Knowledge-based Planning Plans Research Articles

Knowledge-based planning for Gamma Knife.

Current methods for Gamma Knife (GK) treatment planning utilizes either manual forward planning, where planners manually place shots in a tumor to achieve a desired dose distribution, or inverse planning, whereby the dose delivered to a tumor is optimized for multiple objectives based on established metrics. For other treatment modalities like IMRT and VMAT, there has been a recent push to develop knowledge-based planning (KBP) pipelines to address the limitations presented by forward and inverse planning. However, no complete KBP pipeline has been created forGK. To develop a novel (KBP) pipeline, using inverse optimization (IO) with 3D dose predictions for GK. Data were obtained for 349 patients from Sunnybrook Health Sciences Centre. A 3D dose prediction model was trained using 322 patients, based on a previously published deep learning methodology, and dose predictions were generated for the remaining 27 out-of-sample patients. A generalized IO model was developed to learn objective function weights from dose predictions. These weights were then used in an inverse planning model to generate deliverable treatment plans. A dose mimicking (DM) model was also implemented for comparison. The quality of the resulting plans was compared to their clinical counterparts using standard GK quality metrics. The performance of the models was also characterized with respect to the dosepredictions. Across all quality metrics, plans generated using the IO pipeline performed at least as well as or better than the respective clinical plans. The average conformity and gradient indices of IO plans was 0.737 0.158 and 3.356 1.030 respectively, compared to 0.713 0.124 and 3.452 1.123 for the clinical plans. IO plans also performed better than DM plans for five of the six quality metrics. Plans generated using IO also have average treatment times comparable to that of clinical plans. With regards to the dose predictions, predictions with higher conformity tend to result in higher quality KBP plans. Plans resulting from an IO KBP pipeline are, on average, of equal or superior quality compared to those obtained through manual planning. The results demonstrate the potential for the use of KBP to generate GK treatment with minimal humanintervention.

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.

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.

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.

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 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.

Deep Learning–Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%,D95%, and D99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.

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

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. 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. 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. 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.

Development and clinical validation of a robust knowledge-based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors.

PurposeTo develop a robust and adaptable knowledge‐based planning (KBP) model with commercially available RapidPlanTM for early stage, centrally located non‐small‐cell lung tumors (NSCLC) treated with stereotactic body radiotherapy (SBRT) and improve a patient's“simulation to treatment” time.MethodsThe KBP model was trained using 86 clinically treated high‐quality non‐coplanar volumetric modulated arc therapy (n‐VMAT) lung SBRT plans with delivered prescriptions of 50 or 55 Gy in 5 fractions. Another 20 independent clinical n‐VMAT plans were used for validation of the model. KBP and n‐VMAT plans were compared via Radiation Therapy Oncology Group (RTOG)–0813 protocol compliance criteria for conformity (CI), gradient index (GI), maximal dose 2 cm away from the target in any direction (D2cm), dose to organs‐at‐risk (OAR), treatment delivery efficiency, and accuracy. KBP plans were re‐optimized with larger calculation grid size (CGS) of 2.5 mm to assess feasibility of rapid adaptive re‐planning.ResultsKnowledge‐based plans were similar or better than n‐VMAT plans based on a range of target coverage and OAR metrics. Planning target volume (PTV) for validation cases was 30.5 ± 19.1 cc (range 7.0–71.7 cc). KBPs provided an average CI of 1.04 ± 0.04 (0.97–1.11) vs. n‐VMAT plan'saverage CI of 1.01 ± 0.04 (0.97–1.17) (P < 0.05) with slightly improved GI with KBPs (P < 0.05). D2cm was similar between the KBPs and n‐VMAT plans. KBPs provided lower lung V10Gy (P = 0.003), V20Gy (P = 0.007), and mean lung dose (P < 0.001). KBPs had overall better sparing of OAR at the minimal increased of average total monitor units and beam‐on time by 460 (P < 0.05) and 19.2 s, respectively. Quality assurance phantom measurement showed similar treatment delivery accuracy. Utilizing a CGS of 2.5 mm in the final optimization improved planning time (mean, 5 min) with minimal or no cost to the plan quality.ConclusionThe RTOG‐compliant adaptable RapidPlan model for early stage SBRT treatment of centrally located lung tumors was developed. All plans met RTOG dosimetric requirements in less than 30 min of planning time, potentially offering shorter “simulation to treatment” times. OAR sparing via KBPs may permit tumorcidal dose escalation with minimal penalties. Same day adaptive re‐planning is plausible with a 2.5‐mm CGS optimizer setting.

Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning

The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning efficiency. The following method was adopted: AIP-SFFP generates a prostate IMRT plan through predictions of fluence maps using patient anatomy. No inverse planning is required. AIP-SFFP is centered on a custom-built deep learning (DL) neural network for fluence map prediction. Predictions are imported to a commercial treatment-planning system for dose calculation and plan generation. AIP-SFFP was demonstrated for prostate IMRT simultaneously-integrated-boost planning (58.8 Gy/70 Gy to PTV58.8 Gy/PTV70 Gy in 28 fx, PTV = Planning Target Volume). Training data was generated from 106 patients using a knowledge-based planning (KBP) plan generator. Two types of 2D projection images were designed to represent structures’ sizes and locations, and a total of eight projections were utilized to describe targets and organs-at-risk. Projections at nine template beam angles were stacked as inputs for artificial intelligence (AI) training. 14 patients were used as independent tests. The generated test plans were compared with the plans from the KBP training plan generator and clinic practice. The following results were obtained: After normalization (PTV70 Gy V70 Gy = 95%), all 14 AI plans met institutional criteria. The coverage of PTV58.8 Gy in the AI plans was comparable to KBP and clinic plans without statistical significance. The whole body (BODY) D1cc and rectum D0.1cc of AI plans were slightly higher (<1 Gy) compared to KBP and clinic plans; in contrast, the bladder D1cc and other rectum and bladder low doses in the AI plans were slightly improved without clinical relevance. The overall isodose distribution in the AI plans was comparable with KBP plans and clinical plans. AIP-SFFP generated each test plan within 20s including the prediction and the dose calculation. In conclusion, AIP-SFFP was successfully developed for prostate IMRT planning. AIP-SFFP demonstrated good overall plan quality and real-time efficiency. Showing great promise, AIP-SFFP will be investigated for immediate clinical application.

Offline Quality Assurance for Intensity Modulated Radiation Therapy Treatment Plans for NRG-HN001 Head and Neck Clinical Trial Using Knowledge-Based Planning

PurposeThis study aimed to investigate whether a disease site–specific, multi-institutional knowledge based-planning (KBP) model can improve the quality of intensity modulated radiation therapy treatment planning for patients enrolled in the head and neck NRG-HN001clinical trial and to establish a threshold of improvements of treatment plans submitted to the clinical trial. Methods and MaterialsFifty treatment plans for patients enrolled in the NRG-HN001 clinical trial were used to build a KBP model; the model was then used to reoptimize 50 other plans. We compared the dosimetric parameters of the submitted and KBP reoptimized plans. We compared differences between KBP and submitted plans for single- and multi-institutional treatment plans. ResultsMean values for the dose received by 95% of the planning target volume (PTV_6996) and for the maximum dose (D0.03cc) of PTV_6996 were 0.5 Gy and 2.1 Gy higher in KBP plans than in the submitted plans, respectively. Mean values for D0.03cc to the brain stem, spinal cord, optic nerve_R, optic nerve_L, and chiasm were 2.5 Gy, 1.9 Gy, 6.4 Gy, 6.6 Gy, and 5.7 Gy lower in the KBP plans than in the submitted plans. Mean values for Dmean to parotid_R and parotid_L glands were 2.2 Gy and 3.8 Gy lower in KBP plans, respectively. In 33 out of 50 KBP plans, we observed improvements in sparing of at least 7 organs at risk (OARs) (brain stem, spinal cord, optic nerves (R & L), chiasm, and parotid glands [R & L]). A threshold of improvement of OARs sparing of 5% of the prescription dose was established for providing the quality assurance results back to the treating institution. ConclusionsA disease site–specific, multi-institutional, clinical trial-based KBP model improved sparing of OARs in a large number of reoptimized plans submitted to the NRG-HN001 clinical trial, and the model is being used as an offline quality assurance tool.

