Radiation Therapy Treatment Planning Research Articles

Initial Report of a Randomized Trial Comparing Conventional vs. Novel Treatment Planning Technique to Ameliorate Immunosuppression from Lung SBRT

SBRT is highly effective against early-stage non-small cell lung cancer. Radiation Therapy (RT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. However, this anti-tumor activity is offset by radiation-induced immunosuppression (RIIS) which results in lower tumor control and survival. Lymphocytes are highly radiosensitive and RIIS means destroying existing as well as newly created cytotoxic and helper T lymphocytes. We hypothesized that optimizing RT treatment planning by considering circulating blood and lymphatics as a critical Organ at Risk (OAR) may mitigate RIIS. We conducted an IRB approved NCI funded clinical trial for 50 early-stage lung cancer patients treated with SBRT alone, from 2020 to 2023, to investigate the ability to reduce RIIS by reducing dose to circulating blood and lymphatics with the aid of a predictive algorithm. All SBRT plans adhered to treatment parameters from RTOG 0813 (central) or RTOG 0915 (peripheral). Patients were randomized to two arms: experimental optimization for RIIS (to reduce dose to blood and lymphatic rich organs) versus standard SBRT planning (without optimization for RIIS). Peripheral blood samples were collected at baseline, end of Tx, 4 weeks and 6 months post Tx. Patients with baseline absolute lymphocyte counts (ALC) less than 0.5x109 cells/L were ineligible for the trial. Data acquired for all blood cell types as well as lymphocyte sub populations CD3+, CD4+, CD8+, CD19+, CD56+. Two sample t-test was used to determine the statistical significance between the cohorts at the time points. The standard arm had an ALC reduction of 28% at one week post Tx and a nadir at 4 weeks with a 34% reduction. Absolute percentage reductions in ALC from baseline in the optimized arm compared to the standard arm are: end of treatment point (13%, p = 0.03), 4 weeks (12%, p = 0.08), 6 months (15%, p = 0.1), and all three time points together 13% (p = 0.001). ALC recovery appears to be faster in the optimized arm. Radiation induced suppression of all blood cell types are also reduced in the optimized arm with respect to standard arm (relative percentages): ALC (34%), WBC (47%), RBC (46%), platelets (40%), monocytes (100%), and neutrophils (62%) at 4-week mark. Average percentage reductions on integral doses, and V5 (volume receiving a 5 Gy dose) of optimized compared to standard plans are: aorta: 26%, 41% heart: 8%, 33%, vena cava: 32%, 52%, T spine: 51%, 81%, lymph nodes: 35%, 57%, total lung- ITV: 1.6%, 1%, body: 10%, 14%. For the first time, we have shown that it is possible to reduce RIIS in a statistically significant manner, compared to standard of care, via optimized RT planning using a predictive model. This has implications in increasing the efficacy of immunotherapy by preserving the existing tumor reactive T cells in the immune system to enhance anti-tumor activity, and in reducing hospitalizations and improving survival.

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied. This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning. A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2). The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47Gy for bladder D50% ; 0.62Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p<0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7Gy), rectum (1.0Gy), and small intestine (0.6Gy), while maintaining PTV homogeneity and conformity. FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.

A predicted three-dimensional dose sequence based treatment planning optimization method for gynecologic IMRT.

In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses. This objective was used to reformulate a traditional fluence map optimization model, which involves a tolerable spatial re-assignment of the predicted dose distribution to the OAR voxels based on their current doses' positions at a sorted dose sequencing. The feasibility of this method was evaluated with ten gynecology (GYN) cancer IMRT cases by comparing its generated plan quality with the original clinical plan. Results showed feasible plan by proposed method, with comparable planning target volume (PTV) dose coverage and greater dose sparing of the OARs. Among ten GYN cases, the average V30 and V45 of rectum were decreased by 4%±4% (p=0.02) and 4%±3% (p<0.01), respectively. V30 and V45 of bladder were decreased by 8%±2% (p<0.01) and 3%±2% (p<0.01), respectively. Our predicted dose sequence-based planning optimization method for GYN IMRT offered a flexible use of predicted 3D doses while ensuring the output plan consistency.

