How important is dosimetrist experience for intensity modulated radiation therapy? A comparative analysis of a head and neck case

Reinforcement learning-driven automated head and neck simultaneous integrated boost (SIB) radiation therapy: flexible treatment planning aligned with clinical preferences

Objective.Head-and-neck simultaneous integrated boost (SIB) treatment planning using intensity modulated radiation therapy is particularly challenging due to the proximity to organs-at-risk. Depending on the specific clinical conditions, different parotid-sparing strategies are utilized to preserve parotid function without compromising local tumor control. Clinically this is typically done with attending's directive or via trial-and-error comparison with different sparing tradeoffs. To streamline this process, we proposed a deep reinforcement learning (DRL)-based framework that automatically generates treatment plans with flexibility to adapt to clinical preferences.Approach.A preference-encoded DRL (PEDRL) framework was developed to self-interact with the clinical treatment planning system and dynamically adjust objective constraints in the inverse optimization space. It was powered by the discrete soft actor-critic algorithm with a multi-layer perceptron architecture. The agent interprets intermediate plan status and iteratively modifies objective constraint values in a human-like fashion. By encoding parotid-sparing preferences within the state space, the agent autonomously adapts the sparing strategy to achieve optimal plan quality based on clinical priorities. The agent was trained through iterative treatment plan generation using 40 cases and subsequently tested on additional 44 patients, with generated plans compared to clinical plans.Main results.The PEDRL-generated plans demonstrated comparable performance across all dosimetric evaluation metrics for both bilateral and unilateral sparing cases in the test set. For bilateral cases, the mean value of the parotid median dose was 18.82 Gy (left) and 19.61 Gy (right), compared to 19.31 Gy (left) and 19.12 Gy (right) in the clinical plans. In unilateral sparing cases, the mean value of the spared parotid median dose was 19.92 Gy in the PEDRL-generated plans, compared to 17.16 Gy in the clinical plans. Significance.The proposed novel automated treatment planning framework efficiently generates SIB treatment plans tailored to clinical preferences, demonstrating both effectiveness and adaptability.

Validating knowledge-based volumetric modulated arc therapy plans with a multi-institution model (broad model) using a complete open-loop dataset for prostate cancer

This study examined the characteristics of the broad model (KBPbroad) through a complete open-loop evaluation of volumetric modulated arc therapy (VMAT) plans for prostate cancer in 30 patients at two institutions. KBPbroad, trained using 561 prostate cancer VMAT plans from five institutions with different treatment protocols, was shared with two institutions. The institutions were not involved in the creation of KBPbroad. Plan created with KBPbroad were compared with clinical plans (CPs) and plans created using a single-institution model at each institution (KBPonsite). KBPbroad maintained the target coverage of CPs while meeting dose limits across varied settings at each institution. At institution X, KBPbroad provided 40, 60, and 70 Gy (V40Gy, V60Gy, and V70Gy, respectively) to 30.8% ± 9.9%, 15.3% ± 8.5%, and 9.0% ± 6.4% of the volume at the rectal wall, respectively, which were significantly smaller than those provided by KBPonsite and CPs. At institution Y, compared with CPs, KBPbroad provided significantly greater V50Gy, V70Gy, dose to 2% of the volume (D2%) at the rectum, and D2% at the bladder but significantly lower V50Gy and V70Gy at the bladder, in addition to superior dose homogeneity and conformality at the planning target volume. Our complete open-loop evaluation of VMAT plans for prostate cancer at two institutions demonstrated the clinical effectiveness of KBPbroad at institutions producing plans with insufficient reductions in OAR doses. Thus, the quality of KBPbroad plans is likely greater than that of KBPonsite plans and CPs.

A deep learning model to predict dose distributions for breast cancer radiotherapy

PURPOSE

In this work, we propose to develop a 3D U-Net-based deep learning model that accurately predicts the dose distribution for breast cancer radiotherapy.

METHODS

This study included 176 breast cancer patients, divided into training, validating and testing sets. A deep learning model based on the 3D U-Net architecture was developed to predict dose distribution, which employed a double encoder combination attention (DECA) module, a cross stage partial + Resnet + Attention (CRA) module, a difficulty perception and a critical regions loss. The performance and generalization ability of this model were evaluated by the voxel mean absolute error (MAE), several clinically relevant dosimetric indexes and 3D gamma passing rates.

RESULTS

Our model accurately predicted the 3D dose distributions with each dosage level mirroring the clinical reality in shape. The generated dose-volume histogram (DVH) matched with the ground truth curve. The total dose error of our model was below 1.16 Gy, complying with clinical usage standards. When compared to other exceptional models, our model optimally predicted eight out of nine regions, and the prediction errors for the first planning target volume (PTV1) and PTV2 were merely 1.03 Gy and 0.74 Gy. Moreover, the mean 3%/3 mm 3D gamma passing rates for PTV1, PTV2, Heart and Lung L achieved 91.8%, 96.4%, 91.5%, and 93.2%, respectively, surpassing the other models and meeting clinical standards.

CONCLUSIONS

This study developed a new deep learning model based on 3D U-Net that can accurately predict dose distributions for breast cancer radiotherapy, which can improve the quality and planning efficiency.

Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality

Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces challenges. The transition from controlled retrospective environments to real-world clinical settings introduces heightened scrutiny from clinical end users, potentially leading to decreased clinical acceptance. Key considerations for implementing AI RT planning include ensuring the AI model performance aligns with clinical standards, using high-quality training data, and incorporating sufficient data variation through meticulous curation by clinical experts. Beyond technical aspects, factors such as potential biases and the level of trust clinical end users place in AI may present unforeseen obstacles for real-world clinical use. Addressing these challenges requires bridging education and expertise gaps among clinical end users, enabling them to confidently embrace and utilize AI for routine RT planning. By fostering a better understanding of AI capabilities, building trust, and providing comprehensive training, the promises of AI RT planning can be a reality in the clinical setting. This article assesses the current clinical use of AI RT planning and explores challenges and considerations for bridging gaps in knowledge and expertise for AI operationalization, with focus on training data curation, workflow integration, explainability, bias, and domain knowledge. Remaining challenges in clinical implementation of AI RT treatment planning are examined in the context of trust building approaches.

Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT)

Purpose.To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated radiation therapy (IMRT) treatment plans for head-and-neck (HN) cancer with consistent organ-at-risk (OAR) sparing performance.Methods.With IRB approval, one hundred and twenty HN patients receiving IMRT were included. The DRL agent was trained with 20 patients. During each inverse optimization process, the intermediate dosimetric endpoints' values, dose volume constraints' values and structure objective function losses were collected as the DRLstates. By adjusting the objective constraints asactions, the agent learned to seek optimal rewards by balancing OAR sparing and planning target volume (PTV) coverage. Reward computed from current dosimetric endpoints and clinical objectives were sent back to the agent to update action policy during model training. The trained agent was evaluated with the rest 100 patients.Results.The DRL agent was able to generate a clinically acceptable IMRT plan within12.4±3.1min without human intervention. DRL plans showed lower PTV maximum dose (109.2%) compared to clinical plans (112.4%) (p< .05). Average median dose of left parotid, right parotid, oral cavity, larynx, pharynx of DRL plans were 15.6 Gy, 12.2 Gy, 25.7 Gy, 27.3 Gy and 32.1 Gy respectively, comparable to 17.1 Gy, 15.7 Gy, 24.4 Gy, 23.7 Gy and 35.5 Gy of corresponding clinical plans. The maximum dose of cord + 5 mm, brainstem and mandible were also comparable between the two groups. In addition, DRL plans demonstrated reduced variability, as evidenced by smaller 95% confidence intervals. The total MU of the DRL plans was 1611 vs 1870 (p< .05) of clinical plans. The results signaled the DRL's consistent planning strategy compared to the planners' occasional back-and-forth decision-making during planning.Conclusion.The proposed DRL agent is capable of efficiently generating HN IMRT plans with consistent quality.