Noninferiority Study of Automated Knowledge-Based Planning Versus Human-Driven Optimization Across Multiple Disease Sites

To evaluate whether automated knowledge-based planning (KBP) (a) is noninferior to human-driven planning across multiple disease sites and (b) systematically affects dosimetric plan quality and variability. Clinical KBP automated planning routines were developed for prostate, prostatic fossa, hypofractionated lung, and head and neck. Clinical implementation consisted of independent generation of human-generated and KBP plans (145 cases across all sites), followed by blinded plan selection. Reviewing physicians were prompted to select a single plan; when plan equivalence was volunteered, this scored as KBP selection. Plan selection analysis used a noninferiority framework testing the hypothesis that KBP is not worse than human-driven planning (threshold: lower 95% confidence interval [CI] > 0.45 = noninferiority; > 0.5 = superiority). Target and organ-at-risk metrics were compared by dose differencing: ΔDx = Dx, human-Dx, KBP (2-tailed paired t test, Bonferroni-corrected P < .05 significance threshold). To evaluate the aggregated effect of KBP on planning performance, we examined post-KBP dosimetric parameters against 183 plans generated just before KBP implementation (2-tailed unpaired t test, Bonferroni-corrected P < .05). Across all disease sites, the KBP success rate (physician preferred + equivalent) was noninferior compared with human-driven planning (83 of 145 = 57.2%; range, 49.2%-65.3%) but did not cross the threshold for superiority. The KBP success rate in respective disease sites was superior with head and neck ([22 + 2]/36 = 66.7%; 95% CI, 51%-82%) and noninferior for lung stereotactic body radiation therapy ([21 + 2]/36 = 63.9%; 95% CI, 48%-80%) but did not meet noninferiority criteria with prostate ([16 + 3]/41 = 46.3%; 95% CI, 31%-62%) or prostatic fossa ([17 + 0]/32 = 53.1%; 95% CI, 36%-70%). Prostate, prostatic fossa, and head and neck showed significant differences in KBP-selected plans versus human-selected plans, with KBP generally exhibiting greater organ-at-risk sparing and human plans exhibiting better target homogeneity. Analysis of plan quality pre- and post-KBP showed some reductions in organ doses and quality metric variability in prostate and head and neck. Fully automated KBP was noninferior to human-driven plan optimization across multiple disease sites. Dosimetric analysis of treatment plans before and after KBP implementation showed a systematic shift to higher plan quality and lower variability with the introduction of KBP.

Standardization of volumetric modulated arc therapy-based frameless stereotactic technique using a multidimensional ensemble-aided knowledge-based planning.

Aim of this article is to describe a new knowledge-based planning (KBP) methodology using volumetric modulated arc therapy (VMAT) for stereotactic radiosurgery (SRS) and radiotherapy (SRT) assisted by an ensemble mapping technique for use in a Monte Carlo planning system. Libraries of 121 stereotactic patients were assembled on the basis of eight different parameters (a) tumor laterality, (b) whether planning target volume (PTV) dose coverage challenged by the presence of the organ at risk (OAR), (c) prescription dose and number of fractions, (d) number of PTVs, (e) tumor volume, (f) shortest distance between OAR and PTV (edge to edge distance, or EED), (g) center to center distance between OARs and PTV (CCD), and (h) lateral dimension of external contour (brain). For new patients, the most appropriate library plan was selected on the basis of the above categorization. A KBP plan was created based on this selected library plan with all parameters unchanged keeping the isocenter at the center of PTV. Using the same beam configuration, another independent treatment plan (IP) was generated by an experienced dosimetrist for comparison. IP and KBP were compared for 76 new patients. Of 197 patients (121 library and 76 new), 103 (52.3%) were placed in the OAR-challenged category and 94 were placed in the OAR unchallenged category. The ensemble mapping technique shows that, for an OAR-challenged patient, picking up the library plan is appropriate. IP was marginally better than KBP in PTV coverage and dose conformity (PCI). Library plans, IP, and KBP offer a mean PCI of 0.77±0.2, 0.79±0.2, and 0.78±0.4, mean PTV-V99% of 97.3±22.0%, 98.9±14.1%, and 98.2±13.2%, and mean MU of 2403.8±2403.8, 2344.0±2423.6, and 2473.6±2296.8, respectively. Statistically significant differences were observed in the planning time between the IP and KBP plans for both OAR-challenged (P<0.001) and -unchallenged (P<0.002) categories. Comparison of optimization and dose calculation time showed a much lower average planning time of 111.0±84.1min for KBP as against 248.2±96.6min for IP. Validation results for KBP plans indicate the multidimensional ensemble mapping mechanism can accurately pick up the most appropriate library plan. KBP plans, although slightly inferior in their dosimetric quality, fulfill all the required clinical conditions and dose constraints. KBP plans save considerable planning time and are nearly independent of the skill and knowledge of the treatment planner. KBP works well with a Monte Carlo planning system like Monaco.

