Intensity-modulated Radiotherapy Plans Research Articles

A robust treatment planning approach for chest motion in postmastectomy chest wall intensity modulated radiation therapy.

Chest wall postmastectomy radiation therapy (PMRT) should consider the effects of chest wall respiratory motion. The purpose of this study is to evaluate the effectiveness of robustness planning intensity modulated radiation therapy (IMRT) for respiratory movement, considering respiratory motion as a setup error. This study analyzed 20 patients who underwent PMRT (10 left and 10 right chest walls). The following three treatment plans were created for each case and compared. The treatment plans are a planning target volume (PTV) plan (PP) that covers the PTV within the body contour with the prescribed dose, a virtual bolus plan (VP) that sets a virtual bolus in contact with the body surface and prescribing the dose that includes the PTV outside the body contour, and a robust plan (RP) that considers respiratory movement as a setup uncertainty and performs robust optimization. The isocenter was shifted to reproduce the chest wall motion pattern and the doses were recalculated for comparison for each treatment plan. No significant difference was found between the PP and the RP in terms of the tumor dose in the treatment plan. In contrast, VP had 3.5% higher PTV Dmax and 5.5% lower PTV V95% than RP (p<0.001). The RP demonstrated significantly higher lung V20Gy and Dmean by 1.4% and 0.4Gy, respectively, than the PP. The RP showed smaller changes in dose distribution affected by chest wall motion and significantly higher tumor dose coverage than the PP and VP. We revealed that the RP demonstrated comparable tumor doses to the PP in treatment planning and was robust for respiratory motion compared to both the PP and the VP. However, the organ at risk dose in the RP was slightly higher; therefore, its clinical use should be carefully considered.

Influence of minimum segment width on intensity-modulated radiotherapy plan for left-sided breast cancer after breast conserving surgery.

To research the influence of the minimum segment width (MSW) on intensity-modulated radiotherapy (IMRT) plan for left breast cancer after breast conserving surgery and provide a reference for plan optimization. Ten patients with left-sided early-stage breast cancer after breast-conserving surgery (BCS) were selected for postoperative radiotherapy. The Monaco 5.11 treatment planning system (TPS) was used, and the optimization parameters of the plan were fixed, while MSW were set to 0.5, 1, 1.5, and 2 cm, and four 5 field-IMRT plans were designed for every patient. The plan quality and execution efficiency of each plan were compared, including planning target volume (PTV) dose, organs at risk (OAR) dose, segments number (SN), monitor units (MU), delivery time (DT), and dose verification results. With the increase of the MSW, the dose indexes of the OAR were similar (P > .05). The D2%, D98%, and D50% of the PTV gradually deviated from the prescription dose, and the uniformity gradually deteriorated (P < .05). The gamma passing rates (GPR) of dose verification and the dose deviation of the PTV were similar (P > .05). The SN, MU, and DT gradually decreased (P < .05), but the amplitude change caused by each increase of 0.5 cm gradually decreased, the decrease of 0.5 to 1 cm was the largest, reaching 16.41%, 21.25%, and 0.35 minutes, respectively, while the 3 values of 1.5 and 2 cm were close. When using Monaco 5.11 TPS to design IMRT plan for left breast cancer after BCS, 1.5 cm MSW could ensure better dose coverage and uniformity of the PTV, as little as possible exposure dose to the OAR, and high dose verification accuracy. The SN, MU, and DT were significantly reduced, and the treatment efficiency was improved.

Robustness of hypofractionated breast radiotherapy after breast-conserving surgery with free breathing.

