Personalized automated treatment planning for breast plus locoregional lymph nodes using Hybrid RapidArc

Minimizing normal tissue low dose bath for left breast Volumetric Modulated Arc Therapy (VMAT) using jaw offset

PURPOSE

With proper beam setup and optimization constraints in the treatment planning system, volumetric modulated arc therapy (VMAT) can improve target dose coverage and conformity while reducing doses to adjacent structures for whole breast radiation therapy. However, the low-dose bath effect on critical structures, especially the heart and the ipsilateral lung, remains a concern. In this study, we present a VMAT technique with the jaw offset VMAT (JO-VMAT) to reduce the leakage and scatter doses to critical structures for whole breast radiation therapy.

MATERIALS AND METHODS

The data of 10 left breast cancer patients were retrospectively used for this study. CT images were acquired on a CT scanner (GE, Discovery) with the deep-inspiration breath hold (DIBH) technique. The planning target volumes (PTVs) and the normal structures (the lungs, the heart, and the contralateral breast) were contoured on the DIBH scan. A 3D field-in-field plan (3D-FiF), a tangential VMAT (tVMAT) plan, and a JO-VMAT plan were created with the Eclipse treatment planning system. An arc treatment field with the x-jaw closed across the central axis creates a donut-shaped high-dose distribution and a cylinder-shaped low-dose volume along the central axis of gantry rotation. Applying this setup with proper multi-leaf collimator (MLC) modulation, the optimized plan potentially can provide sufficient target coverage and reduce unnecessary irradiation to critical structures. The JO-VMAT plans involve 5-6 tangential arcs (3 clockwise arcs and 2-3 counterclockwise arcs) with jaw offsets. The plans were optimized with objective functions specified to achieve PTV dose coverage and homogeneity; For organs at risk (OARs), objective functions were specified individually for each patient to accomplish the best achievable treatment plan. For tVMAT plans, optimization constraints were kept the same except that the jaw offset was removed from the initial beam setup. The dose volume histogram (DVH) parameters were generated for dosimetric evaluation of PTV and OARs.

RESULTS

The D95% to the PTV was greater than the prescription dose of 42.56 Gy for all the plans. With both VMAT techniques, the PTV conformity index (CI) was statistically improved from 0.62 (3D-FiF) to 0.83 for tVMAT and 0.84 for JO-VMAT plans. The difference in the homogeneity index (HI) was not significant. The Dmax to the heart was reduced from 12.15 Gy for 3D-FiF to 8.26 Gy for tVMAT and 7.20 Gy for JO-VMAT plans. However, a low-dose bath effect was observed with tVMAT plans to all the critical structures including the lungs, the heart, and the contralateral breast. With JO-VMAT, the V5Gy and V2Gy of the heart were reduced by 32.7% and 15.4% compared to 3D-FiF plans. Significantly, the ipsilateral lung showed a reduction in mean dose (4.65-3.44 Gy) and low dose parameters (23.4% reduction for V5Gy and 10.7% reduction for V2Gy) for JO-VMAT plans compared to the 3D-FiF plans. The V2Gy dose to the contralateral lung and breast was minimal with JO-VMAT techniques.

CONCLUSION

A JO-VMAT technique was evaluated in this study and compared with 3D-FiF and tVMAT techniques. Our results showed that the JO-VMAT technique can achieve clinically comparable coverage and homogeneity and significantly improve dose conformity within PTV. Additionally, JO-VMAT eliminated the low-dose bath effect at all OARs evaluation metrics including the ipsilateral/contralateral lung, the heart, and the contralateral breast compared to 3D-FiF and tVMAT. This technique is feasible for the whole breast radiation therapy of left breast cancers.

Hybrid VMAT-3DCRT as breast cancer treatment improvement tool

Radiation therapy is an important tool in the treatment of breast cancer and can play a crucial role in improving patient outcomes. For breast cancer, if the technique has been for a long time the use of 3DCRT, clinicians have seen the management evolve greatly in recent years. Field-in-field and IMRT approaches and more recently dynamic arctherapy are increasingly available. All of these approaches are constantly trying to improve tumour coverage and to preserve organs at risk by minimising the doses delivered to them. If arctherapy allows a considerable reduction of high doses received by healthy tissues, no one can deny that it also leads to an increase of low doses in tissues that would not have received any with other techniques. We propose a hybrid approach combining the robustness of the 3DCRT approach and the high technicality and efficiency of arctherapy. Statistical tests (ANOVA, Wilcoxon, determination coefficient, ROC, etc.) allow us to draw conclusions about the possibility of using the hybrid approach in certain cases (right breast, BMI [Formula: see text], age [Formula: see text], target volume [Formula: see text] cc, etc.). Depending on the breast laterality and patients morphological characteristics, hybridization may prove to be a therapeutic tool of choice in the management of breast cancer in radiotherapy.