Improving Quality and Consistency in NRG Oncology Radiation Therapy Oncology Group 0631 for Spine Radiosurgery via Knowledge-Based Planning

Touse knowledge-based planning (KBP) as a method of producing high-quality, consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body radiation therapy on NRG Oncology Radiation Therapy Oncology Group (RTOG)0631. An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues. The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of planning target volume receiving prescription dose increased from 93.3%±3.2% to 98.3%±1.4% (P=.01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1±7.3cm3 for manual plans to 1.8±0.6cm3 with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Knowledge-based planning plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target. Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results, owing to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are nonstandard.

Evaluation of Knowledge-Based Versus Manual Treatment Planning of Whole-Pelvic Volumetric Modulated Arc Therapy With Simultaneous Integrated Boost for Node-Positive Prostate Cancer

The aim of this study is to evaluate the effectiveness and usability of knowledge-based planning (KBP) software in whole-pelvic volumetric modulated arc therapy with simultaneous integrated boost (WP-SIB-VMAT) for node-positive prostate cancer (NP-PC). A commercial KBP software was used for KBP model construction and plan optimization. Twenty-two planning data of the patients with NP-PC who had been treated with WP-SIB-VMAT in our department were used for KBP model construction. Another 32 planning data of the patients with NP-PC who had been treated with whole-pelvic fixed-gantry intensity modulated radiation therapy with SIB in our department were used for validation. The structure sets included high-, intermediate-, low-risk PTVs, and 5 organs at risk (OARs): femurs, large bowel, small bowel, bladder, and rectum. The patients were numbered according to the date of starting irradiation. Experienced medical physicist “A” and radiation oncologist “B” manually performed the optimization of WP-SIB-VMAT in patient 01-16 (Manual A), and patient 17-32 (Manual B), respectively. Resident “C” performed the optimization of WP-SIB-VMAT in patient 01-32 with use of the KBP model. He followed the rule that adjustments of optimizing objectives for PTVs with automatically generated priorities and line objectives for OARs were forbidden. For adjustment of dose to OARs, he was allowed to manually add new objectives and structures against anatomical contours. All optimizations were performed to satisfy the clinical protocol in our department. We measured the required time for optimization, and assessed the dose distributions. Two-tailed Wilcoxon signed-rank sum tests were used to identify significant (p < 0.05) differences in the required time and the mean doses to OARs. The median required time for optimization was significantly shorter in KBP than in manual planning. Similarly, except for the rectum, the median mean doses to OARs were significantly lower in KBP. The median mean dose to the rectum in Manual A plans was significantly lower than in KBP plans. Although dose coverage in protruded region of low-risk PTV, especially in obturator lymph node region, was tended to be lower in KBP plans, the dose distributions of these were considered to be clinically acceptable. No significant change was observed in KBP plans when the number of plans included in the model was increased up to 40.Abstract 3576MedianManual AKBPP valueManual BKBPP valueTime [min]48.534.0<0.0565.531.0<0.05Femurs Dmean [Gy]25.522.8<0.0525.622.7<0.05Large bowel Dmean [Gy]25.022.7<0.0525.021.7<0.05Small bowel Dmean [Gy]19.817.2<0.0521.218.5<0.05Bladder Dmean [Gy]45.343.8<0.0544.944.0<0.05Rectum Dmean [Gy]40.542.4<0.0545.142.1<0.05 Open table in a new tab Although minor manual adjustments were required to achieve the clinical goals in some cases, the KBP software generated comparable plans in significantly shorter time than manual optimization approach in WP-SIB-VMAT planning for NP-PC.

Rapid Pre-Clinical Validation of Knowledge-Based Planning Routines across Multiple Disease Sites