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT

BackgroundCurrent intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively.MethodsWe proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body.ResultsThe proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose.ConclusionWe proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.

The use of image synthesis techniques in target and roi delineation in the upright position.

The use of multi-modality imaging technologies such as CT, MRI, and PET imaging is state of the art for radiation therapy treatment planning. Except for a limited number of low magnetic field MR scanners the majority of such imaging technologies can only image the patient in a recumbent position. Delivering radiation therapy treatments with the patient in an upright orientation has many benefits and several companies are now developing upright patient positioners combined with upright diagnostic helical CT scanners to facilitate upright radiation therapy treatments. Due to the directional changes in the gravitational forces on the patient's body, most structures and organs will change position and shape between the recumbent and upright positions. Detailed knowledge about such structures and organs are therefore often only available in the recumbent position. The problem statement is therefore well defined, that is, how do we know where such structures and organs, that is, the target or region at risk volumes, are in the upright position if those cannot be identified and or delineated accurately enough using the upright diagnostic quality CT images only? This paper outlines two methods based on synthetic CT or MR images to overcome this problem.

Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain.

Advancements in deep-learning based synthetic computed tomography (sCT) image conversion methods have enabled the development of magnetic resonance imaging (MRI)-only based radiotherapy treatment planning (RTP) of the brain. This study evaluates the clinical feasibility of a commercial, deep-learning based MRI-only RTP method with respect to dose calculation and patient positioning verification performance in RTP of the brain. Clinical validation of dose calculation accuracy was performed by a retrospective evaluation for 25 glioma and 25 brain metastasis patients. Dosimetric and image quality of the studied MRI-only RTP method was evaluated by a direct comparison of the sCT-based and computed tomography (CT)-based external beam radiation therapy (EBRT) images and treatment plans. Patient positioning verification accuracy of sCT images was evaluated retrospectively for 10 glioma and 10 brain metastasis patients based on clinical cone-beam computed tomography (CBCT) imaging. An average mean dose difference of Dmean = 0.1% for planning target volume (PTV) and 0.6% for normal tissue (NT) structures were obtained for glioma patients. Respective results for brain metastasis patients were Dmean = 0.5% for PTVs and Dmean =1.0% for NTs. Global three-dimensional (3D) gamma pass rates using 2%/2 mm dose difference and distance-to-agreement (DTA) criterion were 98.0% for the glioma subgroup, and 95.2% for the brain metastasis subgroup using 1%/1 mm criterion. Mean distance differences of <1.0 mm were observed in all Cartesian directions between CT-based and sCT-based CBCT patient positioning in both subgroups. In terms of dose calculation and patient positioning accuracy, the studied MRI-only method demonstrated its clinical feasibility for RTP of the brain. The results encourage the use of the studied method as part of a routine clinical workflow.

Detection of suboptimal IMRT treatment plan using machine learning on radiomics features of dose distribution for lung cancers

