Intensity-modulated Radiation Therapy Treatment Research Articles

The multiple-set split feasibility problem and its application in intensity-modulated radiation therapy

In this paper, we apply a parallel iterative method for solving the split common fixed point problem in Hilbert spaces, without prior knowledge of the transfer operator norm, to the multiple-set split feasibility problem, which plays an important role in medical image reconstruction and signal processing. By formulating a model of the intensity-modulated radiation therapy treatment in a mathematical framework based on the multiple-set split feasibility problem, we also offer an effective approach for solving the intensity-modulated radiation therapy treatment planning.

A deep learning-based dose prediction method for evaluation of radiotherapy treatment planning

[Purpose]To address the issue of low accuracy in dose distribution prediction in radiotherapy, we propose a deep learning-based model for predicting three-dimensional dose distribution in tumor radiation therapy. The model utilizes quantitative evaluation methods to assess the treatment plans. [Methods]We selected a dataset of 130 cervical cancer patients, including CT images and target region files. A deep learning U-Net model based on convolutional neural networks and residual blocks was employed to automatically extract multi-scale and multi-level feature maps of CT images, target regions, and anatomical structures of critical organs for intensity-modulated radiation therapy (IMRT) treatment plans and perform three-dimensional dose distribution prediction. Quantitative analysis methods, including error measures such as maximum dose (Dmax), mean dose (Dmean), V20, and D95, were used. [Results]For cervical cancer cases, DVH (dose-volume histogram) graphs were generated based on the evaluation results, directly reflecting the differences between the actual and predicted doses. The actual errors met the basic requirements, and a quantitative evaluation approach was used to optimize the dosimetric parameters. The specific quantification results are: DSC: 86.52 ± 9.31, 95% HD: 3.74 ± 1.49, JD: 0.112 ± 0.026, MSD: 0.067 ± 0.031. [Conclusion]Through training the deep learning model, we have successfully captured the complex nonlinear relationship between IMRT plan feature map parameters and three-dimensional dose distribution. In practical clinical applications, this trained model can accurately predict personalized three-dimensional dose distribution for new patients and effectively assess treatment plans in a quantitative manner. The source code is available at: https://github.com/xiebw9509/Radiotherapy_dose_prediction.

Treatment planning comparison for head and neck cancer between photon, proton, and combined proton–photon therapy – From a fixed beam line to an arc

Background and purposeThis study investigates whether combined proton–photon therapy (CPPT) improves treatment plan quality compared to single-modality intensity-modulated radiation therapy (IMRT) or intensity-modulated proton therapy (IMPT) for head and neck cancer (HNC) patients. Different proton beam arrangements for CPPT and IMPT are compared, which could be of specific interest concerning potential future upright-positioned treatments. Furthermore, it is evaluated if CPPT benefits remain under inter-fractional anatomical changes for HNC treatments. Material and methodsFive HNC patients with a planning CT and multiple (4–7) repeated CTs were studied. CPPT with simultaneously optimized photon and proton fluence, single-modality IMPT, and IMRT treatment plans were optimized on the planning CT and then recalculated and reoptimized on each repeated CT. For CPPT and IMPT, plans with different degrees of freedom for the proton beams were optimized. Fixed horizontal proton beam line (FHB), gantry-like, and arc-like plans were compared. ResultsThe target coverage for CPPT without adaptation is insufficient (average V95%=88.4 %), while adapted plans can recover the initial treatment plan quality for target (average V95%=95.5 %) and organs-at-risk. CPPT with increased proton beam flexibility increases plan quality and reduces normal tissue complication probability of Xerostomia and Dysphagia. On average, Xerostomia NTCP reductions compared to IMRT are −2.7 %/-3.4 %/-5.0 % for CPPT FHB/CPPT Gantry/CPPT Arc. The differences for IMPT FHB/IMPT Gantry/IMPT Arc are + 0.8 %/-0.9 %/-4.3 %. ConclusionCPPT for HNC needs adaptive treatments. Increasing proton beam flexibility in CPPT, either by using a gantry or an upright-positioned patient, improves treatment plan quality. However, the photon component is substantially reduced, therefore, the balance between improved plan quality and costs must be further determined.