Automatic Treatment Planning for Radiation Therapy: A Cross-Modality and Protocol Study

Purpose

This study investigated the applicability of 3-dimensional dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multicriteria optimizer (MCO) on adapting predictions to different clinical preferences.

Methods and Materials

Using a previously created 3-stage U-Net in-house model trained on the 2020 American Association of Physicists in Medicine OpenKBP challenge data set (340 head and neck plans, all planned using 9-field static intensity modulated radiation therapy [IMRT]), we retrospectively generated dose predictions for 20 patients. These dose predictions were, in turn, used to generate deliverable IMRT, VMAT, and tomotherapy plans using the fallback plan functionality in Raystation. The deliverable plans were evaluated against the dose predictions based on primary clinical goals. A new set of plans was also generated using MCO-based optimization with predicted dose values as constraints. Delivery QA was performed on a subset of the plans to assure clinical deliverability.

Results

The mimicking approach accurately replicated the predicted dose distributions across different modalities, with slight deviations in the spinal cord and external contour maximum doses. MCO optimization significantly reduced doses to organs at risk, which were prioritized by our institution while maintaining target coverage. All tested plans met clinical deliverability standards, evidenced by a gamma analysis passing rate >98%.

Conclusions

Our findings show that a model trained only on IMRT plans can effectively contribute to planning across various modalities. Additionally, integrating predictions as constraints in an MCO-based workflow, rather than direct dose mimicking, enables a flexible, warm-start approach for treatment planning, although the benefit is reduced when the training set differs significantly from an institution's preference. Together, these approaches have the potential to significantly decrease plan turnaround time and quality variance, both at high-resource medical centers that can train in-house models and smaller centers that can adapt a model from another institution with minimal effort.

Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy

PURPOSE

The goal of this study was to propose a knowledge-based planning system which could automatically design plans for lung cancer patients treated with intensity-modulated radiotherapy (IMRT).

METHODS AND MATERIALS

From May 2018 to June 2020, 612 IMRT treatment plans of lung cancer patients were retrospectively selected to construct a planning database. Knowledge-based planning (KBP) architecture named αDiar was proposed in this study. It consisted of two parts separated by a firewall. One was the in-hospital workstation, and the other was the search engine in the cloud. Based on our previous study, A‑Net in the in-hospital workstation was used to generate predicted virtual dose images. A search engine including a three-dimensional convolutional neural network (3D CNN) was constructed to derive the feature vectors of dose images. By comparing the similarity of the features between virtual dose images and the clinical dose images in the database, the most similar feature was found. The optimization parameters (OPs) of the treatment plan corresponding to the most similar feature were assigned to the new plan, and the design of a new treatment plan was automatically completed. After αDiar was developed, we performed two studies. The first retrospective study was conducted to validate whether this architecture was qualified for clinical practice and involved 96 patients. The second comparative study was performed to investigate whether αDiar could assist dosimetrists in improving the quality of planning for the patients. Two dosimetrists were involved and designed plans for only one trial with and without αDiar; 26 patients were involved in this study.

RESULTS

The first study showed that about 54% (52/96) of the automatically generated plans would achieve the dosimetric constraints of the Radiation Therapy Oncology Group (RTOG) and about 93% (89/96) of the automatically generated plans would achieve the dosimetric constraints of the National Comprehensive Cancer Network (NCCN). The second study showed that the quality of treatment planning designed by junior dosimetrists was improved with the help of αDiar.

CONCLUSIONS

Our results showed that αDiar was an effective tool to improve planning quality. Over half of the patients' plans could be designed automatically. For the remaining patients, although the automatically designed plans did not fully meet the clinical requirements, their quality was also better than that of manual plans.

Improving 3D dose prediction for breast radiotherapy using novel glowing masks and gradient-weighted loss functions

BACKGROUND

The quality of treatment plans for breast cancer can vary greatly. This variation could be reduced by using dose prediction to automate treatment planning. Our work investigates novel methods for training deep-learning models that are capable of producing high-quality dose predictions for breast cancer treatment planning.

PURPOSE

The goal of this work was to compare the performance impact of two novel techniques for deep learning dose prediction models for tangent field treatments for breast cancer. The first technique, a "glowing" mask algorithm, encodes the distance from a contour into each voxel in a mask. The second, a gradient-weighted mean squared error (MSE) loss function, emphasizes the error in high-dose gradient regions in the predicted image.

METHODS

Four 3D U-Net deep learning models were trained using the planning CT and contours of the heart, lung, and tumor bed as inputs. The dataset consisted of 305 treatment plans split into 213/46/46 training/validation/test sets using a 70/15/15% split. We compared the impact of novel "glowing" anatomical mask inputs and a novel gradient-weighted MSE loss function to their standard counterparts, binary anatomical masks, and MSE loss, using an ablation study methodology. To assess performance, we examined the mean error and mean absolute error (ME/MAE) in dose across all within-body voxels, the error in mean dose to heart, ipsilateral lung, and tumor bed, dice similarity coefficient (DSC) across isodose volumes defined by 0%-100% prescribed dose thresholds, and gamma analysis (3%/3 mm).

RESULTS

The combination of novel glowing masks and gradient weighted loss function yielded the best-performing model in this study. This model resulted in a mean ME of 0.40%, MAE of 2.70%, an error in mean dose to heart and lung of -0.10 and 0.01 Gy, and an error in mean dose to the tumor bed of -0.01%. The median DSC at 50/95/100% isodose levels were 0.91/0.87/0.82. The mean 3D gamma pass rate (3%/3 mm) was 93%.

CONCLUSIONS

This study found the combination of novel anatomical mask inputs and loss function for dose prediction resulted in superior performance to their standard counterparts. These results have important implications for the field of radiotherapy dose prediction, as the methods used here can be easily incorporated into many other dose prediction models for other treatment sites. Additionally, this dose prediction model for breast radiotherapy has sufficient performance to be used in an automated planning pipeline for tangent field radiotherapy and has the major benefit of not requiring a PTV for accurate dose prediction.

Investigating the impact of rapidplan on ethos automated planning

Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP). Seventy-four patients with head-and-neck (HN) cancer and 37 patients with prostate cancer were used to construct separate RP models. Additionally, 16 patients from each group (HN and prostate) were selected to assess the performance of Ethos auto-planning results. Initially, a template-based Ethos plan (Non-RP plan) was generated, followed by integrating the corresponding RP model's DVH estimates into the optimization process to generate another plan (RP plan). We compared the target coverage, OAR doses, and total monitor units between the non-RP and RP plans. Both RP and non-RP plans achieved comparable target coverage in HN and Prostate cases, with a negligible difference of less than 0.5% (p > 0.2). RP plans consistently demonstrated lower doses of OARs in both HN and prostate cases. Specifically, the mean doses of OARs were significantly reduced by 9% (p < 0.05). RP plans required slightly higher monitor units in both HN and prostate sites (p < 0.05), however, the plan generation time was almost similar (p > 0.07). The inclusion of the RP model reduced the OAR doses, particularly reducing the mean dose to critical organs compared to non-RP plans while maintaining similar target coverage. Our findings provide valuable insights for clinics adopting Ethos planning, potentially enhancing the auto-planning to operate optimally.

AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions

PURPOSE

Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy.

METHODS AND MATERIALS

We retrospectively collected data from 530 patients with esophageal cancer, including single-target and simultaneous integrated boost prescriptions, for model building. The proposed Asymmetric ResNeSt (AS-NeSt) model features novel 3-dimensional (3D) ResNeSt blocks and an asymmetrical architecture. We constructed a loss function targeting global and local doses and validated the model's performance against existing alternatives. Model-assisted experiments were used to validate its clinical benefits.