This study aimed to evaluate the robustness with respect to the positional variations of five planning strategies in free-breathing breast hypofractionated radiotherapy (HFRT) for patients after breast-conserving surgery. Twenty patients who received breast HFRT with 42.72 Gy in 16 fractions were retrospectively analyzed. Five treatment planning strategies were utilized for each patient, including 1) intensity-modulated radiation therapy (IMRT) planning (IMRTpure); 2) IMRT planning with skin flash tool extending and filling the fluence outside the skin by 2cm (IMRTflash); 3) IMRT planning with planning target volume (PTV) extended outside the skin by 2cm in the computed tomography dataset (IMRTePTV); 4) hybrid planning, i.e., 2 Gy/fraction three-dimensional conformal radiation therapy combined with 0.67 Gy/fraction IMRT (IMRThybrid); and 5) hybrid planning with skin flash (IMRThybrid-flash). All plans were normalized to 95% PTV receiving 100% of the prescription dose. Six additional plans were created with different isocenter shifts for each plan, which were 1mm, 2mm, 3mm, 5mm, 7mm, and 10mm distally in the X (left-right) and Y (anterior-posterior) directions, namely, (X,Y), to assess their robustness, and the corresponding doses were recalculated. Variation of dosimetric parameters with increasing isocenter shift was evaluated. All plans were clinically acceptable. In terms of robustness to isocenter shifts, the five planning strategies followed the pattern IMRTePTV, IMRThybrid-flash, IMRTflash, IMRThybrid, and IMRTpure in descending order. V 95% of IMRTePTV maintained at 99.6% ± 0.3% with a (5,5) shift, which further reduced to 98.2% ± 2.0% with a (10,10) shift. IMRThybrid-flash yielded the robustness second to IMRTePTV with less risk from dose hotspots, and the corresponding V 95% maintained >95% up until (5,5). Considering the dosimetric distribution and robustness in breast radiotherapy, IMRTePTV performed best at maintaining high target coverage with increasing isocenter shift, while IMRThybrid-flash would be adequate with positional uncertainty<5mm.

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.

Predictive modeling of dose-volume parameters of carcinoma tongue cases using machine learning models

The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinoma of the tongue cases prospectively. Intensity-modulated radiotherapy (IMRT) plans for 20 patients with carcinoma of the tongue were generated using the Eclipse treatment planning system. A machine learning model was generated using a Python 3.10 computer language in a Jupyter notebook using Anaconda software. The PTVs and OARs doses obtained from the clinical treatment plans were used as a primary dataset. Machine learning models are built with two different datasets (10 and 20) for each selected volume. Volumes from 10 new sets of patients were fed into the software for predicting the corresponding dose values. Through the input given, the plan generated dose values of 10 patients were compared with the predicted outcomes of the 10 and 20 dataset models. The model created using the PTVs volume data predicted the dose values with increased accuracy. By verifying the model prediction with the TPS generated value, both the 10 and 20 dataset models predict all the 10 PTVs data within an error bound of 3% and most of the OARs data within an error bound of 5%. The dosimetric features implemented in the machine learning models reasonably predict both the PTVs dose parameter and OARs constraints and give confidence in decision-making during the clinical planning process.

Clinical Implementation of “Plan of the Day” Strategy in Definitive Radiation Therapy of Cervical Cancer: Online Adaptation to Address the Challenge of Organ Filling Reproducibility

Background: Definitive pelvic intensity-modulated radiotherapy (IMRT) in cervical cancer is susceptible to geographic miss due to daily positional and volumetric variations in target and organs at risk. Hence, despite evidence of reduced acute and late treatment-related toxicities, implementation of image-guided IMRT (IG-IMRT) with a reasonable safety margin to encompass organ motion is challenging. Materials and Methods: In this prospective, non-randomized phase II study, patients with cervical cancer FIGO (2009) stage IB2-IIIB between the ages of 18 and 65 years were treated with definitive pelvic CT-RT with a pre-specified organ (bladder and rectum) filling protocol. Reproducibility of organ filling was assessed along with the implementation of daily comprehensive adaptive IGRT, with a library of 3 IMRT (VMAT) plans with incremental expansions of CTV to PTV (Primary) margins (small 0.7 cm, adequate 1 cm and large 1.5 cm) and a backup motion robust 3DCRT plan; the appropriate plan is chosen based on pre-treatment CBCT (‘Plan of the Day’ approach). Results: Fifty patients with a median age of 49 years (IQR 45-56 years), received definitive RT (45-46Gy in 23-25 fractions to pelvis; with simultaneous integrated boost to gross nodes in 15 patients) with the said IGRT protocol. In the analysis of 1171 CBCT images (in 1184 treatment sessions), the mean planning CT and CBCT bladder volumes were 417 ccs and 373 ccs respectively. Significant inter-fractional variation in bladder volume was noted with a mean absolute dispersion of 29.5% with respect to planning CT; significant influential random factors were post-chemotherapy sessions (p=<0.001), pre-CBCT protocol duration (p= 0.001), grades of CINV (p=0.001). Significantly higher variation in bladder filling noted in patients with higher age (p=0.014) and larger planning CT bladder volume (p=<0.001). Time trend analysis of fraction-wise bladder volume revealed an absolute systemic reduction of 16.3% in bladder volume means from 1st to 5th week. Variation in rectal diameter was much less pronounced, with 19.2% mean dispersion, without any significant factors affecting it.While in 19% and 2% of sessions ‘Large’ IMRT PTV and ‘3DCRT’ were necessary to cover the primary target respectively, reduction in treated volume was possible in 43% of sessions with ‘Small’ PTV selection instead of standard ‘Adequate’ PTV (36% sessions). ‘Plan of the day’ selection had a moderate to strong correlation with non-absolute dispersion of bladder filling (Spearman's R= 0.4; p= 0.001) and a weak (but significant) correlation with grades of acute toxicities. The planned protocol was well tolerated with no radiation-induced local grade III toxicity. Conclusion: Inter-fractional variation in organ filling (especially bladder) is inevitable despite fixed pre-treatment protocol in definitive settings (intact cervix). Despite the logistical challenges; adaptive IGRT in form of ‘plan of the day’ based on incremental CTV-to-PTV margins is a relatively simple and feasible strategy to minimise geometric uncertainties in radical IG-IMRT of cervical cancer.