Generalisation of radiotherapy dose calculation for Monte Carlo algorithm combined with 3D Swin-Unet: a multi-institutional IMRT evaluation

Objective. Accurate dose calculations are essential prerequisites for precise radiotherapy. The integration of deep learning into dosimetry could consider computational accuracy and efficiency and has potential applicability to clinical dose calculation. The generalisation of a deep learning dose calculation method (hereinafter referred to as TERMA-Monte Carlo network, T-MC net) was evaluated in clinical practice using intensity-modulated radiotherapy (IMRT) plans for various human body regions and multiple institutions, with the Monte Carlo (MC) algorithm serving as a benchmark. Approach. Sixty IMRT plans were selected from four institutions for testing the head and neck, chest and abdomen, and pelvis regions. Using the MC results as the benchmark, the T-MC net calculation results were used to perform three-dimensional dose distribution and dose-volume histogram (DVH) comparisons of the entire body, planning target volume (PTV) and organs at risk (OARs), respectively, and calculate the mean ±95% confidence interval of gamma pass rate (GPR), percentage of agreement (PA) and dose difference ratio (DDR) of dose indices D95, D50, and D5. Main results. For the entire body, the GPRs of 3%/3 mm, 2%/2 mm, 2%/1 mm, and the PA were 99.62 ± 0.32%, 98.50 ± 1.09%, 95.60 ± 2.90% and 97.80 ± 1.12%, respectively. For the PTV, the GPRs of 3%/3 mm, 2%/2 mm, 2%/1 mm and the PA were 98.90 ± 1.00%, 95.78 ± 2.83%, 92.23 ± 4.74% and 98.93 ± 0.62%, respectively. The absolute value of average DDR was less than 1.4%. Significance. We proposed a general dose calculation framework based on deep learning, using the MC algorithm as a benchmark, performing a generalisation test for IMRT treatment plans across multiple institutions. The framework provides high computational speed while maintaining the accuracy of MC and may become an effective dose algorithm engine in treatment planning, adaptive radiotherapy, and dose verification.

Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.

We apply the superiorization methodology to the constrained intensity-modulated radiation therapy (IMRT) treatment planning problem. Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking. Within the open-source inverse planning toolkit matRad, we implement a prototypical algorithmic framework for superiorization using the well-established Agmon, Motzkin, and Schoenberg (AMS) feasibility-seeking projection algorithm and common nonlinear dose optimization objective functions. Based on this prototype, we apply superiorization to intensity-modulated radiation therapy treatment planning and compare it with (i) bare feasibility-seeking (i.e., without any objective function) and (ii) nonlinear constrained optimization using first-order derivatives. For these comparisons, we use the TG119 water phantom, the head-and-neck and the prostate patient of the CORT dataset. Bare feasibility-seeking with AMS confirms previous studies, showing it can find solutions that are nearly equivalent to those found by the established piece-wise least-squares optimization approach. The superiorization prototype solved the linearly constrained planning problem with similar dosimetric performance to that of a general-purpose nonlinear constrained optimizer while showing smooth convergence in both constraint proximity and objective function reduction. Superiorization is a useful alternative to constrained optimization in radiotherapy inverse treatment planning. Future extensions with other approaches to feasibility-seeking, e.g., with dose-volume constraints and more sophisticated perturbations, may unlock its full potential for high performant inverse treatment planning.

Flash Therapy for Cancer: A Potentially New Radiotherapy Methodology.

In traditional treatment modalities and standard clinical practices, FLASH radiotherapy (FL-RT) administers radiation therapy at an exceptionally high dosage rate. When compared to standard dose rate radiation therapy, numerous preclinical investigations have demonstrated that FL-RT provides similar benefits in conserving normal tissue while maintaining equal antitumor efficacy, a phenomenon possible due to the 'FLASH effect' (FE) of FL-RT. The methodologies involve proton radiotherapy, intensity-modulated radiation treatment, and managing high-throughput damage by radiation to solid tissues. Recent results from animal studies indicate that FL-RT can reduce radiation-induced tissue damage, significantly enhancing anticancer potency. Focusing on the potential benefits of FL proton beam treatment in the years to come, this review details the FL-RT research that has been done so far and the existing theories illuminating the FL effects. This subject remains of interest, with many issues still needing to be answered. We offer a brief review to emphasize a few of the key efforts and difficulties in moving FL radiation research forward. The existing research state of FL-RT, its affecting variables, and its different specific impacts are presented in this current review. Key topics discussed include the biochemical mechanism during FL therapy, beam sources for FL therapy, the FL effect on immunity, clinical and preclinical studies on the protective effect of FL therapy, and parameters for effective FL therapy.