RESULTS

The AS-NeSt model maintained an absolute prediction error below 5% for each dosimetric metric. The average Dice similarity coefficient for isodose volumes was 0.93. The model achieved an average relative prediction error of 2.02%, statistically lower than Hierarchically Densely Connected U-net (4.17%), DoseNet (2.35%), and Densely Connected Network (3.65%). It also demonstrated significantly fewer parameters and shorter prediction times. Clinically, the AS-NeSt model raised physicians' ability to accurately preassess appropriate treatment methods before planning from 95.24% to 100%, reduced planning time by over 61% for junior dosimetrists and 52% for senior dosimetrists, and decreased both inter- and intra-dosimetrist discrepancies by more than 50%.

CONCLUSIONS

The AS-NeSt model, developed with innovative 3D ResNeSt blocks and an asymmetrical encoder-decoder structure, has been validated using clinical esophageal cancer patient data. It accurately predicts 3D dose distributions for various prescriptions, including simultaneous integrated boost, showing potential to improve the management of esophageal cancer treatment in a clinical setting.

A new predictive parameter for dose-volume metrics in intensity-modulated radiation therapy planning for prostate cancer: Initial phantom study

BACKGROUND

Organ-at-risk (OAR) sparing is often assessed using an overlap volume-based parameter, defined as the ratio of the volume of OAR that overlaps the planning target volume (PTV) to the whole OAR volume. However, this conventional overlap-based predictive parameter (COPP) does not consider the volume relationship between the PTV and OAR.

PURPOSE

We propose a new overlap-based predictive parameter that consider the PTV volume. The effectiveness of proposed overlap-based predictive parameter (POPP) is evaluated compared with COPP.

METHODS

We defined as POPP = (overlap volume between OAR and PTV/OAR volume) × (PTV volume/OAR volume). We generated intensity modulated radiation therapy (IMRT) based on step and shoot technique, and volumetric modulated arc therapy (VMAT) plans with the Auto-Planning module of Pinnacle3 treatment planning system (v14.0, Philips Medical Systems, Fitchburg, WI) using the American Association of Physicists in Medicine Task Group (TG119) prostate phantom. The relationship between the position and size of the prostate phantom was systematically modified to simulate various geometric arrangements. The correlation between overlap-based predictive parameters (COPP and POPP) and dose-volume metrics (mean dose, V70Gy, V60Gy, and V37.5 Gy for rectum and bladder) was investigated using linear regression analysis.

RESULTS

Our results indicated POPP was better than COPP in predicting intermediate-dose metrics. The bladder results showed a trend similar to that of the rectum. The correlation coefficient of POPP was significantly greater than that of COPP in < 62 Gy (82% of the prescribed dose) region for IMRT and in < 55 Gy (73% of the prescribed dose) region for VMAT regarding the rectum (p < 0.05).

CONCLUSIONS

POPP is superior to COPP for creating predictive models at an intermediate-dose level. Because rectal bleeding and bladder toxicity can be associated with intermediate-doses as well as high-doses, it is important to predict dose-volume metrics for various dose levels. POPP is a useful parameter for predicting dose-volume metrics and assisting the generation of treatment plans.

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation

Introduction

Acquisition of dosimetric knowledge by radiation therapy planners is a protracted and complex process. This study delves into the impact of empirical predictive models based on the knowledge-based planning (KBP) methodology, aimed at detecting suboptimal results and homogenizing and improving existing practices for prostate cancer. Moreover, the dosimetric effect of implementing these models into routine clinical practice was also assessed.

Materials and methods

Based on the KBP method, we analyzed 25 prostate treatment plans performed using VMAT by expert operators, aiming to correlate dose indicators with patient geometry. The D a v g C a v ( G y ) , V 45 G y C a v ( c c ) , and V 15 G y C a v ( c c ) of the peritoneal cavity and the V 60 G y ( % ) and V 70 G y ( % ) of the rectum and bladder were linked to geometric characteristics such as the distance from the planning target volume (PTV) to the organs at risk (OAR), the volume of the OAR, or the overlap between the PTV and the OAR. In the second phase, the KBP was used in routine clinical practice in a prospective cohort of 25 patients and compared with the 41 patient plans calculated before implementing the tool.

Results

Using linear regression, we identified strong geometric predictive factors for the peritoneal cavity, rectum, and bladder (R 2   > 0.8), with an average prescribed dose of 97.8%, covering 95% of the target volume. The use of the model led to a significant dose reduction ( Δ ) for all evaluated OARs. This trend was most notable for Δ V 15 G y C a v = - 171.5  cc  ( p = 0.003 ) . Significant reductions were also obtained in average doses to the rectum and bladder, Δ D a v g R e c t =   - 2.3   G y   ( p = 0.040 ) , and Δ D a v g V e s s =   - 3.3   G y   ( p = 0.039 ) respectively. Based on this model, we reduced the number of plans with OAR constraints above the clinical recommendations from 19% to 8%.

Conclusions

The KBP methodology established a robust and personalized predictive model for dose estimation to organs at risk in prostate cancer. Implementing the model resulted in improved sparing of these organs. Notably, it yields a solid foundation for harmonizing dosimetric practices, alerting us to suboptimal results, and improving our knowledge.

Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer

Purpose

Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer.

Methods and Materials

Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board-approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review.

Results

Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were "per protocol" or met "variation acceptable" ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes.

Conclusions

A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.

Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning

Background and purpose

Automation in radiotherapy treatment planning aims to improve both the quality and the efficiency of the process. The aim of this study was to report on a clinical implementation of a Deep Learning (DL) auto-planning model for left-sided breast cancer.

Materials and methods

The DL model was developed for left-sided breast simultaneous integrated boost treatments under deep-inspiration breath-hold. Eighty manual dose distributions were revised and used for training. Ten patients were used for model validation. The model was then used to design 17 clinical auto-plans. Manual and auto-plans were scored on a list of clinical goals for both targets and organs-at-risk (OARs). For validation, predicted and mimicked dose (PD and MD, respectively) percent error (PE) was calculated with respect to manual dose. Clinical and validation cohorts were compared in terms of MD only.

Results

Median values of both PD and MD validation plans fulfilled the evaluation criteria. PE was < 1% for targets for both PD and MD. PD was well aligned to manual dose while MD left lung mean dose was significantly less (median:5.1 Gy vs 6.1 Gy). The left-anterior-descending artery maximum dose was found out of requirements (median values:+5.9 Gy and + 2.9 Gy, for PD and MD respectively) in three validation cases, while it was reduced for clinical cases (median:-1.9 Gy). No other clinically significant differences were observed between clinical and validation cohorts.

Conclusion

Small OAR differences observed during the model validation were not found clinically relevant. The clinical implementation outcomes confirmed the robustness of the model.

Standardization of knowledge-based volumetric modulated arc therapy planning with a multi-institution model (broad model) to improve prostate cancer treatment quality

PURPOSE

To evaluate whether knowledge-based volumetric modulated arc therapy plans for prostate cancer with a multi-institution model (broad model) are clinically useful and effective as a standardization method.

METHODS

A knowledge-based planning (KBP) model was trained with 561 prostate VMAT plans from five institutions with different contouring and planning policies. Five clinical plans at each institution were reoptimized with the broad and single institution model, and the dosimetric parameters and relationship between Dmean and the overlapping volume (rectum or bladder and target) were compared.

RESULTS

The differences between the broad and single institution models in the dosimetric parameters for V50, V80, V90, and Dmean were: rectum; 9.5% ± 10.3%, 3.3% ± 1.5%, 1.7% ± 1.6%, and 3.6% ± 3.6%, (p < 0.001), bladder; 8.7% ± 12.8%, 1.5% ± 2.6%, 0.7% ± 2.4%, and 2.7% ± 4.6% (p < 0.02), respectively. The differences between the broad model and clinical plans were: rectum; 2.4% ± 4.6%, 1.7% ± 1.7%, 0.7% ± 2.4%, and 1.5% ± 2.0%, (p = 0.004, 0.015, 0.112, and 0.009) bladder; 2.9% ± 5.8%, 1.6% ± 1.9%, 0.9% ± 1.7%, and 1.1% ± 4.8%, (p < 0.018), respectively. Positive values indicate that the broad model has a lower value. Strong correlations were observed (p < 0.001) in the relationship between Dmean and the rectal and bladder volume overlapping with the target in the broad model (R = 0.815 and 0.891, respectively). The broad model had the smallest R2 of the three plans.