Fully automated volumetric modulated arc therapy technique for radiation therapy of locally advanced breast cancer

BACKGROUND

This study aimed to evaluate an a-priori multicriteria plan optimization algorithm (mCycle) for locally advanced breast cancer radiation therapy (RT) by comparing automatically generated VMAT (Volumetric Modulated Arc Therapy) plans (AP-VMAT) with manual clinical Helical Tomotherapy (HT) plans.

METHODS

The study included 25 patients who received postoperative RT using HT. The patient cohort had diverse target selections, including both left and right breast/chest wall (CW) and III-IV node, with or without internal mammary node (IMN) and Simultaneous Integrated Boost (SIB). The Planning Target Volume (PTV) was obtained by applying a 5 mm isotropic expansion to the CTV (Clinical Target Volume), with a 5 mm clip from the skin. Comparisons of dosimetric parameters and delivery/planning times were conducted. Dosimetric verification of the AP-VMAT plans was performed.

RESULTS

The study showed statistically significant improvements in AP-VMAT plans compared to HT for OARs (Organs At Risk) mean dose, except for the heart and ipsilateral lung. No significant differences in V95% were observed for PTV breast/CW and PTV III-IV, while increased coverage (higher V95%) was seen for PTV IMN in AP-VMAT plans. HT plans exhibited smaller values of PTV V105% for breast/CW and III-IV, with no differences in PTV IMN and boost. HT had an average (± standard deviation) delivery time of (17 ± 8) minutes, while AP-VMAT took (3 ± 1) minutes. The average γ passing rate for AP-VMAT plans was 97%±1%. Planning times reduced from an average of 6 h for HT to about 2 min for AP-VMAT.

CONCLUSIONS

Comparing AP-VMAT plans with clinical HT plans showed similar or improved quality. The implementation of mCycle demonstrated successful automation of the planning process for VMAT treatment of locally advanced breast cancer, significantly reducing workload.

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.

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

PURPOSE

In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics.

METHODS

A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices.

RESULTS

The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for theD m a x ${D_{max}}$ andD m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net.

CONCLUSION

All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.

Improving normal tissue sparing using scripting in endometrial cancer radiation therapy planning

The aim of this study was to improve the protection of organs at risk (OARs), decrease the total planning time and maintain sufficient target doses using scripting endometrial cancer external beam radiation therapy (EBRT) planning. Computed tomography (CT) data of 14 endometrial cancer patients were included in this study. Manual and automatic planning with scripting were performed for each CT. Scripts were created in the RayStation™ (RaySearch Laboratories AB, Stockholm, Sweden) planning system using a Python code. In scripting, seven additional contours were automatically created to reduce the OAR doses. The scripted and manual plans were compared to each other in terms of planning time, dose-volume histogram (DVH) parameters, and total monitor unit (MU) values. While the mean total planning time for manual planning was 368 ± 8 s, it was only 55 ± 2 s for the automatic planning with scripting (p < 0.001). The mean doses of OARs decreased with automatic planning (p < 0.001). In addition, the maximum doses (D2% and D1%) for bilateral femoral heads and the rectum were significantly reduced. It was observed that the total MU value increased from 1146 ± 126 (manual planning) to 1369 ± 95 (scripted planning). It is concluded that scripted planning has significant time and dosimetric advantages over manual planning for endometrial cancer EBRT planning.

Implementation of automated personalised breast radiotherapy planning techniques with scripting in Raystation

OBJECTIVE

Implement scripted automatic breast planning (AP) for breast techniques within Raystation.

METHODS

Manual plans (MPs) were re-planned and compared with AP plans for whole breast (WB), partial breast (PB), hybrid volumetric modulated arc therapy simultaneous integrated boost (VMAT SIB) and VMAT nodal plans.