Novel hybrid treatment planning approach for irradiation a pediatric craniospinal axis

This study presents a new treatment planning approach merging 3D-CRT and VMAT fields into a hybrid treatment plan (HybTP), in order to achieve an optimum dose coverage of the planning target volume (PTV) and protection of OAR. Craniospinal axis irradiation (CSI) treated with 3D conformal radiotherapy (3D-CRT) is associated with high doses to the heart and eye lenses but provides better sparing of lungs and kidneys compared to volumetric modulated arc therapy (VMAT). VMAT treatment spares eye lenses and the heart, but lungs and kidneys are not as effective as 3D-CRT. Thus, a combination of both techniques (HybTP) may be optimal in sparing all these organs at risk (OAR). The results of HybTP are compared with helical tomotherapy (HT), intensity modulated radio therapy (IMRT), VMAT, and 3D-CRT plans. Hybrid, HT, VMAT, IMRT, and 3D-CRT treatment plans for a male child (age 6 years) with medulloblastoma were created and compared. A total dose of 35.2 Gy (PTV) with a dose per fraction of 1.6 Gy was prescribed. The following dose acceptance criteria were defined:(1)Mean PTV dose should be 100% of the prescribed dose, while the maximum dose should not exceed 107%.(2)At least 98% of the PTV should receive 95% of the prescribed dose.(3)The cribriform plate should be covered by the 95% isodose line.(4)The acceptance criteria for the OARs were: lenses Dmax<10 Gy, lungs Dmean<7 Gy, kidneys Dmean<15 Gy, heart Dmean<26 Gy, and heart V25<10%.The plans were compared regarding dose homogeneity index (HI) and conformity index (CI), PTV coverage, (particularly at cribriform plate) and doses at OARs. Best conformity was achieved with HT (CI = 0.98) followed by VMAT (CI = 0.96), IMRT (CI = 0.91), HybTP (CI = 0.86), and 3D-CRT (CI = 0.83). The homogeneity index varied marginally. For both HT and IMRT the HI was 0.07, and for 3D-CRT, VMAT and HybTP the HI was between 0.13 and 0.15. The cribriform plate was sufficiently covered by HybTP, VMAT, and 3D-CRT. The dose acceptance criteria for OARs were met by HT and HybTP. VMAT did not meet the criteria for lung (Dmean = right 10.4 Gy/left 10.2 Gy), 3D-CRT did not meet the criteria for eye lenses (Dmax = right 32.3 Gy/left 33.1), and heart (V25≈44%) and IMRT did not meet the criteria for lung (Dmean = right 11.1 Gy/left 11.2 Gy) and eye lenses (Dmax = right 12.2 Gy/left 13.1). HybTP meets all defined acceptance criteria and has proved to be a reasonable alternative for CSI. With HybTP that combines VMAT at the brain and heart with 3D-CRT posterior spinal fields (to spare lungs and kidneys), both appropriate coverage of the PTV and sparing of OAR can be achieved.

Sensitivity and specificity of Monte Carlo based independent secondary dose computation for detecting modulation-related dose errors in intensity modulated radiotherapy

BackgroundThe recent availability of Monte Carlo based independent secondary dose calculation (ISDC) for patient-specific quality assurance (QA) of modulated radiotherapy requires the definition of appropriate, more sensitive action levels, since contemporary recommendations were defined for less accurate ISDC dose algorithms. PurposeThe objective is to establish an optimum action level and measure the efficacy of a Monte Carlo ISDC software for pre-treatment QA of intensity modulated radiotherapy treatments. MethodsThe treatment planning system and the ISDC were commissioned by their vendors from independent base data sets, replicating a typical real-world scenario. In order to apply Receiver-Operator-Characteristics (ROC), a set of treatment plans for various case classes was created that consisted of 190 clinical treatment plans and 190 manipulated treatment plans with dose errors in the range of 1.5–2.5%. All 380 treatment plans were evaluated with ISDC in the patient geometry. ROC analysis was performed for a number of Gamma (dose-difference/distance-to-agreement) criteria. QA methods were ranked according to Area under the ROC curve (AUC) and optimum action levels were derived via Youden’s J statistics. ResultsOverall, for original treatment plans, the mean Gamma pass rate (GPR) for Gamma(1%, 1 mm) was close to 90%, although with some variation across case classes. The best QA criterion was Gamma(2%, 1 mm) with GPR > 90% and an AUC of 0.928. Gamma criteria with small distance-to-agreement had consistently higher AUC. GPR of original treatment plans depended on their modulation degree. An action level in terms of Gamma(1%, 1 mm) GPR that decreases with modulation degree was the most efficient criterion with sensitivity = 0.91 and specificity = 0.95, compared with Gamma(3%, 3 mm) GPR > 99%, sensitivity = 0.73 and specificity = 0.91 as a commonly used action level. ConclusionsISDC with Monte Carlo proves highly efficient to catch errors in the treatment planning process. For a Monte Carlo based TPS, dose-difference criteria of 2% or less, and distance-to-agreement criteria of 1 mm, achieve the largest AUC in ROC analysis