CONCLUSIONS

KBP with the broad model is clinically effective and applicable as a standardization method at multiple institutions.

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

OBJECTIVE

The 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.

METHODS

52 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.

RESULTS

The 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).

CONCLUSION

Compared 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.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system

BACKGROUND AND PURPOSE

To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions.

MATERIAL AND METHODS

All patients underwent iodine contrast-enhanced planning CT. Fourteen OAR were manually delineated. DL.1 and DL.2 solutions were trained with 63 mono-centric patients and > 1000 multi-centric patients, respectively. Ten and 15 patients with varied anatomies were selected for the atlas library and for testing, respectively. The evaluation was based on geometric indices (DICE coefficient and 95th percentile-Hausdorff Distance (HD_95%)), time needed for manual corrections and clinical dosimetric endpoints obtained using automated treatment planning.

RESULTS

Both DICE and HD_95% results indicated that DL algorithms generally performed better compared with ABAS algorithms for automatic segmentation of HN OAR. However, the hybrid-ABAS (ABAS.3) algorithm sometimes provided the highest agreement to the reference contours compared with the 2 DL. Compared with DL.2 and ABAS.3, DL.1 contours were the fastest to correct. For the 3 solutions, the differences in dose distributions obtained using AS contours and AS + manually corrected contours were not statistically significant. High dose differences could be observed when OAR contours were at short distances to the targets. However, this was not always interrelated.

CONCLUSION

DL methods generally showed higher delineation accuracy compared with ABAS methods for AS segmentation of HN OAR. Most ABAS contours had high conformity to the reference but were more time consuming than DL algorithms, especially when considering the computing time and the time spent on manual corrections.

Knowledge-based planning using both the predicted DVH of organ-at risk and planning target volume

PURPOSE

The purpose of this study was to evaluate the performance of a knowledge-based planning (KBP) method in nasopharyngeal cancer radiotherapy using the predicted dose-volume histogram (DVH) of organ-at risk (OAR) and planning target volume (PTV).

METHODS AND MATERIALS

A total of 85 patients previously treated for nasopharyngeal cancer using 9-field 6-MV intensity-modulated radiation therapy (IMRT) were identified for training and 30 similar patients were identified for testing. The dosimetric deposition information, individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation, was used to predict both OAR and PTV DVH. Two KBP methods (KBP_OAR and KBP_OAR+PTV) were established for plan generation based on the DVH prediction. The KBP_OAR method utilized the dose constraints based on the predicted OAR DVH and the PTV dose constraints obtained according to the planning experience, while the KBP_OAR+PTV method applied the dose constraints based on the predicted OAR and PTV DVH. For the plan evaluation, the PTV dose coverage was used D₉₈ and D₂, and the maximum dose, mean dose or dose-volume parameters were used for the OARs. Statistical differences of the two KBP methods were tested with the Wilcoxon signed rank test.

RESULTS

For patients with T3 tumors, there was no significant difference between the KBP_OAR and KBP_OAR+PTV methods in dosimetric results at most OARs and PTVs. Both KBP methods achieved a similar number of plans meeting the dose requirements. For patients with T4 tumors, KBP_OAR+PTV reduced the maximum dose by more than 1 Gy in the body, spinal cord, optic nerve, eye and temporal lobes and reduced the V₅₀ value by more than 3.9% in the larynx and tongue without reducing the PTV dose compared with KBP_OAR. The KBP_OAR+PTV method increased the plans by more than 14.2% in meeting the maximum dose requirements at the body, optic nerve, mandible and eye and increased the plans by more than 21.4% in meeting the V₅₀ of the larynx and V₅₀ of the tongue when compared with the KBP_OAR method.

CONCLUSIONS

For patients with T3 tumors, no significant difference was found between the KBP_OAR and KBP_OAR+PTV methods in dosimetric results at most OARs and PTVs. For patients with T4 tumors, the KBP_OAR+PTV method performs better than the KBP_OAR method in improving the quality of the plans. Compared with the KBP_OAR method, dose sparing of some OARs was achieved without reducing PTV dose coverage and helped to increase the number of plans meeting the dose requirements when the KBP_OAR+PTV method was utilized.

Multi-institution model (big model) versus single-institution model of knowledge-based volumetric modulated arc therapy (VMAT) planning for prostate cancer

We established a multi-institution model (big model) of knowledge-based treatment planning with over 500 treatment plans from five institutions in volumetric modulated arc therapy (VMAT) for prostate cancer. This study aimed to clarify the efficacy of using a large number of registered treatment plans for sharing the big model. The big model was created with 561 clinically approved VMAT plans for prostate cancer from five institutions (A: 150, B: 153, C: 49, D: 60, and E: 149) with different planning strategies. The dosimetric parameters of planning target volume (PTV), rectum, and bladder for two validation VMAT plans generated with the big model were compared with those from each institutional model (single-institution model). The goodness-of-fit of regression lines (R2 and χ2 values) and ratios of the outliers of Cook’s distance (CD) > 4.0, modified Z-score (mZ) > 3.5, studentized residual (SR) > 3.0, and areal difference of estimate (dA) > 3.0 for regression scatter plots in the big model and single-institution model were also evaluated. The mean ± standard deviation (SD) of dosimetric parameters were as follows (big model vs. single-institution model): 79.0 ± 1.6 vs. 78.7 ± 0.5 (D50) and 0.13 ± 0.06 vs. 0.13 ± 0.07 (Homogeneity Index) for the PTV; 6.6 ± 4.0 vs. 8.4 ± 3.6 (V90) and 32.4 ± 3.8 vs. 46.6 ± 15.4 (V50) for the rectum; and 13.8 ± 1.8 vs. 13.3 ± 4.3 (V90) and 39.9 ± 2.0 vs. 38.4 ± 5.2 (V50) for the bladder. The R2 values in the big model were 0.251 and 0.755 for rectum and bladder, respectively, which were comparable to those from each institution model. The respective χ2 values in the big model were 1.009 and 1.002, which were closer to 1.0 than those from each institution model. The ratios of the outliers in the big model were also comparable to those from each institution model. The big model could generate a comparable VMAT plan quality compared with each single-institution model and therefore could possibly be shared with other institutions.

A novel knowledge-based prediction model for estimating an initial equivalent uniform dose in semi-auto-planning for cervical cancer

Background

We developed a novel concept, equivalent uniform length (EUL), to describe the relationship between the generalized equivalent uniform dose (EUD) and the geometric anatomy around a tumor target. By correlating EUL with EUD, we established two EUD–EUL knowledge-based (EEKB) prediction models for the bladder and rectum that predict initial EUD values for generating quality treatment plans.

Methods

EUL metrics for the rectum and bladder were extracted and collected from the intensity-modulated radiotherapy therapy (IMRT) plans of 60 patients with cervical cancer. The two EEKB prediction models were built using linear regression to establish the relationships between EUL_r and EUD_r (EUL and EUD of rectum) and EUL_b, and EUD_b (EUL and EUD of bladder), respectively. The EE plans were optimized by incorporating the predicted initial EUD parameters for the rectum and bladder with the conventional pinnacle auto-planning (PAP) initial dose parameters for other organs. The efficiency of the predicted initial EUD values were then evaluated by comparing the consistency and quality of the EE plans, PAP plans (based on default PAP initial parameters), and manual plans (designed manually by different dosimetrists) for a sample of 20 patients.

Results

Linear regression analyses showed a significant correlation between EUL and EUD (R² = 0.79 and 0.69 for EUD_b and EUD_r, respectively). In a sample of 20 patients, the average bladder V40 and V50 derived from the EE plans were significantly lower (V40: 30.00 ± 5.76, V50: 14.36 ± 4.00) than the V40 and V50 values derived from manual plans (V40: 36.03 ± 8.02, V50: 19.02 ± 5.42). Compared with the PAP plans, the EE plans produced significantly lower average V30 and Dmean values for the bladder (V30: 50.55 ± 6.33, Dmean: 31.48 ± 1.97 Gy).