RESULTS

WB AP plans took 7 min comparing well to MP. One WB AP failed a mandatory dose constraint. Small statistically significant differences showed improved coverage for AP at expense of slightly hotter plans, however absolute differences were small (mean differences < 1% or D _0.5cc<0.2 Gy). PB AP plans took 9 min, showing improved coverage (V _24.7Gy97.6 vs 96.4 %). One PB AP case failed a mandatory constraint. Other dosimetric differences were non-significant. SIB AP plans took 14 min with one case failing a mandatory constraint with minor differences compared with MP except larger V _42.8Gy (3 vs 1.5 %) and more MU. VMAT AP plans took 12 min and were hotter for PTVp_4000 but had higher nodal coverage. Contra_Lung V _2.5Gy was higher (8.8 %) than MP plans (6.5 %).

CONCLUSION

Automatic planning of modern breast techniques has been successfully introduced using a commercial planning system. AP plans are very similar to MP, requiring little manual interaction for most cases with significant timesaving potential.

ADVANCES IN KNOWLEDGE

Scripted breast plans produced within minutes for WB, PB, SIB and VMAT. Successfully introduced into large busy department. Plans similar to manual plans, requiring little manual interaction.

Improved treatment robustness of postoperative breast cancer radiotherapy including supraclavicular nodes

A combination of a three-dimensional conformal radiation therapy (3D-CRT) plan with a dose gradient of the chest wall area and a volumetric modulated arc therapy (VMAT) plan of the supraclavicular area might improve the dose distribution robustness in the junction. To investigate the impact of patient motion on the dose distribution, hybrid 3D-CRT and VMAT plans were recalculated by shifting the isocenter of the VMAT plan. Compared to the nominal plan, the target D_98% for high- vs low-dose gradients decreased by 24% vs 12%. Hybrid VMAT with a low-dose gradient 3D-CRT plan was found to be robust towards patient motion.

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning

Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default- and altered-angle KBPs, and the altered-angle plans showed a <1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 ± 1.8%. While the altered-angle plans saw greater benefit from AIO, both default- and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis.

Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer

PURPOSE

Deep learning-based knowledge-based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high-quality plans. This paper presents a novel KBP model using an attention-gating mechanism and a three-dimensional (3D) U-Net for intensity-modulated radiation therapy (IMRT) 3D dose distribution prediction in head-and-neck cancer.

METHODS

A total of 340 head-and-neck cancer plans, representing the OpenKBP-2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention-gated 3D U-Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean-squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U-Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning-based dose prediction models.

RESULTS

The proposed attention-gated 3D U-Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground-truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U-Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U-Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U-Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U-Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U-Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U-Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U-Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U-Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U-Net) in the body contour. The average difference in predicting the D₉₉ value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the D m a x ${D_{max}}$ (brainstem, spinal cord, and mandible) and D m e a n ${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground-truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground-truth plans. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for a new patient that is sufficient for real-time applications.

CONCLUSIONS

The attention-gated 3D U-Net model demonstrated a capability in predicting accurate 3D dose distributions for head-and-neck IMRT plans with consistent quality. The prediction performance of the proposed model was overall superior to a baseline standard U-Net model, and it was also competitive to the performance of the best state-of-the-art dose prediction method reported in the literature. The proposed model could be used to obtain dose distributions for decision-making before planning, quality assurance of planning, and guiding-automated planning for improved plan consistency, quality, and planning efficiency.

A pilot study of machine-learning based automated planning for primary brain tumours

Purpose

High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this population has not previously been studied.

Methods and materials

We developed a ML model that identifies learned relationships between image features and expected dose in a training set of 95 patients with a primary brain tumour treated with focal radiotherapy to a dose of 54 Gy in 30 fractions. This ML method was then used to create predicted dose distributions for 15 previously-treated brain tumour patients across two institutions, as a testing set. Dosimetry to target volumes and organs-at-risk (OARs) were compared between the clinically-delivered (human-generated) plans versus the ML plans.