Evaluation of Peer Review Practice in Radiation Oncology Treatment Planning: Single Institution Experience

Pretreatment Peer review meeting at our radiation oncology department is a weekly meeting, where the initial radiation treatment (RT) planning process for every patient is discussed including: Computed Tomography (CT) simulation and treatment planning. In this study, we are aiming to evaluate our peer review process and to report the compliance to the suggested modifications. All RT plans for all patients indicated for radiotherapy accepted by the primary radiation oncology team then presented in our weekly peer review meeting for final approval. A minimum of two radiation oncologists reviewed each plan and their suggestions documented then suggested modifications are classified as changes in: field arrangement, clinical target volume (CTV), dose and fractionation. Compliance to suggested modification also recorded as: applied and not applied with or without documented justification. Atotal of 1779 radiation treatment plans were discussed in our regular weekly peer review meetings for a period of 3 years. 67.5% of them were treated with radical intent while 32.5% treated with palliative intent. The most common technique used was three-dimensional conformal (3DCRT) followed by intensity modulated RT (IMRT) (70.6% and 29.4%, respectively). The most frequently reviewed sites were: breast (38%), Gastrointestinal (11.9%) and head and neck (9.3%).1592 plans (89.48%) were labeled as: approved, 162 (9.1%) labeled as: modified and only 25 (1.4%) plans labeled as: hold. The frequency of modifications was significantly greater in breast (39.5%) than Gastrointestinal (17.9%) followed by gynecological cancers (14.2%) (P value <0.001). Modifications were more predominant with radical plans (64.2%) than palliative ones (35.8%) (P value <0.001) and statistically greater using 3DCRT (64.2%) than IMRT (35.8%) (P value <0.001). CTV recorded as the most frequent variable suggested for modifications (34.6%), followed by dose and fractionation (24.7%) (P value = 0.035). 72.2% of recommended modifications were applied by the consultant of primary team and 22.2% of non-applied modifications were justified and documented. There was 100% compliance rate to all plans labeled as hold. The practiceof peer review in radiation oncology specialty is very challenging. Its role in quality assurance of the radiotherapy process is highly accepted by radiation oncologists at our radiation oncology department. It is reflected in their high compliance rate to the suggested modifications. We recommend its implementation routinely at any radiation oncology department to enhance the quality of RT plans.

Voxel Based Analysis of Local Dose Patterns on Radiation Induced Xerostomia in Head and Neck Cancer with Machine Learning Methods

