced planning time. A KBP model is highly dependent on characteristics of the training data. Our institution developed and validated a lung SBRT KBP model from plans treated with a C-arm linear accelerator (Linac). This study aims to evaluate the quality of lung SBRT plans with a KBP model when this model is used for an O-ring Linac with different MLC design, beam quality, and calculation algorithm. Two academic hospitals in the same healthcare network were included in this study. One hospital trained a KBP model for five-fraction lung SBRT using 43 plans calculated using the Eclipse AcurosXB v15.6 algorithm for a C-arm Linac. The model was developed to prioritize dose gradient outside the PTV while satisfying institutional critical organ limits and validated using an additional set of 12 plans. A cohort of 10 patients (11 plans) previously treated with lung SBRT using the O-ring Linac at the second hospital was selected. These plans were calculated using the Eclipse AAA v15.6 algorithm with prescription doses of 30-50 Gy in five fractions. Each plan was re-optimized using the KBP model, without manual adjustment of the optimization objectives, using the same beam geometry as in the clinical plans. KBP plans were normalized to achieve the same PTV coverage as the clinical plans. Clinical and KBP plans were evaluated for ITV D98%, PTV conformity index (CI), R50 and maximum dose at 2 cm from the PTV (D2cm). All clinical and KBP plans met critical organ dose constraints. The range of CI was 1-1.13 vs. 0.96-1.05 for clinical plans and KBP plans, respectively. R50 and D2cm were lower with KBP plans (p = 0.007 and p = 0.05, respectively). ITV D98% range was 105-120% vs. 117-122% of the PTV prescription dose for clinical plans and KBP plans, respectively, and higher with KBP plans (p<0.001). For plans where the PTV overlaps the chest wall (n = 7), the maximum chest wall doses (D0.03cc) from KBP plans were higher than the clinical plans (p = 0.16), with an absolute chest wall D0.03cc difference of 9 Gy (54 Gy vs 63 Gy). The KBP model includes an ITV SIB of 120% of the PTV prescription, whereas the clinical plans reduced the ITV SIB when the PTV overlapped the chest wall, highlighting a difference in clinical practice between the two hospitals in this study. This study supports that a lung SBRT KBP model trained using plans for one Linac system and the AcurosXB algorithm can be used to generate clinically acceptable plans without manual planner intervention for a different Linac system and the AAA calculation algorithm, indicating the potential of a KBP model use for centralized planning process across different Linac systems and calculation algorithms.

Knowledge Based Planning Evolution: Multi-Criteria Optimization of Prostates. Is There Always a Better Model?

The current paradigm of radiotherapy Knowledge Based Planning (KBP) utilizes dose volume relationships on previously treated plans and may be insensitive to clinician plan preferences. To improve KBP models, incorporation of plan quality metrics and optimization weightings can be achieved using Clinical Scoring Metrics (CSM) and Multi-Criteria Optimization (MCO) respectively. A combination of KBP, MCO and CSM may result in higher quality plan output with potential to improve all prospective clinical plans. This study assesses the benefit of MCO, using CSM, and its application in the evolution of the KBP model. A retrospective Dose Volume Histogram (DVH) parameter review was undertaken for 40 intact prostate radiotherapy cases. Following analysis, these cases were used to develop the CSM system to reflect clinician preferences and departmental protocol. Hypofractionated plans containing simultaneous integrated boost (SIB) and urethral sparing were originally planned using a clinically approved KBP model (Model A). Model A was then refined using MCO, improving the 40 individual model plans to form Model B. A further 20 prostate patients, who were excluded from KBP development, were planned using both models to compare plan quality using the CSM system. Using a single optimization, Model A resulted in a median plan score of 134.1 (92.4-151.5), compared to the median Model B score of 145.1 (114.5-175.1) out of a possible score of 200 points. A Wilcoxon rank sum test was conducted and found the model score difference was significant (p = 0.014). Organ At Risk (OAR) doses, including Rectum V30 Gy, were found to significantly (p = 0.019) decrease, with Model A resulting in a median of 22.1 Gy (9.3-39.0), compared to the Model B median of 16.3 Gy (6.8-35.7). PTV coverage metric V57 Gy, resulted in no significant differences (p = 0.47) with a median score of 98.4% (97.2% - 99.9%) and 98.4% (96.9% - 99.2%) for model A and B, respectively. The application of MCO was used to influence and produce higher quality KBP models, as supported by a CSM system. OAR doses were found to significantly decrease, with no effect on target coverage. As a result, existing plan objectives were tightened through the utilization of KBP models, even with a single optimization resulting in higher scoring plans. The future development of this work may increase the efficiency of planning operations and allow the clinician to accurately anticipate planning goals for not only prostate, but all disease sites.