Quality assurance in radiotherapy is an important process so that the use of radiation provides maximum benefits. Currently, the implementation of machine learning (ML) in the quality assurance of treatment planning is growing. In this study, 34 optimal intensity-modulated radiation therapy (IMRT) treatment plans and 10 suboptimal IMRT treatment plans obtained from Siloam MRCCC Semanggi Hospital were used to train an ML model of the type autoencoder for anomaly detection developed using PyTorch. There were four stages in this study, namely the preparation stage, development stage, validation stage, and evaluation stage. At the development stage, the raw data was prepared so that it is ready to be used for training. At the development stage, the model was developed and a hyperparameters optimization was performed. The accuracy of the model was analyzed at the validation stage. Finally, at the evaluation stage, the model performance was evaluated along with a Mann-Whitney U test on dose-volume histogram (DVH) parameters, radiomics features, and DVH metrics (conformity index and homogeneity index) to show the difference between treatment plans. The model used 161 radiomics features with an epoch of 1250 iterations, 150-50-17 hidden layers configuration, and a learning rate of 0.2 as the most optimal configuration. The results showed an accuracy of 36% with 7% of radiomics features, 50% of DVH parameters, and the homogeneity index being different significantly. After refinement, that is removing data with conformity index below one, the accuracy became 50% with 12% of radiomics features, 45% of DVH parameters, and both DVH metrics being different significantly. If the radiomics features used are those that were significantly different, the accuracy increased to 93%. From these results, it can be concluded that most radiomics features are noise that need to be removed so the model can detect suboptimal treatment plans. In addition, the planning target volume segment along with the firstorder radiomics features group is the main differentiator between the optimal and suboptimal treatment plans.

Scorecards: Quantifying Dosimetric Plan Quality in Pancreatic Ductal Adenocarcinoma Stereotactic Body Radiation Therapy

A scoring mechanism called the scorecard that objectively quantifies the dosimetric plan quality of pancreas stereotactic body radiation therapy treatment plans is introduced. A retrospective analysis of patients with pancreatic ductal adenocarcinoma receiving stereotactic body radiation therapy at our institution between November 2019 and November 2020 was performed. Ten patients were identified. All patients were treated to 36 Gy in 5 fractions, and organs at risk (OARs) were constrained based on Alliance A021501. The scorecard awarded points for OAR doses lower than those cited in Alliance A021501. A team of 3 treatment planners and 2 radiation oncologists, including a physician resident without plan optimization experience, discussed the relative importance of the goals of the treatment plan and added additional metrics for OARs and plan quality indexes to create a more rigorous scoring mechanism. The scorecard for this study consisted of 42 metrics, each with a unique piecewise linear scoring function which is summed to calculate the total score (maximum possible score of 365). The scorecard-guided plan, the planning and optimization for which were done exclusively by the physician resident with no prior plan optimization experience, was compared with the clinical plan, the planning and optimization for which were done by expert dosimetrists, using the Sign test. Scorecard-guided plans had, on average, higher total scores than those clinically delivered for each patient, averaging 280.1 for plans clinically delivered and 311.7 for plans made using the scorecard (P=.003). Additionally, for most metrics, the average score of each metric across all 10 patients was higher for scorecard-guided plans than for clinically delivered plans. The scorecard guided the planner toward higher coverage, conformality, and OAR sparing. A scorecard tool can help clarify the goals of a treatment plan and provide an objective method for comparing the results of different plans. Our study suggests that a completely novice treatment planner can use a scorecard to create treatment plans with enhanced coverage, conformality, and improved OAR sparing, which may have significant effects on both tumor control and toxicity. These tools, including the scorecard used in this study, have been made freely available.

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.

Evaluation of commercial devices for patient specific QA of stereotactic radiotherapy plans.

Stereotactic radiotherapy (SRT) methods have become common for the treatment of small tumors in various parts of the body. Small field dosimetry has a unique set of challenges when it comes to the pre-treatment validation of a radiotherapy plan that involves film dosimetry or high-resolution detectors. Comparison of commercial quality assurance (QA) devices to the film dosimetry method for pre-treatment evaluation of stereotactic radiosurgery (SRS), fractionated SRT, and stereotactic body radiation therapy treatment plans have been evaluated in this study. Forty stereotactic QA plans were measured using EBT-XD film, IBA Matrixx Resolution, SNC ArcCHECK, Varian aS1200 EPID, SNC SRS MapCHECK, and IBA myQA SRS. The results of the commercial devices are compared to the EBT-XD film dosimetry results for each gamma criteria. Treatment plan characteristics such as modulation factor and target volume were investigated for correlation with the passing rates. It was found that all detectors have greater than 95% passing rates at 3%/3mm. Passing rates decrease rapidly for ArcCHECK and the Matrixx as criteria became more strict. In contrast, EBT-XD film, SNC SRS MapCHECK, and IBA myQA SRS passing rates do not decline as rapidly when compared to Matrix Resolution, ArcCHECK, and the EPID. EBT-XD film, SNC SRS MapCHECK, and IBA myQA SRS maintain greater than 90% passing rate at 2%/1mm and greater than 80% at 1%/1mm. Additionally, the ability of these devices to detect changes in dose distribution due to MLC positioning errors was investigated. Ten VMAT SBRT/SRS treatment plans were created with 6MV FFF or 10MV FFF beam energies using Eclipse 15.6. A MATLAB script was used to create two MLC positioning error scenarios from the original treatment plan. It was found that errors in MLC positioning were most reliably detected at 2%/1mm for high-resolution detectors and that lower-resolution detectors did not consistently detect MLC positioning errors.