Conclusions

Our EEKB prediction models predicted reasonable initial EUD values for the rectum and bladder based on patient-specific geometric EUL values, thereby improving optimization and planning efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02120-4.

Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer

Purpose

Head and neck (HN) radiation (RT) treatment planning is complex and resource intensive. Deviations and inconsistent plan quality significantly affect clinical outcomes. We sought to develop a novel automated virtual integrative (AVI) knowledge-based planning application to reduce planning time, increase consistency, and improve baseline quality.

Methods and Materials

An in-house write-enabled script was developed from a library of 668 previously treated HN RT plans. Prospective hazard analysis was performed, and mitigation strategies were implemented before clinical release. The AVI-planner software was retrospectively validated in a cohort of 52 recent HN cases. A physician panel evaluated planning limitations during initial deployment, and feedback was enacted via software refinements. A final second set of plans was generated and evaluated. Kolmogorov-Smirnov test in addition to generalized evaluation metric and weighted experience score were used to compare normal tissue sparing between final AVI planner versus respective clinically treated and historically accepted plans. A t test was used to compare the interactive time, complexity, and monitor units for AVI planner versus manual optimization.

Results

Initially, 86% of plans were acceptable to treat, with 10% minor and 4% major revisions or rejection recommended. Variability was noted in plan quality among HN subsites, with high initial quality for oropharynx and oral cavity plans. Plans needing revisions were comprised of sinonasal, nasopharynx, P-16 negative squamous cell carcinoma unknown primary, or cutaneous primary sites. Normal tissue sparing varied within subsites, but AVI planner significantly lowered mean larynx dose (median, 18.5 vs 19.7 Gy; P < .01) compared with clinical plans. AVI planner significantly reduced interactive optimization time (mean, 2 vs 85 minutes; P < .01).

Conclusions

AVI planner reliably generated clinically acceptable RT plans for oral cavity, salivary, oropharynx, larynx, and hypopharynx cancers. Physician-driven iterative learning processes resulted in favorable evolution in HN RT plan quality with significant time savings and improved consistency using AVI planner.

SuPART: supervised projective adapted resonance theory for automatic quality assurance approval of radiotherapy treatment plans

Radiotherapy is a common treatment modality for the treatment of cancer, where treatments must be carefully designed to deliver appropriate dose to targets while avoiding healthy organs. The comprehensive multi-disciplinary quality assurance (QA) process in radiotherapy is designed to ensure safe and effective treatment plans are delivered to patients. However, the plan QA process is expensive, often time-intensive, and requires review of large quantities of complex data, potentially leading to human error in QA assessment. We therefore develop an automated machine learning algorithm to identify ‘acceptable’ plans (plans that are similar to historically approved plans) and ‘unacceptable’ plans (plans that are dissimilar to historically approved plans). This algorithm is a supervised extension of projective adaptive resonance theory, called SuPART, that learns a set of distinctive features, and considers deviations from them indications of unacceptable plans. We test SuPART on breast and prostate radiotherapy datasets from our institution, and find that SuPART outperforms common classification algorithms in several measures of accuracy. When no falsely approved plans are allowed, SuPART can correctly auto-approve 34% of the acceptable breast and 32% of the acceptable prostate plans, and can also correctly reject 53% of the unacceptable breast and 56% of the unacceptable prostate plans. Thus, usage of SuPART to aid in QA could potentially yield significant time savings.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Dose Prediction Models Based on Geometric and Plan Optimization Parameter for Adjuvant Radiotherapy Planning Design in Cervical Cancer Radiotherapy

The prediction of an additional space for the dose sparing of organs at risk (OAR) in radiotherapy is still difficult. In this pursuit, the present study was envisaged to find out the factors affecting the bladder and rectum dosimetry of cervical cancer. Additionally, the relationship between the dose-volume histogram (DVH) parameters and the geometry and plan dose-volume optimization parameters of the bladder/rectum was established to develop the dose prediction models and guide the planning design for lower OARs dose coverage directly. Thirty volume modulated radiation therapy (VMAT) plans from cervical cancer patients were randomly chosen to build the dose prediction models. The target dose coverage was evaluated. Dose prediction models were established by univariate and multiple linear regression among the dosimetric parameters of the bladder/rectum, the geometry parameters (planning target volume (PTV), volume of bladder/rectum, overlap volume of bladder/rectum (OV), and overlapped volume as a percentage of bladder/rectum volume (OP)), and corresponding plan dose-volume optimization parameters of the nonoverlapping structures (the structure of bladder/rectum outside the PTV (NOS)). Finally, the accuracy of the prediction models was evaluated by tracking d = (predicted dose-actual dose)/actual in additional ten VMAT plans. V ₃₀, V ₃₅, and V ₄₀ of the bladder and rectum were found to be multiple linearly correlated with the relevant OP and corresponding dose-volume optimization parameters of NOS (regression R ² > 0.99, P < 0.001). The variations of these models were less than 0.5% for bladder and rectum. Percentage of bladder and rectum within the PTV and the dose-volume optimization parameters of NOS could be used to predict the dose quantitatively. The parameters of NOS as a limited condition could be used in the plan optimization instead of limiting the dose and volume of the entire OAR traditionally, which made the plan optimization more unified and convenient and strengthened the plan quality and consistency.

Algorithm for an automatic treatment planning system using a single-arc VMAT for prostate cancer

Optimization process in treatment planning for intensity‐modulated radiation therapy varies with the treatment planner. Therefore, a large variation in the quality of dose distribution is usually observed. To reduce variation, an automatic optimizing toolkit was developed for the Monaco treatment planning system (Elekta AB, Stockholm, Sweden) for prostate cancer using volumetric‐modulated arc therapy (VMAT). This toolkit was able to create plans automatically. However, most plans needed two arcs per treatment to ensure the dose coverage for targets. For prostate cancer, providing a plan with a single arc was advisable in clinical practice because intrafraction motion management must be considered to irradiate accurately. The purpose of this work was to develop an automatic treatment planning system with a single arc per treatment for prostate cancer using VMAT. We designed the new algorithm for the automatic treatment planning system to use one arc per treatment for prostate cancer in Monaco. We constructed the system in two main steps: (1) Determine suitable cost function parameters for each case before optimization, and (2) repeat the calculation and optimization until the conditions for dose indices are fulfilled. To evaluate clinical suitability, the plan quality between manual planning and the automatic planning system was compared. Our system created the plans automatically in all patients within a few iterations. Statistical differences between the plans were not observed for the target and organ at risk. It created the plans with no human input other than the initial template setting and system initiation. This system offers improved efficiency in running the treatment planning system and human resources while ensuring high‐quality outputs.

Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface

OBJECTIVE

Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front.

METHODS

Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM).

RESULTS

On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk.

CONCLUSION

Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners.

SIGNIFICANCE

Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.

Customization of a Model For Knowledge-Based Planning to Achieve Ideal Dose Distributions in Volume Modulated arc Therapy for Pancreatic Cancers

Purpose:

To evaluate customizing a knowledge-based planning (KBP) model using dosimetric analysis for volumetric modulated arc therapy for pancreatic cancer.

Materials and Methods:

The first model (M1) using 56 plans and the second model (M2) using 31 plans were created in the first 7 months of the study. The ratios of volume of both kidneys overlapping the expanded planning target volume to the total volume of both kidneys (V_overlap/V_whole) were calculated in all cases to customize M1. Regression lines were derived from V_overlap/V_whole and mean dose to both kidneys. The third model (M3) was created using 30 plans which data put them below the regression line. For validation, KBP was performed with the three models on 21 patients.