Results

The ML method was able to create deliverable plans in all 15 patients in the testing set. All ML plans were generated within 30 min of initiating planning. Planning target volume coverage with 95% of the prescription dose was attained in all plans. OAR doses were similar across most structures evaluated; mean doses to brain and left temporal lobe were lower in ML plans than manual plans (mean difference to left temporal, – 2.3 Gy, p = 0.006; mean differences to brain, – 1.3 Gy, p = 0.017), whereas mean doses to right cochlea and lenses were higher in ML plans (+ 1.6–2.2 Gy, p < 0.05 for each).

Conclusions

Use of an automated ML method to aid RT planning for children and young adults with primary brain tumours is dosimetrically feasible and can be successfully used to create high-quality 54 Gy RT plans. Further evaluation after clinical implementation is planned.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01967-3.

Fast, automated knowledge-based treatment planning for selecting patients for proton therapy based on normal tissue complication probabilities

Purpose

Selecting patients who will benefit from proton therapy is laborious and subjective. We demonstrate a novel automated solution for creating high-quality knowledge-based plans (KBPs) using proton and photon beams to identify patients for proton treatment based on their normal tissue complication probabilities (NTCP).

Methods and Materials

Two previously validated RapidPlan PT models for locally advanced head and neck cancer were used in combination with scripting to automatically create proton and photon KBPs for 72 patients with recent oropharynx cancer. NTCPs were calculated for each patient based on the KBPs, and patient selection was simulated according to the current Dutch national protocol.

Results

The photon/proton KBP exhibited good correlation between predicted and achieved organ-at-risk mean doses, with a ≤5 Gy difference in 208/196 out of 215 structures relevant for the head and neck cancer NTCP model. The proton KBPs yielded on average 7.1/6.1/7.6 Gy lower dose to salivary/swallowing structures/oral cavity than the photon KBPs. This reduced average grade 2/3 dysphagia and xerostomia by 7.1/3.3 and 5.5/2.0 percentage points, resulting in 16 of 72 patients (22%) being indicated for proton treatment. The entire automated process took <30 minutes per patient.

Conclusions

Automated support for decision making using KBP is feasible and fast. The planning solution has potential to speed up the planning and patient-selection process significantly without major compromises to the plan quality.

Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer

Purpose:

To create and to validate knowledge-based volumetric modulated arc therapy (VMAT) models for breast cancer treatments without lymph node irradiation.

Materials and Methods:

One hundred VMAT-based breast plans (manual plans [MP]) were selected to create two knowledge-based VMAT models (breast left and breast right) using RapidPlan™. The plans were generated on Eclipse v15.5 (Varian Medical Systems, Palo Alto, CA) with 6 MV of a Novalis Tx equipped with a high-resolution multileaf collimator. The models were verified based on goodness-of-fit statistics using the coefficients of determination (R²) and Chi-square (χ²), and the goodness-of-estimation statistics through the mean square error (MSE). Geometrical and dosimetrical constraints were identified and removed from the RP models using statistical evaluation metrics and plots. For validation, 20 plans that integrate the models and 20 plans that do not were reoptimized with RP (closed and opened validation). Dosimetrical parameters of interest were used to compare MP versus RP plans for the Heart, Homolateral_Lung, Contralateral_Lung, and Contralateral_Breast. Optimization planning time and user independency were also analyzed.

Results:

The most unfavorable results of R² in both models for the organs at risk were as follows: for Contralateral_Lung 0.51 in RP right breast (RP_RB) and for Heart 0.60 in RP left breast (RP_LB). The most unfavorable results of χ² test were: for Contralateral_Breast 1.02 in RP_RB and for Heart 1.03 in RP_LB. These goodness-of-fit results show that no overfitting occurred in either of the models. There were no unfavorable results of mean square error (MSE, all < 0.05) in any of the two models. These goodness-of-estimation results show that the models have good estimation power. For closed validation, significant differences were found in RP_RB for Homolateral_Lung (all P ≤ 0.001), and in the RP_LB differences were found for the heart (all P ≤ 0.04) and for Homolateral_Lung (all P ≤ 0.022). For open validation, no statistically significant differences were obtained in either of the models. RP models had little impact on reducing optimization planning times for expert planners; nevertheless, the result showed a 30% reduction time for beginner planners. The use of RP models generates high-quality plans, without differences from the planner experience.

Conclusion:

Two RP models for breast cancer treatment using VMAT were successfully implemented. The use of RP models for breast cancer reduces the optimization planning time and improves the efficiency of the treatment planning process while ensuring high-quality plans.