Despite the technological advancements, xerostomia caused by the irradiation of parotid and/or submandibular glands significantly impacts a patient's quality of life and remains a clinical challenge. The mean dose within the salivary glands that neglects the local dose effect is an oversimplified predictor thus its efficacy is severely limited. This study aims to uncover the local dose patterns associated with the xerostomia by performing a voxel-based analysis of the spatial parotid dose. A cohort of 143 patients who underwent intensity modulated radiation therapy treatment were retrospectively selected for this study. A binary classification was adopted for predictive modeling: group 1 (102 patients) if grade 0 or 1 and group 2 (41 patients) if grade 2 or 3. The anatomy of the parotid gland from the computed tomography (CT) of these patients were standardized to that of a reference patient with rigid and multistage B-spline deformable registration. The resultant deformation field vectors were subsequently utilized to warp the parotid doses and dose gradients to the reference ones. To facilitate pattern recognition, the bilateral parotid doses and dose gradients were further regrouped into contra- and ipsi-lateral depending on their proximity to the tumor targets. Four supervised machine learning (ML) models including ReliefF, L1 regularized logistic regression (L1LR), ridge regression (RR), and random forest (RF) were employed to determine the feature importance of each voxel and discover the dose patterns within the glands that are correlated with xerostomia. The predictive performance of each model was evaluated with the area under the curves (AUC) of the receiver operating characteristic. The image registration accuracy of an average of 95.6% for the parotid gland standardization on CT images was achieved. The four ML models were assessed with 10-fold cross validation showing comparable performance and the AUC scores were 0.74 ± 0.09, 0.75 ± 0.08, 0.73 ± 0.10, and 0.77 ± 0.09 for ReliefF, L1LR, RR, and RF, respectively. The importance patterns showed that the doses to the superior and medial region of the contralateral parotid gland were more correlated with xerostomia from the ReliefF model but not others. Similar importance patterns were observed from the dose gradient distributions along the left-right and anterior-posterior directions. The voxel-based model, which takes into account the spatial information of dose and dose gradient within the parotid gland, demonstrated an improvement in xerostomia predictive modeling as compared to the mean dose predictor, particularly in the local dose effect on xerostomia. If these findings are validated with more patient data and independent studies, they could be incorporated into treatment planning of head and neck patients to reduce the toxicity occurrence.

First-Year Experience of IMRT/SBRT Treatments Using a Novel Biology-Guided Radiation Therapy System

This study presents the first-year experience of treating patients using intensity-modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) with the X1 system, the first biology-guided radiation therapy (BgRT) machine installed in a clinical setting. A total of 78 patients underwent IMRT and SBRT treatments on the X1 system from May 2021 to May 2022. Clinical and technical data, such as treatment sites, number of pre-treatments kVCT scans, beam on time, patient setup time, imaging time per kVCT, and couch shifts after kVCT match, were collected and analyzed. Additionally, daily machine output stability, patient-specific quality assurance (QA) results, machine uptime, and user survey were also documented and reported. The most commonly treated site was the head and neck (63%), followed by the pelvis (23%), thorax (6%), and abdomen (8%). All treatments, except for 5 pelvis patients (6%) who received SBRT treatments for bony metastases, were conventionally fractionated IMRT (CF IMRT). The average number of kVCT scans per fraction is 1.2 ± 0.5 for all treatments. The average beam on time in minutes was 9.2 ± 3.5 for all treatments, 8.4 ± 2.4 for head and neck, 6.7 ± 1.3 for thorax, 10.3 ± 1.6 for abdomen, 11.6 ± 5.1 for CF IMRT pelvis, and 10.8 ± 5.3 for SBRT pelvis. The average patient setup time and imaging time per kVCT was 4.8 ± 2.6 minutes and 4.6 ± 1.5 minutes, respectively. The average couch corrections based on kVCT images were 0.4 ± 4.4 mm, 1.0 ± 4.5 mm, and 1.3 ± 4.3 mm along the x, y, and z direction, respectively; the average couch rotation corrections were 0.1 ± 0.9° for pitch, 0.0 ± 0.9° for roll, and 0.2 ± 1.2° for yaw. The daily machine output was 0.4 ± 1.2% from the baseline. The patient QA had a gamma passing rate of 97.4 ± 2.8%. The machine uptime was 92% of the total treatment time. The kVCT image quality and daily QA process received the highest level of satisfaction, while the treatment workflow for therapists received the lowest level of satisfaction (table 1). At one year after the installation of the X1 system, this study reports successful treatment of 78 patients using IMRT/ SBRT. With the recent FDA clearance of BgRT, our institution is preparing to treat patients using PET-guidance via a new product release, which should address deficiencies in the current IGRT workflow.