Results:

V₁₈ of the left kidney for M1 plans was 7.3% greater than for clinical plans. Dmean of the left kidney for M2 plans was 2.2% greater than for clinical plans. There was no significant difference between all kidney doses in M3 and clinical plans. Dmean of the left kidney for M2 plans was 2.2% greater than for clinical plans. Dmean to both kidneys did not differ significantly between the three models in validation plans with V_overlap/V_whole lower than average. In plans with larger than average volumes, the Dmean of validation plans created by M3 was significantly lower for both kidneys by 1.7 and 0.9 Gy than with M1 and M2, respectively.

Conclusions:

Selecting plans to register in a model by analyzing dosimetry and geometry is an effective means of improving the KBP model.

Fully automated noncoplanar radiation therapy treatment planning

PURPOSE

To perform fully automated noncoplanar (NC) treatment planning, we propose a method called NC-POPS to produce NC plans using the Pareto optimal projection search (POPS) algorithm.

METHODS

NC radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planner's active treatment planning time and remove inter-planner variability. Our NC-POPS algorithm extends the original POPS algorithm to the NC setting with potential applications to both intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). The proposed algorithm consists of two main parts: (1) NC beam angle optimization (BAO) and (2) fully automated inverse planning using the POPS algorithm.

RESULTS

We evaluate the performance of NC-POPS by comparing between various NC and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity.

CONCLUSIONS

Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of NC IMRT cases with the potential to substantially improve treatment planning workflow and plan quality.

Assessing the need for adaptive radiotherapy in head and neck cancer patients using an automatic planning tool

Background

Unbiased analysis of the impact of adaptive radiotherapy (ART) is necessary to evaluate dosimetric benefit and optimize clinics' workflows. The aim of the study was to assess the need for adaptive radiotherapy (ART) in head and neck (H&N) cancer patients using an automatic planning tool in a retrospective planning study.

Materials and methods

Thirty H&N patients treated with adaptive radiotherapy were analysed. Patients had a CT scan for treatment planning and a verification CT during treatment according to the clinic's protocol. Considering these images, three plans were retrospectively generated using the iCycle tool to simulate the scenarios with and without adaptation: 1) the optimized plan based on the planning CT; 2) the optimized plan based on the verification CT (ART-plan); 3) the plan obtained by considering treatment plan 1 re-calculated in the verification CT (non-ART plan). The dosimetric endpoints for both target volumes and OAR were compared between scenarios 2 and 3 and the SPIDERplan used to evaluate plan quality.

Results

The most significant impact of ART was found for the PTVs, which demonstrated decreased D98% in the non-ART plan. A general increase in the dose was observed for the OAR but only the spinal cord showed a statistical significance. The SPIDERplan analysis indicated an overall loss of plan quality in the absence of ART.

Conclusion

These results confirm the advantages of ART in H&N patients, especially for the coverage of target volumes. The usage of an automatic planning tool reduces planner-induced bias in the results, guaranteeing that the observed changes derive from the application of ART.

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

Background

In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle³ for full planning automation of VMAT prostate cancer treatments.

Material and Methods

Thirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans.

Results

For similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11–16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification.

Conclusions

The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.

Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Artificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.

Evaluation of treatment plan quality for head and neck IMRT: a multicenter study

Intensity-modulated radiotherapy (IMRT) treatment planning for head and neck cancer is challenging and complex due to many organs at risk (OAR) in this region. The experience and skills of planners may result in substantial variability of treatment plan quality. This study assessed the performance of IMRT planning in Malaysia and observed plan quality variation among participating centers. The computed tomography dataset containing contoured target volumes and OAR was provided to participating centers. This is to control variations in contouring the target volumes and OARs by oncologists. The planner at each center was instructed to complete the treatment plan based on clinical practice with a given prescription, and the plan was analyzed against the planning goals provided. The quality of completed treatment plans was analyzed using the plan quality index (PQI), in which a score of 0 indicated that all dose objectives and constraints were achieved. A total of 23 plans were received from all participating centers comprising 14 VMAT, 7 IMRT, and 2 tomotherapy plans. The PQI indexes of these plans ranged from 0 to 0.65, indicating a wide variation of plan quality nationwide. Results also reported 5 out of 21 plans achieved all dose objectives and constraints showing more professional training is needed for planners in Malaysia. Understanding of treatment planning system and computational physics could also help in improving the quality of treatment plans for IMRT delivery.

Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients With Asymmetric Network

The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric, able to preserve input information and to adapt the limitation of GPU memory. Squeeze and excitation (SE) units are used to improve the data-fitting ability. A loss function involving both the dose distribution and prescription dose as ground truth are designed. In the experiment, A-Net is separately trained and tested in the 50Gy and 60Gy dataset and most of the metrics A-Net achieve similar performance as HD-Unet and 3D-Unet, and some metrics slightly better. In the 50Gy-and-60Gy-combined dataset, most of the A-Net's metrics perform better than the other two. In conclusion, A-Net can accurately predict the IMRT dose distribution in the three datasets of 50Gy and 50Gy-and-60Gy-combined dataset.

Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer

Background and purpose

Treatment planning of radiotherapy is a time-consuming and planner dependent process that can be automated by dose prediction models. The purpose of this study was to evaluate the performance of two machine learning models for breast cancer radiotherapy before possible clinical implementation.

Materials and methods

An in-house developed model, based on U-net architecture, and a contextual atlas regression forest (cARF) model integrated in the treatment planning software were trained. Obtained dose distributions were mimicked to create clinically deliverable plans. For training and validation, 90 patients were used, 15 patients were used for testing. Treatment plans were scored on predefined evaluation criteria and percent errors with respect to clinical dose were calculated for doses to planning target volume (PTV) and organs at risk (OARs).

Results

The U-net plans before mimicking met all criteria for all patients, both models failed one evaluation criterion in three patients after mimicking. No significant differences (p < 0.05) were found between clinical and predicted U-net plans before mimicking. Doses to OARs in plans of both models differed significantly from clinical plans, but no clinically relevant differences were found. After mimicking, both models had a mean percent error within 1.5% for the average dose to PTV and OARs. The mean errors for maximum doses were higher, within 6.6%.

Conclusions

Differences between predicted doses to OARs of the models were small when compared to clinical plans, and not found to be clinically relevant. Both models show potential in automated treatment planning for breast cancer.

DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects

Purpose

A recurrent neural network (RNN) and its variants such as gated recurrent unit-based RNN (GRU-RNN) were found to be very suitable for dose-volume histogram (DVH) prediction in our previously published work. Using the dosimetric information generated by nonmodulated beams of different orientations, the GRU-RNN model was capable of accurate DVH prediction for nasopharyngeal carcinoma (NPC) treatment planning. On the basis of our previous work, we proposed an improved approach and aimed to further improve the DVH prediction accuracy as well as study the feasibility of applying the proposed method to relatively small-size patient data.

Methods

Eighty NPC volumetric modulated arc therapy (VMAT) plans with local IRB's approval in recent two years were retrospectively and randomly selected in this study. All these original plans were created using the Eclipse treatment planning system (V13.5, Varian Medical Systems, USA) with ≥95% of PGTVnx receiving the prescribed doses of 70 Gy, ≥95% of PGTVnd receiving 66 Gy, and ≥95% of PTV receiving 60 Gy. Among them, fifty plans were used to train the DVH prediction model, and the remaining were used for testing. On the basis of our previously published work, we simplified the 3-layer GRU-RNN model to a single-layer model and further trained every organ at risk (OAR) separately with an OAR-specific equivalent uniform dose- (EUD-) based loss function.

Results

The results of linear least squares regression obtained by the new proposed method showed the excellent agreements between the predictions and the original plans with the correlation coefficient r = 0.976 and 0.968 for EUD results and maximum dose results, respectively, and the coefficient r of our previously published method was 0.957 and 0.946, respectively. The Wilcoxon signed-rank test results between the proposed and the previous work showed that the proposed method could significantly improve the EUD prediction accuracy for the brainstem, spinal cord, and temporal lobes with a p value < 0.01.