Harmonization of breast cancer radiotherapy treatment planning in the Netherlands

PURPOSE

The aim was to reach consensus in The Netherlands on which parameters should be used to evaluate breast cancer radiotherapy (RT) plans.

MATERIALS AND METHODS

A Benchmark Case with delineated planning target volumes (PTVs) and Organs At Risk (OARs) was sent to all Dutch radiotherapy centres in combination with a questionnaire, with the request to generate RT plans prescribing 15 times 2.67 Gy for four different treatment indications according to the institutional irradiation technique. The plans and accompanying questionnaire answers were analysed using descriptive statistics. These results, together with a harmonisation proposal, were sent to all centres. The proposal was discussed at a meeting of the Dutch Society of Radiation Oncology breast cancer platform. Distinct parameters were accepted if consensus on them was reached.

RESULTS

19 out of 20 Dutch departments participated in this study. PTV coverage varied considerably, with D98% between 63% and 99% for the breast and between 37% and 97% for the internal mammary nodes (IMN). Also substantial OAR dose differences were observed, with e.g. mean heart doses ranging between 1.85 Gy and 5.42 Gy in case the IMN were included in the PTV. For evaluation of the PTVs D98%, D2% and Dmean were chosen to report on, with target values of ≥ 95% (90% for the PTV_IMN), ≤ 107%, and 99-101%, respectively. For OARs, consensus was reached on the parameters to be evaluated, without target values: Dmean of the heart, Dmean and V5% of the lungs, and in case of periclavicular radiotherapy V30Gy of the thyroid gland. For patients younger than 40 years a contralateral mean breast dose of ≤ 1 Gy was agreed upon.

CONCLUSION

A new Dutch consensus guideline for evaluation of breast cancer RT plans has been established.

Comprehensive nodal breast VMAT: solving the low-dose wash dilemma using an iterative knowledge-based radiotherapy planning solution

Introduction

Aimed to develop a simple and robust volumetric modulated arc radiotherapy (VMAT) solution for comprehensive lymph node (CLN) breast cancer without increase in low‐dose wash.

Methods

Forty CLN‐breast patient data sets were utilised to develop a knowledge‐based planning (KBP) VMAT model, which limits low‐dose wash using iterative learning and base‐tangential methods as benchmark. Another twenty data sets were employed to validate the model comparing KBP‐generated ipsilateral VMAT (ipsi‐VMAT) plans against the benchmarked hybrid (h)‐VMAT (departmental standard) and bowtie‐VMAT (published best practice) methods. Planning target volume (PTV), conformity/homogeneity index (CI/HI), organ‐at‐risk (OAR), remaining‐volume‐at‐risk (RVR) and blinded radiation oncologist (RO) plan preference were evaluated.

Results

Ipsi‐ and bowtie‐VMAT plans were dosimetrically equivalent, achieving greater nodal target coverage (P < 0.05) compared to h‐VMAT with minor reduction in breast coverage. CI was enhanced for a small reduction in breast HI with improved dose sparing to ipsilateral‐lung and humeral head (P < 0.05) at immaterial expense to spinal cord. Significantly, low‐dose wash to OARs and RVR were comparable between all plan types demonstrating a simple VMAT class solution robust to patient‐specific anatomic variation can be applied to CLN breast without need for complex beam modification (hybrid plans, avoidance sectors or other). This result was supported by blinded RO review.

Conclusions

A simple and robust ipsilateral VMAT class solution for CLN breast generated using iterative KBP modelling can achieve clinically acceptable target coverage and OAR sparing without unwanted increase in low‐dose wash associated with increased second malignancy risk.

Comparison of volumetric modulated arc therapy and intensity-modulated radiotherapy for left-sided whole-breast irradiation using automated planning

Background

Published treatment technique comparisons for postoperative left-sided whole breast irradiation (WBI) with deep-inspiration breath-hold (DIBH) are scarce, small, and inconclusive. In this study, fully automated multi-criterial plan optimization, generating a single high-quality, Pareto-optimal plan per patient and treatment technique, was used to compare for a large patient cohort 1) intensity modulated radiotherapy (IMRT) with two tangential fields and 2) volumetric modulated arc therapy (VMAT) with two small tangential subarcs.