Standardizing LT Chest Wall Radiotherapy Treatment Planning in a Low- or Middle- Income Country Radiotherapy Clinic Using Knowledge Based Planning

Radiotherapy departments in low- or middle-income countries (LMICs) tend to lag behind introducing emerging technologies like intensity-modulated radiotherapy (IMRT). IMRT has become the standard of care in high-income countries (HIC) due to reduced toxicity and improved outcomes in a wide variety of cancers. The purpose of this work is showing the results of left Chest-Wall knowledge-based planning (KBP) standardization and implementation in a LMIC setting. A Halcyon Linac was installed at our clinic in Guatemala in 2019 and currently used to treat ∼90 IMRT patients daily. The standardization of IMRT procedures has been difficult for complex sites like chest-wall. The steps for standardization included: AAPM TG-263 nomenclature implementation, and planning workflows within the TPS, creation of optimization structures, and plan quality evaluation following RTOG1005 protocol hypofractionation arm. 25 plans were created manually achieving all RTOG1005 protocol constraints. The statistics were analyzed trough the model analytics tool provided by KPB manufacturer. The results show that more plans are needed to improve the KBP model. This initial model was used to create a standardized clinical protocol in the TPS in order to continue adding plans to the KBP model database. This approach ensures that we obtain consistent plan quality and standardize our planning. The manual planning objectives achieved: CONCLUSION: The experience using the TPS to standardize our treatment planning process achieved good consistency in our planning objectives. This approach will help create KBP models according to our own clinic-specific requirements. Future work will be made to compare our LMIC KBP models with those made at a HIC academic radiotherapy center.

Identifying and Re-Planning Challenging Retrospective SRS Patients to Evaluate the Potential for a White Matter Tract Sparing Knowledge-Based Planning Model

Diffusion tensor imaging (DTI) with automated tractography, along with auto-parcellation of volumetric MRI (vMRI), allows for reliable parcellation of critical white matter (WM) and hippocampal structures in the brain. Knowledge-based planning (KBP) may facilitate the optimization of new brain SRS plans that highly prioritize WM tracts and hippocampal sparing, allowing for cognitive preservation. However, building a high-performing model is challenging as the geometry of WM tracts and their proximity to planning target volumes (PTVs) can vary substantially. In this work we propose an iterative step for creating a KBP model where we used a preliminary model to identify challenging plans and worked on improving their dosimetry to refine the model predictions. Tractography of eloquent deep WM was obtained from high resolution DTI, along with auto-segmentation of bilateral hippocampi from vMRI and an open-source software for processing and analyzing brain MRI images. These ROIs were imported to the treatment planning system as organs-at-risk (OARsDTI). A preliminary KBP model was built from an IRB-approved cohort of 30 consecutive patients (75 plans total) treated for brain metastasis with non-coplanar SRS with total dose from 20 to 30 Gy in one to five fractions using a LINAC. This preliminary model was used to identify the 7 plans with the highest doses to the OARsDTI. These plans were re-optimized with new objectives on the OARsDTI that were partially or totally overlapped with the isodose line representing 30% of the prescription dose (as a surrogate for proximity to the PTV). The OARsDTI that were overlapping with the PTV were excluded from this analysis. We evaluated the re-optimized plans (PlanNew) by comparing maximum and mean dose (Dmax and Dmean, respectively) of OARsDTI to the initial values from the preliminary plans (PlanPre). In order to verify that the PlanNew were not compromising other structures or treatment goals, we also evaluated conformity index (CI), PTV coverage and the dosimetry of OARs included in the PlanPre. Additionally, the PlanNew were normalized to our clinical standard used for the PlanPre (100% of prescription dose to 98% of the PTV). In the 7 plans, there were 30 OARsDTI overlapping with the 30% isodose line. Refining the dosimetric objectives for the OARsDTI decreased the Dmean on average 31% when comparing PlanNew to Planpre (p<0.05, Wilcoxon rank-sum test). The CI of the PlanNew were not statistically different from the PlanPre. Although Dmax of PlanNew was 12% lower than Dmax of Planpre, the difference was not statistically different. The addition of a plan refinement step was shown to improve dose to the OARsDTI in 7 challenging SRS plans. The generation of these plans are a necessary step to creating a refined KBP model capable of accounting for the high variability in WM tracts geometry and their proximity with PTV.