Coverage of Lateral Lymph Nodes in Rectal Cancer Patients with Routine Radiation Therapy Practice and Associated Locoregional Recurrence Rates

Involved internal iliac and obturator lateral lymph nodes (LLNs) are a known risk factor for the occurrence of ipsilateral local recurrences (LLR) in rectal cancer. This study examined coverage of LLNs with routine radiation therapy practice in the Netherlands and associated LLR rates. Patients with a primary tumor ≤8 cm of the anorectal junction, cT3-4 stage, and at least 1 internal iliac or obturator LLN with short axis ≥5 mm who received neoadjuvant (chemo)radiation therapy, were selected from a national, cross-sectional study of patients with rectal cancer treated in the Netherlands in 2016. Magnetic resonance images and radiation therapy treatment plans were reviewed regarding segmented LLNs as gross tumor volume (GTV), location of LLNs within clinical target volume (CTV), and received proportion of the planned radiation therapy dose. A total of 223 out of 3057 patients with at least 1 LLN ≥5 mm were selected. Of those, 180 (80.7%) LLNs were inside the CTV, of which 60 (33.3%) were segmented as GTV. Overall, 202 LLNs (90.6%) received ≥95% of the planned dose. Four-year LLR rates were not significantly higher for LLNs situated outside the CTV compared with those inside (4.0% vs 12.5%, P=.092) or when receiving <95% versus ≥95% of the planned radiation therapy dose (7.1% vs 11.3%, P=.843), respectively. Two of 7 patients who received a dose escalation of 60 Gy developed an LLR (4-year LLR rate of 28.6%). This evaluation of routine radiation therapy practice showed that adequate coverage of LLNs was still associated with considerable 4-year LLR rates. Techniques resulting in better local control for patients with involved LLNs need to be explored further.

Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy

BackgroundProstate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade.MethodsPSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels.ResultsPerfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992.ConclusionsMachine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.

Glossary of Terms and Concepts in Medical Radiation Physics. Terminology Used in Treatment Planning of External Radiation Therapy

Medical radiation physics supports medical diagnostic and treatment process based on the use of ionizing radiation in radiation therapy, nuclear medicine, radiation diagnostics and radiation safety of patients and personnel. However, the successful implementation of this process requires close interaction of specialists in all the above areas. The basis of such interaction is the unification of the terminology and concepts used at various stages of the diagnostic and treatment process. The article presents a glossary on treatment planning of external radiation therapy which contains the most commonly used terms in this field and explanations for each of them. The glossary is intended for use both in professional education, including postgraduate education, and for medical physicists and radiation oncologists working in radiological medical organization

Robust quantification of CT-ventilation biomarker techniques and repeatability in a porcine model.

Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post-processing techniques. CT-ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT-ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remainingvariables. To characterize CT-ventilation biomarker repeatability and dependence on image acquisition and post-processing methodology in anesthetized and mechanically ventilatedpigs. Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four-dimensional CT (4DCT) and maximum inhale and exhale breath-hold CT (BH-CT) scans on five dates to generate CT-ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian-based post-processing techniques. measured local expansion between an image pair using either inhale and exhale BH-CT images or two 4DCT breathing phase images. measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra- and interday biomarker repeatability, image acquisition and post-processing technique dependence were quantitatively analyzed. Biomarkers showed strong agreement with voxel-wise Spearman correlation for intraday repeatability and for all other comparisons, including between image acquisition techniques. Intra- and interday repeatability were significantly different (p < 0.01). LER2 and LERN post-processing did not significantly affect intraday repeatability. 4DCT and BH-CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhumansubjects.

Assessment of Treatment Plan Quality between Flattening Filter and Flattening Filter Free Photon Beam for Carcinoma of the Esophagus with IMRT Technique.

As compared to the flattened photon beam, removing the flattening filter (FF) from the head of a gantry decreases the average energy of the photon beam and increases the dose rate, leading to an impact on the quality of treatment plans. This study aimed to compare the quality of intensity-modulated radiation therapy (IMRT) treatment plans for esophageal cancer with and without a flattened filter photon beam. In this analytical study, 12 patients, who had already been treated with a 6X FF photon beam, were treated based on new IMRT methods using a 6X the flattening filter-free (FFF) photon beam. Both 6X FF IMRT and 6X FFF IMRT plans used identical beam parameters and planning objectives. All plans were evaluated with planning indices and doses for organs at risk (OARs). Insignificant dose variation was for HI, CI, D98%, and V95% between FF and FFF photon beam IMRT plans. FF-based IMRT plan delivered a 15.51 % and 11.27% higher mean dose to both lungs and heart than the FFF plan, respectively. The integral dose (ID) for the heart and lungs was 11.21% and 15.51%, respectively, less in the IMRT plan with an FFF photon beam. In contrast to the FF photon beam, a filtered photon beam-oriented IMRT plan provides significant OAR sparing without losing the quality of the treatment plan. High monitor units (MUs), low ID, and Beam on Time (BOT) are major highlights of the IMRT plan with FFF beam.

Machine Learning for Predicting Clinician Evaluation of Treatment Plans for Left-Sided Whole Breast Radiation Therapy

The objective of this work was to investigate the ability of machine learning models to use treatment plan dosimetry for prediction of clinician approval of treatment plans (no further planning needed) for left-sided whole breast radiation therapy with boost. Investigated plans were generated to deliver a dose of 40.05 Gy to the whole breast in 15 fractions over 3 weeks, with the tumor bed simultaneously boosted to 48 Gy. In addition to the manually generated clinical plan of each of the 120 patients from a single institution, an automatically generated plan was included for each patient to enhance the number of study plans to 240. In random order, the treating clinician retrospectively scored all 240 plans as (1) approved without further planning to seek improvement or (2) further planning needed, while being blind for type of plan generation (manual or automated). In total, 2×5 classifiers were trained and evaluated for ability to correctly predict the clinician's plan evaluations: random forest (RF) and constrained logistic regression (LR) classifiers, each trained for 5 different sets of dosimetric plan parameters (feature sets [FS]). Importances of included features for predictions were investigated to better understand clinicians' choices. Although all 240 plans were in principle clinically acceptable for the clinician, only for 71.5% was no further planning required. For the most extensive FS, accuracy, area under the receiver operating characteristic curve, and Cohen's κ for generated RF/LR models for prediction of approval without further planning were 87.2 ± 2.0/86.7 ± 2.2, 0.80 ± 0.03/0.86 ± 0.02, and 0.63 ± 0.05/0.69 ± 0.04, respectively. In contrast to LR, RF performance was independent of the applied FS. For both RF and LR, whole breast excluding boost PTV (PTV40.05Gy) was the most important structure for predictions, with importance factors of 44.6% and 43%, respectively, dose recieved by 95% volume of PTV40.05 (D95%) as the most important parameter in most cases. The investigated use of machine learning to predict clinician approval of treatment plans is highly promising. Including nondosimetric parameters could further increase classifiers' performances. The tool could become useful for aiding treatment planners in generating plans with a high probability of being directly approved by the treating clinician.