Materials and methods

Forty-eight randomly selected patients recently treated with DIBH and 16 × 2.66 Gy were included. The optimizer was configured for the clinical planning protocol. Comparisons between IMRT and VMAT included dosimetric plan parameters, estimated excess relative risks (ERR) for toxicities, delivery times, MUs, and deliverability accuracy at a linac.

Results

The automatically generated IMRT and VMAT plans applied in this study were similar or higher in quality than the manually generated clinical plans. For equal PTVin V95% (98.4 ± 0.9%), VMAT had significant advantages compared to IMRT regarding breast dose homogeneity and doses in heart and ipsilateral lung, at the cost of some minor deteriorations for contralateral breast (few cases with larger deteriorations) and lung. Conformality improved from 1.38 to 1.18 (p < 0.001). With VMAT, ERR for major coronary events and ipsilateral lung tumors were reduced by 3% (range: −1–12%) and 16% (range: −3–38%), respectively. MUs and delivery times were higher for VMAT. There were no statistical differences in γ passing rates.

Conclusion

For WBI in conservative therapy of left-sided breast patients treated with DIBH, VMAT with two tangential subarcs was generally dosimetrically superior to IMRT with two tangential static fields. Results need confirmation by robustness analyses.

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.

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

PURPOSE

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

METHODS AND MATERIALS

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

RESULTS

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

CONCLUSIONS

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

Single click automated breast planning with iterative optimization

Purpose

To present the development of an in‐house coded solution for treatment planning of tangential breast radiotherapy that creates single click plans by emulating the iterative optimization process of human dosimetrists.

Method

One hundred clinical breast cancer patients were retrospectively planned with an automated planning (AP) code incorporating the hybrid intensity‐modulated radiotherapy (IMRT) approach. The code automates all planning processes including plan generation, beam generation, gantry and collimator angle determination, open segments and dynamic IMRT fluence and calculations. Thirty‐nine dose volume histogram (DVH) metrics taken from three international recommendations were compared between the automated and clinical plans (CP), along with median interquartile analysis of the DVH distributions. Total planning time and delivery QA were also compared between the plan sets.

Results

Of the 39 planning metrics analyzed 23 showed no significant difference between clinical and automated planning techniques. Of the 16 metrics with statistically significant variations, 2 were improved in the clinical plans in comparison to 14 improved in the AP plans. Automated plans produced a greater number of ideal plans against international guidelines as per EviQ (AP:77%, CP:68%), RTOG 1005 (AP:80%, CP:71%), and London Cancer references (AP:80%, CP:75%). Delivery QA results for both techniques were equivalent. Automated planning techniques resulted in an average reduction in planning time from 23 to 5 minutes.

Conclusion

We have introduced an automated planning code with iterative optimization that produces equivalent quality plans to manual clinical planning. The resultant change in workflow results in a reduction in treatment planning times.

Virtual Tangential-fields Arc Therapy (ViTAT) for whole breast irradiation: Technique optimization and validation

PURPOSE

To test the performances of a volumetric arc technique named ViTAT (Virtual Tangential-fields Arc Therapy) mimicking tangential field irradiation for whole breast radiotherapy.

METHODS

ViTAT plans consisted in 4 arcs whose starting/ending position were established based on gantry angle distribution of clinical plans for right and left-breast. The arcs were completely blocked excluding the first and last 20°. Different virtual bolus densities and thicknesses were preliminarily evaluated to obtain the best plan performances. For 40 patients with tumor laterality equally divided between right and left sides, ViTAT plans were optimized considering the clinical DVHs for OARs (resulting from tangential field manual planning) to constrain them: ViTAT plans were compared with the clinical tangential-fields in terms of DVH parameters for both PTV and OARs.

RESULTS

Distal angle values were suggested in the ranges [220°,240°] for the right-breast and [115°,135°] for the left-breast cases; medial angles were [60°,40°] for the right side and [295°,315°] for the left side, limiting the risk of collision. The optimal virtual bolus had -500 HU density and 1.5 cm thickness. ViTAT plans generated dose distributions very similar to the tangential-field plans, with significantly improved PTV homogeneity. The mean doses of ipsilateral OARs were comparable between the two techniques with minor increase of the low-dose spread in the range 2-15 Gy (few % volume); contralateral OARs were slightly better spared with ViTAT.