Knowledge-based planning (KBP) requires extensive testing and validation prior to clinical implementation. The purpose of this work is demonstrate, across multiple disease sites, a rapid validation method for KBP routines. We retrospectively identified 57 prostate plans, 40 lung stereotactic body radiotherapy (SBRT) plans (equal numbers left and right), and 20 head-and-neck plans, all treated with volumetric modulated arc therapy (VMAT). Using a commercial KBP system and four site-specific routines, we generated deliverable KBP plans for each available case. All field parameters (e.g. arc arrangement, beam energy) and prescription scaling (D95%=100%) were preserved in each case. With site-specific reporting scripts that automatically extract dose-volume histogram (DVH) information from the original clinical plans and the newly created KBP plans, all relevant target and organ-at-risk (OAR) DVH cutpoints can be compared in each disease sample. Aggregate analysis of site-specific DVH metrics illuminate the systematic differences between the proposed KBP routines and the existing clinical planning performance. 57 prostate cases were replanned with the prostate KBP routine in ∼3 hours (∼10 min each case, 4 continuously running in parallel). With similar runtime to prostate, 40 lung SBRT cases were executed in ∼2 hours. The 20 head-and-neck cases required ∼30 minutes per case, completed in <4 hours. The reporting scripts extracted data in <30 seconds/case. A sample of critical metrics for each disease site are summarized in Table 1. In prostate, the rectal DVHs were improved between the KBP and clinical samples with no cost to increased target heterogeneity. In head-and-neck, KBP better spared the parotid glands while delivering slightly higher max doses and equivalent D95 for all targets. Both KBP lung SBRT incarnations created more heterogeneous target doses; this was offset by improved left lung doses at 50%, whereas the right lung KBP plans were the same or worse for V50%. With just one clinician-day of effort 4 KBP routines were tested against existing clinical practice across 117 patients. Future implementations of treatment planning and informatics systems that allow batch replanning of cases in the background could effectively eliminate barriers to safely incorporating new KBP routines (either internally-generated or externally obtained) into clinical practice.Abstract 3556; Table 1ClinicalKBPProstatePTVDmax107.8% ± 1.3%107.3% ± 1.3%RectumV40V65V7526.5% ± 7.0%10.1% ± 3.7%5.9% ± 2.5%23.5% ± 6.0%9.2% ± 3.5%5.2% ± 2.5%Head-and-neckPTV_high (70 Gy)Dmax109.2% ± 2.6%111.0% ± 3.5%PTV_intermediate (59.4 Gy) PTV_low (54.1 Gy)D95101.2% ± 3.2%102.% ± 2.2%101.6% ± 3.1%102.4% ± 2.2%Left Parotid Right ParotidDmean (Gy)25.9 ± 14.425.7 ± 9.823.3 ± 15.021.1 ± 8.9Left Lung SBRTPTVDmax109.6% ± 3.5%122.9% ± 4.5%Left LungV50% (cc)78.5 ± 61.973.4 ± 55.7Right Lung SBRTPTVDmax112.0% ± 3.0%128.0% ± 7.1%Right LungV50% (cc)114.8 ± 77.7117.2 ± 81.9 Open table in a new tab

Improving Quality and Consistency in Clinical Trials via Knowledge-Based Planning, NRG Oncology RTOG 0631

Consistency and standardization of radiotherapy planning quality for conducting a multi-institutional clinical trial are always challenging, and non-protocol compliant radiotherapy plans have been shown to impact patient outcomes. This study was aimed to utilize knowledge-based planning (KBP) as a method of producing high quality, consistent, protocol compliant treatment plans, in a complex setting of spine SBRT on RTOG 0631. An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted to NRG for RTOG 0631. The original and KBP plans were compared via their protocol scores, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues. The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and external RTOG 0631 cases. Two external plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in conformity or gradient indices between the manually optimized plans and the KBP plans. The volume of PTV receiving prescription dose increased from 93.3 ± 3.2% to 99.5 ± 0.7% (P<0.001) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 ± 7.3 cc for manual plans to 1.2 ± 0.4 cc with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results due to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are non-standard. The KBP model will be made available on the Center for Innovation in Radiation Oncology website.

Heuristic knowledge-based planning for single-isocenter stereotactic radiosurgery to multiple brain metastases.