High-dimensional automated radiation therapy treatment planning via Bayesian optimization.

Radiation therapy treatment planning can be viewed as an iterative hyperparameter tuning process to balance conflicting clinical goals. In this work, we investigated the performance of modern Bayesian optimization (BO) methods on automated treatment planning problems in high-dimensionalsettings. Twenty locally advanced rectal cancer patients treated with intensity-modulated radiation therapy (IMRT) were retrospectively selected as test cases. The adjustable planning parameters included both dose objectives and their corresponding weights. We implemented an automated treatment planning framework and tested the performance of two BO methods on the treatment planning task: one standard BO method (Gaussian Process with Expected Improvement [GPEI]) and one BO method dedicated to high-dimensional problems (Sparse Axis Aligned Subspace BO [SAAS-BO]). Another derivative-free method (Nelder-Mead simplex search) and the random tuning method were also included as baselines. The four automated methods' plan quality and planning efficiency were compared with the clinical plans regarding target coverage and organs at risk (OAR) sparing. The predictive models in both BO methods were compared to analyze the different search patterns of the two BOmethods. For the target structures, the SAAS-BO plans achieved comparable hot spot control ( ) and homogeneity ( ) with the clinical plans, significantly better than the GPEI and Nelder-Mead plans ( ). Both SAAS-BO and GPEI plans significantly outperformed the clinical plans in conformity and dose spillage ( ). Compared with the clinical plans, the treatment plans generated by the four automated methods all made reductions in evaluated dosimetric indices for the femoral head and the bladder. The Nelder-Mead plans achieved similar plan quality scores compared with the BO plans, but exhibited poorer control in the target hot spot and dose spillage. The analysis of the underlying predictive models has shown that both BO methods have identified similar sensitive planning parameters. This work implemented a BO-based hyperparameter tuning framework for automated treatment planning. Both tested BO methods were able to produce high-quality treatment plans and reduce the workload of treatment planners. The model analysis also confirmed the intrinsic low dimensionality of the tested treatment planningproblems.

Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning.

Accelerated partial breast irradiation (APBI) yields similar rates of recurrence and cosmetic outcomes as compared to whole breast radiation therapy (RT) when patients and treatment techniques are appropriately selected. APBI combined with stereotactic body radiation therapy (SBRT) is a promising technique for precisely delivering high levels of radiation while avoiding uninvolved breast tissue. Here we investigate the feasibility of automatically generating high quality APBI plans in the Ethos adaptive workspace with a specific emphasis on sparing the heart. Nine patients (10 target volumes) were utilized to iteratively tune an Ethos APBI planning template for automatic plan generation. Twenty patients previously treated on a TrueBeam Edge accelerator were then automatically replanned using this template without manual intervention or reoptimization. The unbiased validation cohort Ethos plans were benchmarked via adherence to planning objectives, a comparison of DVH and quality indices against the clinical Edge plans, and qualitative reviews by two board-certified radiation oncologists. 85% (17/20) of automated validation cohort plans met all planning objectives; three plans did not achieve the contralateral lung V1.5Gy objective, but all other objectives were achieved. Compared to the Eclipse generated plans, the proposed Ethos template generated plans with greater evaluation planning target volume (PTV_Eval) V100% coverage (p = 0.01), significantly decreased heart V1.5Gy (p< 0.001), and increased contralateral breast V5Gy, skin D0.01cc, and RTOG conformity index (p = 0.03, p = 0.03, and p = 0.01, respectively). However, only the reduction in heart dose was significant after correcting for multiple testing. Physicist-selected plans were deemed clinically acceptable without modification for 75% and 90% of plans by physicians A and B, respectively. Physicians A and B scored at least one automatically generated plan as clinically acceptable for 100% and 95% of planning intents, respectively. Standard left- and right-sided planning templates automatically generated APBI plans of comparable quality to manually generated plans treated on a stereotactic linear accelerator, with a significant reduction in heart dose compared to Eclipse generated plans. The methods presented in this work elucidate an approach for generating automated, cardiac-sparing APBI treatment plans for daily adaptive RT with high efficiency.