Conclusions

The accuracy of DVH prediction achieved in different OARs showed the great improvements compared to the previous works, and more importantly, the effectiveness and robustness showed by the simplified GRU-RNN trained from relatively small-size DVH samples, fully demonstrated the feasibility of applying the proposed method to small-size patient data. Excellent agreements in both EUD results and maximum dose results between the predictions and original plans indicated the application prospect in a physically and biologically related (or a mixture of both) model for treatment planning.

Sparing Organs at Risk with Simultaneous Integrated Boost Volumetric Modulated Arc Therapy for Locally Advanced Non-Small Cell Lung Cancer: An Automatic Treatment Planning Study

Background

The technique of simultaneous integrated boost volumetric modulated arc therapy (SIB-VMAT) has been widely used in locally advanced non-small cell lung cancer; however, its dosimetric advantages are seldom reported. This study aimed to quantify dosimetric advantages of SIB-VMAT.

Methods

Forty patients with stage III non-small cell lung cancer in our hospital were retrospectively included. SIB-VMAT and conventional VMAT (C-VMAT) plans were generated for every patient using the automatic treatment planning system. A reduced dose was delivered to PTV in SIB-VAMT plans compared to C-VMAT plans (50.4Gy vs 60Gy). The prescribed dose was 50.4 Gy in 28 fractions to PTV and 59.92 Gy in 28 fractions to PGTV in SIB-VMAT plans, while 60 Gy in 30 fractions to PTV in C-VMAT plans. Dose-volume metrics of PTV, total lung, heart, esophagus and spinal cord were recorded. The quality score was used to evaluate organs at risk (OAR) protection for two type prescription plans.

Results

Conformal coverage of the targets (PGTV/PTV) by 95% of the prescription dose was well achieved in radiation plans. SIB-VMAT plans achieved significantly higher quality score than C-VMAT plans (Mean: 68.15±13.32 vs 49.15±13.35, P<0.001). More plans scored above sixty in SIB-VMAT group compared to C-VMAT group (72.5% vs 20%, P<0.001). Notable reductions in mean dose, V₃₀, V₄₀ and V₅₀ of total lung were observed in SIB-VMAT plans compared to C-VMAT plans, with median decreased proportions of 6.5%, 8.7%, 19.6% and 32.1%, respectively. Statistically significant decrease in heart V₃₀ and V₄₀ was also achieved in SIB-VMAT plans, with median decreased proportions of 26.1% and 38.8%. SIB-VMAT plans achieved significant reductions in the maximum doses to both esophagus and spinal cord. Patients with CTV/(GTV+GTVnd) ≥8.6 showed more notable decrease in total lung V₅₀ (median, 33.6% vs 28.8%, P=0.001) in SIB-VMAT plans compared to those with the ratio being less than 8.6.

Conclusion

SIB-VMAT technique could lead to a substantial sparing of normal organs, including lung, heart, esophagus and cord, mainly through reducing high and inter-median dose exposure. Patients with CTV/(GTV+GTVnd) ≥8.6 might benefit more from SIB-VMAT.

Influence of maximum MLC leaf speed on the quality of volumetric modulated arc therapy plans

Purpose

Maximum leaf speed is a configurable parameter of MLC in a treatment‐planning system. This study investigated the influence of MLC on the quality of VMAT plans.

Methods

Seven MLCs with different maximum leaf speeds (1.0, 1.5, 2.25, 3.5, 5.0, 7.5, and 10 cm/s) were configured for an accelerator in treatment‐planning system. Correspondingly, seven treatment plans, with the identical initial optimization parameter, were designed with the mdaccAutoPlan system. Six nasopharyngeal carcinoma (NPC) patients and nine rectal cancer patients were selected, representing complex and simple clinical circumstances. VMAT plan quality was evaluated with PlanIQ^TM software. The results were statistically analyzed with a one‐way analysis of variance (ANOVA) and pairwise comparison tests.

Results

The relative changes of plan scores achieved by the seven configured accelerators, with specific maximum MLC leaf speed (MMLS) for each patient, were studied. Two apparent trends of MMLS influence on VMAT plan scores were observed: Plan scores increased with MMLS; Plan scores increased rapidly when MMLS increased from 1 to 3.5, thus the relative change of plan score decreased in this MMLS range. The stationary point of maximum MLC speed (MMSSP) is defined, for the specific MMLS when the relative changes of plan scores is first <5%, as MMLS increases from 1.0 to 10. For rectal plans, MMSSPs were 2.25 for six patients and 3.5 for the other three patients. For NPC plans, MMSSPs were 3.5 for five patients and 2.25 for one patient.

Conclusion

This work indicates that MMLS directly influences VMAT plan quality in NPC cases and rectal cancer cases. VMAT plan quality improved conspicuously as MMLS increased from 1 to 3.5, VMAT plan quality with marginal improvement when MMLS is above 3.5.

Impact of dosimetric differences between CT and MRI derived target volumes for external beam cervical cancer radiotherapy

OBJECTIVES

The use of MRI is becoming more prevalent in cervical cancer external beam radiotherapy (RT). The aim of this study was to investigate the impact of dosimetric differences between CT and MRI-derived target volumes for cervical cancer external beam RT.

METHODS

An automated planning technique for volumetric modulated arc therapy was developed. Two automated planning plans were generated for 18 cervical cancer patients where planning target volumes (PTVs) were generated based on CT or MRI data alone. Dose metrics for planning target volumes and organs at risk (OARs) were compared to analyse any differences based on imaging modality.

RESULTS

All treatment plans were clinically acceptable. Bladder doses (V40) were lower in MRI-based plans (p = 0.04, 53.6 ± 17.2 % vs 60.3 ± 13.1 % for MRI vs CT, respectively). The maximum dose for left iliac crest showed lower doses in CT-based plans (p = 0.02, 47.8 ± 0.7 Gy vs 47.4 ± 0.4 Gy MRI vs CT, respectively). No significant differences were seen for other OARs.

CONCLUSIONS

The dosimetric differences of CT- and MRI-based contouring variability for this study was small. CT remains the standard imaging modality for volume delineation for these patients.

ADVANCES IN KNOWLEDGE

This is the first study to evaluate the dosimetric implications of imaging modality on target and OAR doses in cervical cancer external beam RT.

Two-Level Factorial Pre-TomoBreast Pilot Study of Tomotherapy and Conventional Radiotherapy in Breast Cancer: Post Hoc Utility of a Mean Absolute Dose Deviation Penalty Score

Background:

A 2-level factorial pilot study was conducted in 2007 just before starting a randomized clinical trial comparing tomotherapy and conventional radiotherapy (CR) to reduce cardiac and pulmonary adverse effects in breast cancer, considering tumor laterality (left/right), target volume (with/without nodal irradiation), surgery (tumorectomy/mastectomy), and patient position (prone/supine). The study was revisited using a penalty score based on the recently developed mean absolute dose deviation (MADD).

Methods:

Eight patients with a unique combination of laterality, nodal coverage, and surgery underwent dual tomotherapy and CR treatment planning in both prone and supine positions, providing 32 distinct combinations. The penalty score was applied using the weighted sum of the MADDs. The Lenth method for unreplicated 2-level factorial design was used in the analysis.

Results:

The Lenth analysis identified nodal irradiation as the active main effect penalizing the dosimetry by 1.14 Gy (P = 0.001). Other significant effects were left laterality (0.94 Gy), mastectomy (0.61 Gy), and interactions between left mastectomy (0.89 Gy) and prone mastectomy (0.71 Gy), with P-values between 0.005 and 0.05. Tomotherapy provided a small reduction in penalty (reduction of 0.54 Gy) through interaction with nodal irradiation (P = 0.080). Some effects approached significance with P-values > 0.05 and ≤ 0.10 for interactions of prone × mastectomy × left (0.60 Gy), nodal irradiation × mastectomy (0.59 Gy), and prone × left (0.55 Gy) and the main effect prone (0.52 Gy).