Left Anterior Descending Coronary Artery-Optimized Knowledge-Based Radiotherapy Planning in Patients with Locally Advanced Non-Small Cell Lung Cancer

The left anterior descending (LAD) coronary artery volume receiving 15 Gy (V15 Gy) has been associated with major adverse cardiac events and mortality in patients with locally advanced non-small cell lung cancer (LA-NSCLC) treated with radiotherapy (RT). However, coronary arteries are not routinely contoured as standard-of-care and there is limited data describing coronary-sparing planning techniques. Our objective was to evaluate whether LAD-sparing could be improved compared to real-world plans using a commercial knowledge-based planning (KBP) software without clinically unacceptable tradeoffs to other critical organs at risk (OAR). Retrospective replanning analysis was conducted on 31 RT plans developed for patients with LA-NSCLC. Each case was manually replanned with volumetric modulated arc radiotherapy (VMAT) optimized for LAD V15 Gy sparing with a prescription dose of 60 Gy. The resulting plans were used to create an LAD-optimized KBP model. The model included both KBP-generated objectives and additional point objectives to meet LAD (V15 Gy < 10%), lung (V5 Gy < 60%, V20 < 30%, mean < 17 Gy), heart (mean < 20 Gy), spinal cord (D0.03cc < 45 Gy), esophagus (V60 Gy < 17%, mean < 34 Gy), and planning target volume (PTV; D95% = 100%). The LAD-optimized model was deployed to replan 33 additional VMAT plans for LA-NSCLC with prescription dose 60 Gy, delivered between 2018 and 2022. Dose constraints or objectives were only unmet with KBP when not met in the original plan. Descriptive statistics were used to compare dosimetric parameters between original and LAD-optimized KBP plans. For the 33 plans identified for LAD-optimized KBP replanning, the original plans had a mean LAD V15 Gy of 17.9% (standard error [SE], 3.9%), while the LAD-optimized KBP plans had a significantly lower mean LAD V15 Gy of 5.9% (SE, 1.9%; p = 0.031). There was no significant difference in mean heart dose between the original and LAD-optimized KBP plans (8.4 Gy [SE 6.6 Gy] vs. 7.3 Gy [SE 5.8 Gy]; p = 0.48). Of these 33 KBP plans, 42% (n = 14) had mean reductions in LAD V15 Gy from 40.5% (SE 4.3%) to 10.5% (SE 3.5%; p = 0.0002), with 9/14 now able to meet < 10%, without affecting other critical OAR objectives (p > 0.5 for lungs, esophagus, spinal cord). 45% (n = 15) originally had an LAD V15 Gy < 1%, which were unchanged using KBP (p > 0.3). Four plans (12%) had higher LAD V15 Gy in the KBP vs. original plan (2/4 still meeting < 10%), due to the model prioritizing other OARs to a greater degree. Improvements in LAD-sparing was achievable in more than 40% of plans without apparent clinically significant tradeoffs in other critical OARs and without corresponding change in MHD. This result underscores the importance of standard implementation of LAD contouring and dose constraints, particularly given the discordance between whole heart and LAD dose exposure.

Automated evaluation for rapid implementation of knowledge-based radiotherapy planning models.