Application of the Integrated Deep Reinforcement Learning Solution in Automatic IMRT Planning for Gastric Cancer

The intensity-modulated radiation therapy (IMRT) treatment planning optimization process is usually a manual, tedious and time-consuming task. An unsupervised integrated automatic IMRT planning solution was discussed and implemented to simulate the manual operation during the whole planning process. Based on knowledge-based planning (KBP) and deep reinforcement learning (DRL) schemes, a novel integrated solution combining the multi-objectives optimization policy network (MOPN) and three-dimensional dose prediction module (3D-DPM) was proposed. The MOPN was trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS). The 3D-DPM was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOPN training. The Eclipse 15.6 TPS Scripting Application Programming Interface (ESAPI) was used to realize the automatic interaction between models and TPS. In this study, 100 previously treated gastric cancer cases were selected from the clinical database, 70 cases were used for the training of dose prediction model and MOPN, the remaining 30 cases for evaluating the feasibility and effectiveness of automatic treatment planning solution. For all tested cases, the complete automatic treatment plan for a new case was generated based on the integrated solution, with about 6 min. Compared with the MOPN initial plans, the actual dose of spinal cord, liver, kidney-left, kidney-right, and intestine in the MOPN final plans reduced 42.4%, 25.2%, 52.4%, 37.8%, 11.8% respectively. The dose result of OARs in the MOPN final plans was similar to those in the clinical plans. In addition, a new case was able to complete optimization with about 15 adjustment steps by the trained MOPN. We successfully developed an integrated solution for automatic treatment planning. It first includes the dose prediction module for obtaining patient-specific initial optimization objectives. We demonstrated that the trained MOPN can mimic the operations of the physicians to adjust multiple objectives and obtain a high-quality plan in a shorter time. This integrated solution contributes to improving the efficiency of the overall planning workflow and reducing the variation of plan quality in clinical practice. Although improvement is warranted, the proposed integrated solution is a promising practical and effective approach for automatic planning in commercial TPS.

Combined Radiotherapy Using a Method for Biologically Informed IMRT Planning

Radiotherapy (RT) regimens routinely employ multiple courses that vary by delivery modality and/or fractionation schedule as part of initial or follow up treatment. The combination of RTs ideally should consider the biological effect of the total composite dose distribution. Biological effect can be quantified by biological effective dose (BED) which is a function of the RT modality, dose fractionation, and tissue radiosensitivity. This work describes a method of optimizing a follow up intensity modulated RT (IMRT) plan that considers BED from prior RT. Required inputs for this method are the desired composite BED objectives (BEDref) for target volume(s) and organs at risk (OARs). First, the physical dose in each CT voxel from prior RT (Dprior) is converted to BED (BEDprior). The difference between BEDref and BEDprior gives the optimal BED distribution for the follow up IMRT plan (BEDIMRT). Next, the optimal physical dose (DIMRT) corresponding to BEDIMRT is calculated for each voxel as a function of the number of IMRT fractions (nIMRT). Commercial IMRT treatment planning systems require optimization objectives to be specified for contoured structures, not individual voxels, so a workaround is proposed: 1) for each structure, BEDref is converted to physical dose (Dopti) as a function of nIMRT, and 2) for each voxel, the difference of Dopti and DIMRT is calculated. The result is a "base dose distribution" (Dbase) that is used with standard inverse optimization tools to optimize an IMRT plan so that the sum of Dbase and the IMRT dose approaches Dopti for each voxel. This method was implemented using a custom software workflow to calculate Dbase as a function of Dprior and input BEDref values. Validation was performed by comparing the workflow output with hand calculated Dbase values at randomly selected points within clinically significant regions. Satisfactory agreement was considered to be within 5%. Feasibility of the method was demonstrated using an RT trial protocol for glioblastoma multiforme that combines permanent low dose rate brachytherapy (BT) using collagen tiles embedded with 131Cs seeds and follow-up IMRT (20 fractions). The Dbase distribution was obtained from the software as a function of the BT dose and BEDref for one patient model. Comparison of Dbase values from the software and hand calculated values resulted in agreement within 5% for 22/25 points and within 10% for 3/25 points. Discrepancies >5% (max 9.4%) were attributed to data precision resolution effects inherent in the DICOM standard. The resulting IMRT plan was deemed clinically acceptable and the composite dose distribution achieved dosimetric objectives for all target and OARs. A method for optimizing a follow up IMRT plan that considers dose from prior RT to produce a desired composite BED distribution was derived and proof of concept was successfully demonstrated. Further validation of this method with additional patients and multimodality RT protocols will be performed.