Single-isocenter, volumetric-modulated arc therapy (VMAT) stereotactic radiosurgery (SRS) for multiple brain metastases (multimets) can deliver highly conformal dose distributions and reduce overall patient treatment time compared to other techniques. However, treatment planning for multimet cases is highly complex due to variability in numbers and sizes of brain metastases, as well as their relative proximity to organs-at-risk (OARs). The purpose of this study was to automate the VMAT planning of multimet cases through a knowledge-based planning (KBP) approach that adapts single-target SRS dose predictions to multiple target predictions. Using a previously published artificial neural network (ANN) KBP system trained on single-target, linac-based SRS plans, 3D dose distribution predictions for multimet patients were obtained by treating each brain lesion as a solitary target and subsequently combining individual dose predictions into a single distribution. Spatial dose distributions di(r→) for each of the i=1…N lesions were merged using the combination function d(r→)=∑iNdin(r→)1/n. The optimal value of n was determined by minimizing root-mean squared (RMS) difference between clinical multimet plans and predicted dose per unit length along the line profile joining each lesion in the clinical cohort. The gradient measure GM=[3/4π]1/3V50%1/3-V100%1/3 is the primary quality metric for SRS plan evaluation at our institution and served as the main comparative metric between clinical plans and the KBP results. A total of 41 previously treated multimet plans, with target numbers ranging from N=2-10, were used to validate the ANN predictions and subsequent KBP auto-planning routine. Fully deliverable KBP plans were developed by converting predicted dose distribution into patient-specific optimization objectives for the clinical treatment planning system (TPS). Plan parity was maintained through identical arc configuration and target normalization. Overall plan quality improvements were quantified by calculating the difference between SRS quality metrics (QMs): ΔQM=QMclinical -QMKBP . In addition to GM, investigated QMs were: volume of brain receiving ≥10Gy (V10Gy ), volume of brain receiving ≥5Gy (ΔV5Gy ), heterogeneity index (HI), dose to 0.1cc of the brainstem (D0.1cc ), dose to 1% of the optic chiasm (D1% ), and interlesion dose (DIL ). In addition to this quantitative analysis, overall plan quality was assessed via blinded plan comparison of the manual and KBP treatment plans by SRS-specializing physicians. A dose combination factor of n=8 yielded an integrated dose profile RMS difference of 2.9% across the 41-patient cohort. Multimet dose predictions exhibited ΔGM=0.07±0.10cm against the clinical sample, implying either further normal tissue sparing was possible or that dose predictions were slightly overestimating achievable dose gradients. The latter is the more likely explanation, as this bias vanished when dose predictions were converted to deliverable KBP plans ΔGM=0.00±0.08cm. Remaining QMs were nearly identical or showed modest improvements in the KBP sample. Equivalent QMs included: ΔV10Gy =0.37±3.78cc, ΔHI=0.02±0.08 and ΔDIL =-2.22±171.4cGy. The KBP plans showed a greater degree of normal tissue sparing as indicated by brain ΔV5Gy =4.11±24.05cc, brainstem ΔD0.1cc =42.8±121.4cGy, and chiasm ΔD1% =50.8±83.0cGy. In blinded review by SRS-specializing physicians, KBP-generated plans were deemed equivalent or superior in 32/41(78.1%) of the cases. Heuristic KBP-driven automated planning in linac-based, single-isocenter treatments for multiple brain metastases maintained or exceeded overall plan quality.

Fully automated, comprehensive knowledge-based planning for stereotactic radiosurgery: Preclinical validation through blinded physician review

PurposeAs knowledge-based planning (KBP) attempts to augment and potentially supplant manual treatment planning, it is imperative to ensure any implementation maintains or improves overall plan quality in any disease site. The purpose of this study was to demonstrate the overall quality of KBP-driven automated stereotactic radiosurgery (SRS) treatment planning using blinded physician comparison and determine systematic factors predictive of physician plan preference to guide future KBP refinement. Methods and materialsAutomated noncoplanar volume modulated arc therapy KBP routines were developed for 199 plans across 3 clinical SRS scenarios: isolated lesions (isolated), lesions closely abutting (<3 cm) organs at risk (involved), and single-isocenter multiple metastases (multimet). Overall plan quality and preference were assessed via blinded review of the plans by two SRS physicians. Quantitative quality metrics were also compared to determine systematic differences in the treatment plans. Multiple parameters were investigated as predictors of KBP plan selection. ResultsFor the isolated, involved, and multimet scenarios, the KBP plans were considered to be superior or equivalent to clinical plans 86.7% (91/105), 81.1% (43/53), and 78.1% (32/41) of the time, respectively. All investigated quality metrics were equivalent or indicated more sparing for all KBP plans. The only nondosimetric predictor was planning target volume in the isolated (P = .02) and involved (P = .05) groups. The dosimetric predictors for the isolated group were gradient measure and heterogeneity index (both P < .01). In the multimet category, the only significant dosimetric predictor was interlesion dose (P = .01). ConclusionsThe fully automated KBP SRS plans were equivalent or superior to previously treated plans in 83.4% (166/199) of cases. In clinical implementation, geometric features found to be predictive of KBP performance can be used to identify plans where KBP results might benefit from further refinement, whereas dosimetric predictive features could be used to further refine KBP optimization priorities.

Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system. We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol.The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volume histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V10 (percentage volume of PBM receiving at least 10 Gy dose) and V20 (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V40 (absolute volume of bowel receiving at least 40 Gy dose) and V45 (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set).To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning "routine"). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBPFINAL), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBPFINAL plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets. KBPFINAL plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBPFINAL versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBPFINAL plans, indicating lowered probability of hematologic toxicity. This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.