Conclusions:

The historical dosimetric analysis previously revealed the feasibility of tomotherapy, but a conclusion could not be made. The MADD-based score is promising, and a new analysis highlights the impact of factors and hierarchy of priorities that need to be addressed if major gains are to be attained.

Factors Associated With the Choice of Radiation Therapy Treatment Facility in Head and Neck Cancer

OBJECTIVE

To analyze the clinicodemographic characteristics and treatment outcomes of patients receiving postoperative radiation therapy (PORT) at a different treatment facility rather than the initial surgical facility for head and neck cancer.

STUDY DESIGN

Retrospective cohort analysis.

METHODS

Utilizing the National Cancer Data Base, 2004 to 2015, patients with a diagnosis of oral cavity/oropharyngeal, hypopharyngeal, and laryngeal squamous cell carcinoma were studied. Multivariate analysis was completed with multivariate regression and Cox proportional hazard model, and survival outcomes were examined using Kaplan-Meier analysis.

RESULTS

A total of 15,181 patients who had surgery for a head and neck cancer at an academic/research center were included in the study population. Of the study population, 4,890 (32.2%) patients completed PORT at a different treatment facility. Treatment at a different facility was more common among patients who were ≥65 years old, white, Medicare recipients, those with a greater distance between residence and surgical treatment facility, and with lower income within area of residence (each P < .05). Overall survival was worse in patients completing PORT at a different treatment facility versus at the institution where surgery was completed (61.9% vs. 66.4%; P = .002).

CONCLUSIONS

PORT at a different facility was more common in older individuals, Medicare recipients, those with greater distance to travel, and lower-income individuals. Completing PORT outside the hospital where surgery was performed was associated with inferior survival outcomes among head and neck cancer patients.

LEVEL OF EVIDENCE

3 Laryngoscope, 131:1019-1025, 2021.

Automated Radiation Treatment Planning for Cervical Cancer

The radiation treatment-planning process includes contouring, planning, and reviewing the final plan, and each component requires substantial time and effort from multiple experts. Automation of treatment planning can save time and reduce the cost of radiation treatment, and potentially provides more consistent and better quality plans. With the recent breakthroughs in computer hardware and artificial intelligence technology, automation methods for radiation treatment planning have achieved a clinically acceptable level of performance in general. At the same time, the automation process should be developed and evaluated independently for different disease sites and treatment techniques as they are unique from each other. In this article, we will discuss the current status of automated radiation treatment planning for cervical cancer for simple and complex plans and corresponding automated quality assurance methods. Furthermore, we will introduce Radiation Planning Assistant, a web-based system designed to fully automate treatment planning for cervical cancer and other treatment sites.

Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy

OBJECTIVES

Radiotherapy plan quality may vary considerably depending on planner's experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics.

METHODS

Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement.

RESULTS

The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements.

CONCLUSION

Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models.

ADVANCES IN KNOWLEDGE

In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.

Inter-planner variation in treatment-plan quality of plans created with a knowledge-based treatment planning system

PURPOSE

This study aimed to clarify the inter-planner variation of plan quality in knowledge-based plans created by nine planners.

METHODS

Five hypofractionated prostate-only (HPO) volumetric modulated arc therapy (VMAT) plans and five whole-pelvis (WP) VMAT plans were created by each planner using a knowledge-based planning (KBP) system. Nine planners were divided into three groups of three planners each: Senior, Junior, and Beginner. Single optimization with only priority modification for all objectives was performed to stay within the dose constraints. The coefficients of variation (CVs) for dosimetric parameters were evaluated, and a plan quality metric (PQM) was used to evaluate comprehensive plan quality.

RESULTS

Lower CVs (<0.05) were observed at dosimetric parameters in the planning target volume for both HPO and WP plans, while the CVs in the rectum and bladder for WP plans (<0.91) were greater than those for HPO plans (<0.17). The PQM values of HPO plans for Cases1-5 (average ± standard deviation) were 41.2 ± 7.1, 40.9 ± 5.6, and 39.9 ± 4.6 in the Senior, Junior, and Beginner groups, respectively. For the WP plans, the PQM values were 51.9 ± 6.3, 47.5 ± 4.3, and 40.0 ± 6.6, respectively. The number of clinically acceptable HPO and WP plans were 13/15 and 11/15 in the Senior group, 13/15 and 10/15 plans in the Junior group, and 8/15 and 2/15 plans in the Beginner group, respectively.

CONCLUSION

Inter-planner variation in the plan quality with RapidPlan remains, especially for the complicated VMAT plans, due to planners' heuristics.

Dosimetric evaluation of automatic and manual plans for early nasopharyngeal carcinoma to radiotherapy

To investigate dosimetric differences and plan qualities between manual plans and automatic plans for nasopharyngeal carcinoma (NPC) in early stage, and provide better options to maximize the benefits. Sixteen cases diagnosed with early NPC were retrospectively investigated. Conventional step and shoot IMRT with 7-fields and full arc volumetric-modulated arc therapy (VMAT) with double arcs were manually generated by experienced planners and automatically generated by Auto-Planning module in Pinnacle³ respectively, such as IMRT manual-planning (mIMRT), IMRT auto-planning (aIMRT), VMAT manual-planning (mVMAT), and VMAT auto-planning (aVMAT) for each patient. Target coverage, organs at risk sparing, monitor units, and planning times were compared and evaluated. All parameters of plans are able to fulfill International Commission on Radiation Units and Measurements repor (ICRU) 83 recommendations. Automatic plans are comparable or superior to manual plans without time-consuming planning process. The CI and HI for PTVs are better in aVMAT when compared with aIMRT and mVMAT, but those are similar between aIMRT and mVMAT. Automatic plans not only have superior dose homogeneity and conformity in PTVs, but also have better sparing for spinal cord or slightly reduce the doses received by other OARs, while the VMAT plans have better sparing for brain stem, especially the aVMAT plans. However, Dmax, V30, and V40 of brain stem are similar between aIMRT and mVMAT without significant difference. The monitor units and planning time for treatment plans have been significantly decreased through automatic planning technique. The automatic VMAT plan has greater clinical advantages and should be recommended to a better option for treating NPC in early stage, while automatic IMRT would be preferentially considered instead of manual VMAT.

Evaluation of auto-planning in IMRT and VMAT for head and neck cancer

PURPOSE

The purposes of this work are to (a) investigate whether the use of auto-planning and multiple iterations improves quality of head and neck (HN) radiotherapy plans; (b) determine whether delivery methods such as step-and-shoot (SS) and volumetric modulated arc therapy (VMAT) impact plan quality; (c) report on the observations of plan quality predictions of a commercial feasibility tool.

MATERIALS AND METHODS

Twenty HN cases were retrospectively selected from our clinical database for this study. The first ten plans were used to test setting up planning goals and other optimization parameters in the auto-planning module. Subsequently, the other ten plans were replanned with auto-planning using step-and-shoot (AP-SS) and VMAT (AP-VMAT) delivery methods. Dosimetric endpoints were compared between the clinical plans and the corresponding AP-SS and AP-VMAT plans. Finally, predicted dosimetric endpoints from a commercial program were assessed.

RESULTS

All AP-SS and AP-VMAT plans met the clinical dose constraints. With auto-planning, the dose coverage of the low dose planning target volume (PTV) was improved while the dose coverage of the high dose PTV was maintained. Compared to the clinical plans, the doses to critical organs, such as the brainstem, parotid, larynx, esophagus, and oral cavity were significantly reduced in the AP-VMAT (P < 0.05); the AP-SS plans had similar homogeneity indices (HI) and conformality indices (CI) and the AP-VMAT plans had comparable HI and improved CI. Good agreement in dosimetric endpoints between predictions and AP-VMAT plans were observed in five of seven critical organs.

CONCLUSION

With improved planning quality and efficiency, auto-planning module is an effective tool to enable planners to generate HN IMRT plans that are meeting institution specific planning protocols. DVH prediction is feasible in improving workflow and plan quality.