Late Toxicity and Patient Satisfaction in Hypofractionated Post-Mastectomy Radiotherapy after Immediate-Autologous Reconstruction

Immediate autologous reconstruction (IAR) is increasingly being used in the context of breast cancer patients (BCP) treated with mastectomy with the need for subsequent radiotherapy treatment. Currently in clinical practice, despite the fact that conventional fractionated RT (CFRT) has been the standard schedule, hypofractionation schemes are becoming increasingly common. Although no results have been reported on tolerance, late toxicity and satisfaction with treatment in this setting. Our aim was to evaluate late side-effects and patient´s satisfaction receiving PMRT after IAR, comparing CFRT vs HFRT. Between June 2015 and February 2020, 47 patients received either CF or HF PMRT after IAR in a tertiary hospital. IAR was performed with latissimus dorsi (LD) or deep inferior epigastric perforator (DIEP) flaps at the time of mastectomy. The prescribed dose was 50 Gy over 25 fractions in the CF schedule and 40.05 Gy over 15 fractions in the HF schedule. The target volume to be treated was the reconstructed chest-wall and the axillary-supraclavicular nodes if indicated, using intensity-modulated radiotherapy treatment technique. Late toxicity was evaluated using CTCAE v.5.0 criteria, and patient´s global satisfaction was assessed using a self-evaluation questionnaire, in which patients answered a series of selected questions related to radiation therapy from the BREAST-Q questionnaire, from 1-5 points. Median age was 48 years (28-77) and median follow-up was 51 months (27-99). 25 patients received HFRT and 22 CFRT. Late side effects after PMRT and tolerance until 3 years with both schedules are shown in Table 1. No relationship was found between incidence of late side effects and RT fractionation. Mean patient´s satisfaction was 3.8 in the CFRT schedule and 4 in the HFRT schedule. According to our findings, HFRT after IAR in post-mastectomy setting seems to be safe and well tolerated, with no high rates of severe late toxicity and with a good level of satisfaction of the treated patients. Table 1. Three years side effects of CRFT and HFRT radiotherapy after mastectomy and immediate autologous reconstruction in 47 patients.

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.

Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes.

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.

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

The goal of this study was to propose aknowledge-based planning system which could automatically design plans for lung cancer patients treated with intensity-modulated radiotherapy (IMRT). From May 2018 to June 2020, 612 IMRT treatment plans of lung cancer patients were retrospectively selected to construct aplanning database. Knowledge-based planning (KBP) architecture named αDiar was proposed in this study. It consisted of two parts separated by afirewall. 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. Asearch engine including athree-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 anew 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 96patients. 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; 26patients were involved in this study. 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. 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.

Characterization of the radiation beam of a tomotherapy equipment with MCNP

This work aims to model and characterize the radiation beam of one Accuray tomotherapy equipment using the Monte Carlo Code MCNP5 (Monte Carlo N-Particle). This tomotherapy equipment is used for delivering high doses of radiation in tumor regions to kill cancer cells and shrink the tumor during radiation therapy of cancer patients, however, the radiation can damage surrounding areas and nearby organs at risk (OAR) if the radiation field is not well delimited. In particular, intensity-modulated radiotherapy treatments (IMRT) with tomotherapy equipment offer great benefits to patients allowing treatment of tumor regions without affecting surrounding areas and OAR. Nowadays, it is well known that a correct simulation of transport of radiation in tomotherapy equipment facilitates considerably the estimation of ideal doses in the tumor, surrounding regions, and OAR. For that reason, in this work, we simulated the geometry of the 6 MV ACCURAY Tomotherapy equipment of the CECAN using the MCNP5. The model includes a TomoLINAC consisting of an electron source that emits Gaussian distribution particles with an average energy of 5.7 MeV and width of 0.3 MeV. The emitted particles impact the tungsten target and pass through primary collimators and jaws that define the irradiation field in the isocenter. To validate the geometry and radiation transport in the TomoLINAC the curves of depth dose percentage (PDD) estimated by simulation and the curves measured experimentally were tuned. In the same way, the simulated transverse and longitudinal profiles were compared with the experimental results. In addition, a comparison between the qualities of the radiation beam characterized with MCNP and measured experimentally in CECAN showed a deviation of 1%. For the simulations, cylindrical detectors located inside a water phantom were considered and it was employed the tally *F8. A good agreement was observed between the PDD's curves obtained from the simulation and those measured experimentally for a field of 5 × 10 cm2 in the isocenter and SSD (distance from the source to the surface) of 85 cm. Also, the comparison between the simulated and experimental transverse profiles obtained at 1.5 cm, 10 cm and 15 cm depth with a radiation field of 5 × 40 cm2 showed very good agreement. The longitudinal profiles were estimated with the same depths as the transverse ones, but for each of them, the openings of the jaws were 5.0 cm, 2.5 cm and 1.0 cm in the longitudinal direction, which corresponds to the direction in which the patient's table moves. The comparison between the simulated and experimental longitudinal profiles showed good concordance too. Once the radiation beam of the ACCURAY tomotherapy equipment had been characterized, experimental dose measurements were made using a Cheese phantom and two A1SL ionization chambers. These results obtained experimentally were compared with those estimated with MCNP for a field of 5 × 40 cm2 at the isocenter and SAD of 85 cm and, it was concluded that both results were similar considering the regions of uncertainty. Finally, we must highlight that the modeling and characterization of the radiation beam of CECAN's ACCURAY tomotherapy equipment can be a key tool for dose estimations in different cancer treatment plans and future research.