Knowledge-based planning (KBP) offers the ability to predict dose-volume metrics based on information extracted from previous plans, reducing plan variability and improving plan quality. As clinical integration of KBP is increasing there is a growing need for quantitative evaluation of KBP models. A .NET-based application, RapidCompare, was created for automated plan creation and analysis of Varian RapidPlan models. RapidCompare was designed to read calculation parameters and a list of reference plans. The tool copies the reference plan field geometry and structure set, applies the RapidPlan model, optimizes the KBP plan, and generates data for quantitative evaluation of dose-volume metrics. A cohort of 85 patients, divided into training (50), testing (10), and validation (25) groups, was used to demonstrate the utility of RapidCompare. After training and tuning, the KBP model was paired with three different optimization templates to compare various planning strategies in the validation cohort. All templates used the same set of constraints for the planning target volume (PTV). For organs-at-risk, the optimization template provided constraints using the whole dose-volume histogram (DVH), fixed-dose/volume points, or generalized equivalent uniform dose (gEUD). The resulting plans from each optimization approach were compared using DVH metrics. RapidCompare allowed for the automated generation of 75 total plans for comparison with limited manual intervention. In comparing optimization techniques, the Dose/Volume and Lines optimization templates generated plans with similar DVH metrics, with a slight preference for the Lines technique with reductions in heart V30Gy and spinal cord max dose. The gEUD model produced high target heterogeneity. Automated evaluation allowed for the exploration of multiple optimization templates in a larger validation cohort than would have been feasible using a manual approach. A final KBP model using line optimization objectives produced the highest quality plans without human intervention.

Study on the transferability of the knowledge-based VMAT model to predict IMRT plans in prostate cancer radiotherapy

ObjectiveThe aim of this study was to investigate the feasibility of VMAT library-derived model transfer in the prediction of IMRT plans by dosimetry comparison among with three groups of IMRT plans: two groups of automatic IMRT plans generated by the knowledge-based the volumetric modulated arc therapy (VMAT) model and intensity-modulated radiation therapy (IMRT) model and one group of manual IMRT plans.Methods52 prostate cancer patients who had completed radiotherapy were selected and randomly divided into 2 groups with 40 and 12 separately. Then both VMAT and IMRT plans were manually designed for all patients. The total plans in the group with 40 cases as training datasets were added to the knowledge-based planning (KBP) models for learning and finally obtained VMAT and IMRT training models. Another 12 cases were selected as the validation group to be used to generated auto IMRT plans by KBP VMAT and IMRT models. At last, the radiotherapy plans from three groups were obtained: the automated IMRT plan (V-IMRT) predicted by the VMAT model, the automated IMRT plan (I-IMRT) predicted by the IMRT model and the manual IMRT plan (M-IMRT) designed before. The dosimetric parameters of planning target volume (PTV) and organ at risks (OARs) as well as the time parameters (monitor unit, MU) were statistically analyzed.ResultsThe dose limit of all plans in the training datasets met the clinical requirements. Compared with the training plans added to VMAT model, the dosimetry parameters have no statistical differences in PTV (P > 0.05); the dose of X% volume (Dx%) with D25% and D35% in rectal and the maximum dose (Dmax) in the right femoral head were lower (P = 0.04, P = 0.01, P = 0.00) while D50% in rectal was higher (< 0.05) in the IMRT model plans. In the 12 validation cases, both automated plans showed better dose distribution compared with the M-IMRT plan: the Dmax of PTV in the I-IMRT plans and the dose in volume of interesting (VOI) of bladder and bilateral femoral heads were lower with a statistically significant difference (P < 0.05). Compared with the I-IMRT plans, dosimetric parameters in PTV and VOI of all OARs had no statistically significant differences (P > 0.05), but the Dmax in left femoral heard and D15% in the right femoral head were lower and have significant differences (P < 0.05). Furthermore, the low-dose regions, which was defined as all volumes outside of the PTV (RV) with the statistical parameters of mean dose (Dmean), the volume of covering more than 5 Gy dose (V5Gy), and also the time parameter (MU) required to perform the plan were considered. The results showed that Dmean in V-IMRT was smaller than that in the I-IMRT plan (P = 0.02) and there was no significant difference in V5Gy and MU (P > 0.05).ConclusionCompared with the manual plan, the IMRT plans generated by the KBP models had a significant advantage in dose control of both OARs and PTV. Compared to the I-IMRT plans, the V-IMRT plans was not only without significant disadvantages, but it also achieved slightly better control of the low-dose region, which meet the clinical requirements and can used in the clinical treatment. This study demonstrates that it is feasible to transfer the KBP VMAT model in the prediction of IMRT plans.