CONCLUSION

ViTAT dose distributions were similar to tangential-fields. ViTAT should allow automatic plan optimization by developing knowledge-based DVH prediction models of patients treated with tangential-fields.

Artificial intelligence in radiotherapy: a technological review

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.

Optimization of autogenerated chest-wall radiation treatment plans developed for postmastectomy breast cancer patients in underserved clinics

Over the past decade, several strides have been made to improve the management of breast cancer in developing countries; however, there are still obstacles present. In the area of radiation therapy, these hurdles include limited access to radiotherapy treatment and scarcity of oncology specialists. In an effort to reduce inequities in cancer care while improving patient outcomes, our research is focused on developing automated postmastectomy radiation therapy (PMRT) plans for breast cancer patients in these underserved communities that can be further improved upon through treatment planning system (TPS) specific optimization guidelines. The automated planning tool utilized algorithms integrated with Varian's Eclipse TPS. The tool created PMRT plans that used monoisocentric tangents and supraclavicular (SCV) fields with a mix of high and low energy photon beams along with field-in-field (FIF) segments. The completed autogenerated PMRT plans were imported into Phillip's Pinnacle 9.10 and Varian's Eclipse 13.6 TPSs to be further improved through manual optimization; the time required to complete this step was measured and assessed. A senior dosimetrist, physicist, and physician evaluated the optimized plans for clinical acceptability. Guidelines were developed for the planning systems that can be implemented by personnel with either limited experience in radiation treatment planning or those with limited time to produce treatment plans. The autogenerated plans in conjunction with our guidelines have shown to significantly reduce the time required to produce a clinically acceptable PMRT plan from approximately 120 ± 60 minutes to just 13 ± 11 (Pinnacle) and 12 ± 7 (Eclipse) minutes, reducing the total uninterrupted treatment planning time by an average of 108 ± 51 minutes. The results from this research indicate that the autogenerated PMRT plans along with the optimization guidelines are a viable option to provide quality and clinically acceptable PMRT plans that are more efficient and consistent for postmastectomy breast cancer patients in severely underserved communities.

Integration of biological factors in the treatment plan evaluation in breast cancer radiotherapy

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This study suggests a PQM methodology for breast cancer radiotherapy evaluation.

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The risk/benefit balance estimation includes tumor biology and smoking status.

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Smoking status influenced risk/benefit balance for different treatment techniques.

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Survival benefit dominated for all patients with high-risk breast cancer.

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Survival benefit for smokers with low- or intermediate- risk cancer was not seen.

Background/purpose

Tumor biology and patient smoking status have clear effects on the benefit of breast radiotherapy. This study developed treatment evaluation strategies that integrated dosimetry, tumor aggressiveness and smoking status for patients undergoing hypo-fractionated whole breast irradiation with simultaneous integrated boost.

Materials/methods

The evaluation method Plan Quality Metrics (PQM) was adapted for breast cancer. Radiotherapy (RT) benefit was assessed for three levels of tumor aggressiveness; RT risk was estimated using mean dose to organs at risk and published Excess Relative Risk per Gy data for lung cancer and cardiac mortality for smokers and non-smokers. Risk for contralateral breast cancer was also evaluated. PQM and benefit/risk was applied to four patient groups (n = 10 each). Plans using 3D conformal radiotherapy (3DCRT), 3DCRT plus intensity-modulated radiation therapy (IMRT), 3DCRT plus volumetric modulated arc therapy (VMAT) and VMAT were evaluated for each patient.

Results

3DCRT-IMRT hybrid planning resulted in higher PQM score (median 87.0 vs. 3DCRT 82.4, p < 0.01), better dose conformity, lower doses to the heart, lungs and contralateral breast. Survival benefit was most predominant for patients with high-risk breast cancer (>7% and >4.5% gain for non-smokers and smokers). For smokers with intermediate- or low-risk breast cancer, RT induced mortality risk dominated for all techniques. When considering the risk of local recurrence, RT benefitted also smokers (>5% and >2% for intermediate- and low-risk cancer).

Conclusions

PQM methodology was suggested for breast cancer radiotherapy evaluation. Further validation is needed. RT was beneficial for all patients with high risk of recurrence. A survival benefit for smokers with low or intermediate risk of recurrence could not be confirmed.