A dosimetric comparative study following RTOG and ESTRO contouring guidelines for breast radiation therapy

PurposeTo compare the dosimetric parameters considering the Radiation Therapy Oncology Group (RTOG) and European Society for Radiotherapy and Oncology (ESTRO) guidelines for breast cancer radiotherapy. Two radiotherapy techniques, intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), were considered. Patients and methodsTwenty-eight patients with left-sided medially-located TanyN2M0 tumors were contoured based on RTOG and ESTRO guidelines. 9-field IMRT, 10-field IMRT, 11-field IMRT, and VMAT treatment plans were applied as radiotherapy (RT) techniques for both contouring sets. The dosimetric parameters of the RT plans were extracted and compared. ResultsComparing dose-volume histogram (DVH) parameters, equivalent uniform dose (EUD), and normal tissue complication probability (NTCP) of OARs across the contouring guidelines and considering each RT technique showed that the only significant differences were higher Dmax, Dmean, V30, and V45, EUD, and NTCP of the thyroid in all treatment modalities when the RTOG guideline had been adopted. Using the VMAT technique, PTV's EUD and the tumor control probability (TCP) were considerably higher when the ESTRO guideline was adopted. Moreover, the conformity index (CI) of VMAT plans was significantly higher when the ESTRO guideline was used. ConclusionUnless having higher doses to thyroid when the RTOG guideline was adopted, the doses to other organs-at-risk (OAR) were similar between the two considering guidelines. Moreover, except for higher EUD, TCP, and CI for VMAT when the ESTRO guideline was used, no other significant differences were obtained between dosimetric parameters of target volumes considering the RT techniques and contouring guidelines.

4D dosimetric-blood flow model: impact of prolonged fraction delivery times of IMRT on the dose to the circulating lymphocytes

To investigate the impact of prolonged fraction delivery of modern intensity-modulated radiotherapy (IMRT) on the accumulated dose to the circulating blood during the course of fractionated radiation therapy. We have developed a 4D dosimetric blood flow model (d-BFM) capable of continuously simulating the blood flow through the entire body of the cancer patient and scoring the accumulated dose to blood particles (BPs). We developed a semi-automatic approach that enables us to map the tortuous blood vessels of the surficial brain of individual patients directly from standard magnetic resonance imaging data of the patient. For the rest of the body, we developed a fully-fledged dynamic blood flow transfer model according to the International Commission on Radiological Protection human reference. We proposed a methodology enabling us to design a personalized d-BFM, such it can be tailored for individual patients by adopting intra- and inter-subject variations. The entire circulatory model tracks over 43 million BPs and has a time resolution of = 10−3 s. A dynamic dose delivery model was implemented to emulate the spatial and temporal time-varying pattern of the dose rate during the step-and-shoot mode of IMRT. We evaluated how different configurations of the dose rate delivery, and a time prolongation of fraction delivery may impact the dose received by the circulating blood (CB).Our calculations indicate that prolonging the fraction treatment time from 7 to 18 min will augment the blood volume receiving any dose from 36.1% to 81.5% during one single fraction. The results indicate that increasing the segment number has only a negligible effect on the irradiated blood volume, when the fraction time is kept identical. We developed a novel concept of customized 4D d-BFM that can be tailored to the hemodynamics of individual patients to quantify dose to the CB during fractionated radiotherapy. The prolonged fraction delivery and the variability of the instantaneous dose rate have a significant impact on the accumulated dose distribution during IMRT treatments. This impact should be considered during IMRT treatments design to reduce RT-induced immunosuppressive effects.