Automatic segmentation of kidneys in computed tomography images using U-Net

PurposeAccurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is biased to the clinician experience and subject to inter-observer variability. Therefore, and due to the development of artificial intelligence tools and particularly deep learning (DL) algorithms, automatic segmentation has been proposed as an alternative. The purpose of this work is to use a DL-based method to segment the kidneys on CT images for radiotherapy treatment planning. Materials and methodsIn this contribution, we used the CT scans of 20 patients. Segmentation of the kidneys was performed using the U-Net model. The Dice similarity coefficient (DSC), the Matthews correlation coefficient (MCC), the Hausdorff distance (HD), the sensitivity and the specificity were used to quantitatively evaluate this delineation. ResultsThis model was able to segment the organs with a good accuracy. The obtained values of the used metrics for the kidneys segmentation, were presented. Our results were also compared to those obtained recently by other authors. ConclusionFully automated DL-based segmentation of CT images has the potential to improve both the speed and the accuracy of radiotherapy organs contouring.

High-Grade Glioma Radiation Therapy and Reirradiation Treatment Planning Using Translocator Protein Positron Emission Tomography With 18F-GE-180

Translocator protein (TSPO) positron emission tomography (PET) using 18F-GE-180 shows high tumor-to-brain contrast in high-grade glioma (HGG), even in areas without magnetic resonance imaging (MRI) contrast enhancement. Until now, the benefit of 18F-GE-180 PET in primary radiation therapy (RT) and reirradiation (reRT) treatment planning for patients with HGG has not been assessed. The possible benefit of 18F-GE-180 PET in RT and reRT planning was retrospectively evaluated through post hoc spatial correlations of PET-based biological tumor volumes (BTVs) with conventional MRI-based consensus gross tumor volumes (cGTVs). To find the ideal threshold for BTV definition in RT and reRT treatment planning, tumor-to-background activity thresholds of 1.6, 1.8, and 2.0 were applied. Spatial overlap of PET- and MRI-based tumor volumes was measured by the Sørensen-Dice coefficient (SDC) and the conformity index (CI). Additionally, the minimal margin to include the entire BTV into the expanded cGTV was determined. Thirty-five primary RT and 16 reRT cases were examined. BTV1.6, BTV1.8, and BTV2.0 were significantly larger than corresponding cGTV volumes in primary RT (median volumes: 67.4, 50.7, and 39.1, respectively, vs 22.6 cm3; P < .001, P < .001, and P=.017, respectively; Wilcoxon test) and reRT cases (median volumes: 80.5, 55.0, and 41.6, respectively, vs 22.7 cm3; P=.001, P=.005, and P=.144, respectively; Wilcoxon test). BTV1.6, BTV1.8, and BTV2.0 showed low but increasing conformity with cGTVs in the primary RT (SDC: 0.51, 0.55, and 0.58, respectively; CI: 0.35, 0.38, and 0.41, respectively) and reRT setting (SDC: 0.38, 0.40, and 0.40, respectively; CI: 0.24, 0.25, and 0.25, respectively). The minimal margin required to include the BTV within the cGTV was significantly smaller in the RT versus the reRT setting for thresholds 1.6 and 1.8 but not significantly different for threshold 2.0 (median margin: 16, 12, and 10, respectively, vs 21.5, 17.5, and 13 mm, respectively; P=.007, P=.031, and P=.093, respectively; Mann-Whitney U test). 18F-GE-180 PET provides valuable information in RT treatment planning for patients with HGG. 18F-GE-180-based BTVs with a threshold of 2.0 were most consistent in primary and reRT.