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.

Development and Validation of Single-Optimization Knowledge-Based Volumetric Modulated Arc Therapy Model Plan in Nasopharyngeal Carcinomas

Knowledge-based planning (KBP) has evolved to standardize and expedite the complex process of radiation therapy planning for nasopharyngeal cancer (NPC). Herein, we aim to develop and validate the suitability of a single-optimization KBP for NPC. Volumetric modulated arc therapy plans of 103 patients with NPC treated between 2016 and 2020 were reviewed and used to generate a KBP model. A validation set of 15 patients was employed to compare the quality of single optimization KBP and clinical plans using the paired t test and the Wilcoxon signed rank test. The time required for either planning was also analyzed. Most patients (86.7%) were of locally advanced stage (III/IV). The median dose received by 95% of the high-risk planning target volume was significantly higher for the KBP (97.1% vs 96.4%; P=.017). The median homogeneity (0.09 vs 0.1) and conformity (0.98 vs 0.97) indices for high-risk planning target volume and sparing of the normal tissues like optic structures, spinal cord, and uninvolved dysphagia and aspiration-related structures were better with the KBP (P < .05). In the blinded evaluation, the physician preferred the KBP plan in 13 out of 15 patients. The median time required to generate the KBP and manual plans was 53 and 77 minutes, respectively. KBP with a single optimization is an efficient and time saving alternative for manual planning in NPC.

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.

In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics. A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices. The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92Gy (p=0.51) for attention Res U-Net, 0.94Gy (p=0.40) for Res U-Net, 2.94Gy (p=0.09) for attention U-Net, and 3.51Gy (p=0.08) for U-Net. For the OARs, the values for the and indices were 2.72Gy (p<0.01) for attention Res U-Net, 2.94Gy (p<0.01) for Res U-Net, 1.10Gy (p<0.01) for attention U-Net, 0.84Gy (p<0.29) for U-Net. All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.

PD-0981 Use of RapidPlan knowledge-based planning models for planning of rectum cases with Ethos TPS

PD-0981 Use of RapidPlan knowledge-based planning models for planning of rectum cases with Ethos TPS

Can knowledge based treatment planning of VMAT for post-mastectomy locoregional radiotherapy involving internal mammary chain and supraclavicular fossa improve performance efficiency?

To validate and evaluate the performance of knowledge-based treatment planning for Volumetric Modulated Arc Radiotherapy for post-mastectomy loco-regional radiotherapy. Two knowledge-based planning (KBP) models for different dose prescriptions were built using the Eclipse RapidPlanTM v 16.1 (Varian Medical Systems, Palo Alto, USA) utilising the plans of previously treated patients with left-sided breast cancer who had undergone irradiation of the left chest wall, internal mammary nodal (IMN) region and supra-clavicular fossa (SCF). Plans of 60 and 73 patients were used to generate the KBP models for the prescriptions of 40 Gy in 15 fractions and 26 Gy in 5 fractions, respectively. A blinded review of all the clinical plans (CLI) and KBPs was done by two experienced radiation oncology consultants. Statistical analysis of the two groups was also done using the standard two-tailed paired t-test or Wilcoxon signed rank test, and p<0.05 was considered significant. A total of 20 metrics were compared. The KBPs were found to be either better (6/20) or comparable (10/20) to the CLIs for both the regimens. Dose to heart, contralateral breast,contralateral lung were either better or comparable in the KBP plans except of ipsilateral lung. Mean dose (Gy) for the ipsilateral lung are significantly (p˂0.001) higher in KBP though the values were acceptable clinically. Plans were of similar quality as per the result of the blinded review which was conducted by slice-by-slice evaluation of dose distribution for target coverage, overdose volume and dose to the OARs. However, it was also observed that treatment times in terms of monitoring units (MUs) and complexity indices are more in CLIs as compared with KBPs (p<0.001). KBP models for left-sided post-mastectomy loco-regional radiotherapy were developed and validated for clinical use. These models improved the efficiency of treatment delivery as well as work flow for VMAT planning involving both moderately hypo fractionated and ultra-hypo fractionated radiotherapy regimens.

Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology

The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk-OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability.

The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT.

Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy

Background and purposeClinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis. Materials and methodsA clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (Mt), while the other one at achieving the highest mean sample quality (Rt). Four subsequent updates were accomplished. The goodness, robustness and quality of the outcomes were measured and compared to those of the common ancestor. Plan quality was assessed through the Plan Quality Metric (PQM) and plan complexity was monitored. ResultsBoth update procedures allowed for an increase in the OARs sparing between +3.9 % and +19.2 % compared to plans generated by a human planner. Target coverage and homogeneity slightly reduced [−0.2 %;−14.7 %] while plan complexity showed only minor changes.Increasing the sample size resulted in more reliable predictions and improved goodness-of-fit, while increasing the mean sample quality improved the outcomes but slightly reduced the models reliability. ConclusionsRepeated updates of clinical KBP models can enhance their robustness, reliability and the overall quality of automatically generated plans. The periodical expansion of the model sample accompanied by the removal of the unacceptable low quality plans should maximize the benefits of the updates while limiting the associated workload.

Evaluating the plan quality of a general head-and-neck knowledge-based planning model versus separate unilateral/bilateral models.

The implementation of knowledge-based planning (KBP) continues to grow in radiotherapy clinics. KBP guides radiation treatment design by generating clinically acceptable plans in a timely and resource-efficient manner. The role of multiple KBP models tailored for variations within a disease site remains undefined in part because of the substantial effort and number of training cases required to create a high-quality KBP model. In this study, our aim was to explore whether site-specific KBP models lead to clinically meaningful differences in plan quality for head-and-neck (HN) patients when compared to a general model. One KBP model was created from prior volumetric-modulated arc therapy (VMAT) cases that treated unilateral HN lymph nodes while another model was created from VMAT cases that treated bilateral HN nodes. Thirty cases from each model (60 cases total) were randomly selected to create a third, general model. These models were applied to 60 HN test cases - 30 unilateral and 30 bilateral - to generate 180 VMAT plans in Eclipse. Clinically relevant dose metrics were compared between models. Paired-sample t-tests were used for statistical analysis, with the threshold for statistical significance set a priori at 0.007, taking into consideration multiple hypothesis testing to avoid type I error. For unilateral test cases, the unilateral model-generated plans had significantly lower spinal cord maximum doses (12.1 Gy vs 19.3 Gy, p < 0.001) and oral cavity mean doses (20.8 Gy vs 23.0 Gy, p < 0.001), compared with the bilateral model-generated plans. The unilateral and general models generated comparable plans for unilateral HN test cases. For bilateral test cases, the bilateral model created plans had significantly lower brainstem maximum doses (10.8 Gy vs 12.2 Gy, p < 0.001) and parotid mean doses (24.0 Gy vs 25.5 Gy, p < 0.001) when compared to the unilateral model. Right parotid mean doses were lower for bilateral model plans compared to general model plans (23.8 Gy vs 24.4 Gy). The general model created plans with significantly lower brainstem maximum doses (10.3 Gy vs 10.8 Gy) and oral cavity mean doses (35.3 Gy vs 36.7 Gy) when compared with bilateral model-generated plans. The general model outperformed the bilateral model in several dose metrics but they were not deemed clinically significant. For both case sets, the unilateral and general model created plans had higher monitor units when compared to the bilateral model, likely due to more stringent constraint settings. All other dose metrics were comparable. This study demonstrates that a balanced general HN model created using carefully curated treatment plans can produce high quality plans comparable to dedicated unilateral and bilateral models.

Knowledge-Based Quality Assurance and Model Maintenance in Lung Cancer Radiation Therapy in a Statewide Quality Consortium of Academic and Community Practice Centers

PurposeLocally advanced lung cancer (LALC) treatment planning is often complex due to challenging tradeoffs related to large targets near organs at risk, making the judgment of plan quality difficult. The purpose of this work was to update and maintain a multi-institutional knowledge-based planning (KBP) model developed by a statewide consortium of academic and community practices for use as a plan quality assurance (QA) tool. Methods and MaterialsSixty LALC volumetric-modulated arc therapy plans from 2021 were collected from 24 institutions. Plan quality was scored, with high-quality clinical (HQC) plans selected to update a KBP model originally developed in 2017. The model was validated via automated KBP planning, with 20 cases excluded from the model. Differences in dose–volume histogram metrics in the clinical plans, 2017 KBP model plans, and 2022 KBP model plans were compared. Twenty recent clinical cases not meeting consortium quality metrics were replanned with the 2022 model to investigate potential plan quality improvements. ResultsForty-seven plans were included in the final KBP model. Compared with the clinical plans, the 2022 model validation plans improved 60%, 65%, and 65% of the lung V20Gy, mean heart dose, and spinal canal D0.03cc metrics, respectively. The 2022 model showed improvements from the 2017 model in hot spot management at the cost of greater lung doses. Of the 20 recent cases not meeting quality metrics, 40% of the KBP model-replanned cases resulted in acceptable plans, suggesting potential clinical plan improvements. ConclusionsA multi-institutional KBP model was updated using plans from a statewide consortium. Multidisciplinary plan review resulted in HQC model training plans and model validation resulted in acceptable quality plans. The model proved to be effective at identifying potential plan quality improvements. Work is ongoing to develop web-based training plan review tools and vendor-agnostic platforms to provide the model as a QA tool statewide.

Bridging the Gap of Radiotherapy Treatment Planning Quality between High-Income, and Low- and Middle-Income Countries Using Knowledge-Based Planning

<h3>Purpose/Objective(s)</h3> Radiotherapy departments in low- and middle-income countries (LMICs) have started introducing intensity-modulated radiotherapy (IMRT). IMRT has become the standard of care in high-income countries (HIC) due to reduced toxicity and improved outcomes in a wide variety of cancers. The purpose of this work is to demonstrate the feasibility of adapting knowledge-based planning (KBP) models established in a HIC to a LMIC lacking experience in IMRT to improve plan quality and planning efficiency. <h3>Materials/Methods</h3> A new o-ring linear accelerator was installed at a hospital in Guatemala in 2019 and has been used to treat ∼80 patients daily with IMRT. Two KBP models developed on a cohort of prostate cancer patients at a university in the US were applied in the hospital in Guatemala to create 12 IMRT plans for 7 prostate patients with different prescriptions, including simultaneous-integrated and sequential boosts). Plan quality described by dose-volume histograms (DVH) metrics and planning time were compared between the plans created with the KBP models and those created manually by local dosimetrists or physicists. <h3>Results</h3> The plans created using these KBP models achieved similar or better coverage over the planning target volume in each plan, and showed a lower dose to bladder and rectum in 11 of 12 plans, and the remainder showed a comparable dose distribution and DVH for rectum and bladder. Sum plans showed better rectal sparing for all patients with V50Gy reduced between 1%-30.4% and bladder sparing for 6 of 7 patients with V50Gy reduced between 1.3% -16.8%. The time efficiency to create IMRT plans using KBP models improved three-fold, on average one hour versus three hours, respectively. <h3>Conclusion</h3> Despite different prescriptions, guidelines and demographics of cancer patients between two institutions in a HIC and LMIC, this work demonstrates that KBP can be a useful tool in ensuring consistent, high-quality IMRT plans at institutions with little to no experience in inverse planning. In addition, KBP has the potential to improve capacity with higher efficiency and may help address the shortage of medical physicists and dosimetrists in LMICs.