Influence and optimization strategy of the magnetic field in 1.5 T MR-linac liver stereotactic radiotherapy

ObjectiveTo compare intensity reduction plans for liver cancer with or without a magnetic field and optimize field and subfield numbers in the intensity-modulated radiotherapy (IMRT) plans designed for liver masses in different regions.MethodsThis retrospective study included 62 patients who received radiotherapy for liver cancer at Shandong Cancer Hospital. Based on each patient's original individualized intensity-modulated plan (plan1.5 T), a magnetic field-free plan (plan0 T) and static intensity-modulated plan with four different optimization schemes were redesigned for each patient. The differences in dosimetric parameters among plans were compared.ResultsIn the absence of a magnetic field in the first quadrant, PTV Dmin increased (97.75 ± 17.55 vs. 100.96 ± 22.78)%, Dmax decreased (121.48 ± 29.68 vs. 119.06 ± 28.52)%, D98 increased (101.35 ± 7.42 vs. 109.35 ± 26.52)% and HI decreased (1.14 ± 0.14 vs. 1.05 ± 0.01). In the absence of a magnetic field in the second quadrant, PTV Dmin increased (84.33 ± 19.74 vs. 89.96 ± 21.23)%, Dmax decreased (105 ± 25.08 vs. 104.05 ± 24.86)%, and HI decreased (1.04 ± 0.25 vs. 0.99 ± 0.24). In the absence of a magnetic field in the third quadrant, PTV Dmax decreased (110.21 ± 2.22 vs. 102.31 ± 26)%, L-P V30 decreased (10.66 ± 9.19 vs. 5.81 ± 3.22)%, HI decreased (1.09 ± 0.02 vs. 0.98 ± 0.25), and PTV Dmin decreased (92.12 ± 4.92 vs. 89.1 ± 22.35)%. In the absence of a magnetic field in the fourth quadrant, PTV Dmin increased (89.78 ± 6.72 vs. 93.04 ± 4.86)%, HI decreased (1.09 ± 0.01 vs. 1.05 ± 0.01) and D98 increased (99.82 ± 0.82 vs. 100.54 ± 0.84)%. These were all significant differences. In designing plans for tumors in each liver region, a total number of subfields in the first area of 60, total subfields in the second zone of 80, and total subfields in the third and fourth zones of 60 or 80 can achieve the dose effect without a magnetic field.ConclusionIn patients with liver cancer, the effect of a magnetic field on the target dose is more significant than that on doses to organs at risk. By controlling the max total number of subfields in different quadrants, the effect of the magnetic field can be greatly reduced or even eliminated.

IMRT as an Alternative Technique for Intracavitary Brachytherapy in Patients with Carcinoma of the Cervix: A Feasibility Study Using Unified Dosimetric Index.

Intracavitary brachytherapy (ICBT) plays an important role in the management of carcinoma of the cervix. This study attempts to find the feasibility of intensity-modulated radiation therapy (IMRT) as a boost for patients who are not suitable for ICBT in order to improve their disease-free and overall survival. Twenty patients with carcinoma of the cervix were included in this study. Nine fields of IMRT and ICBT plans were generated for PTVBoost. Various dosimetric indices like coverage, conformity, homogeneity, and gradient index were calculated, and the corresponding unified dosimetric index (UDI) values were generated. Plans were classified based on combined UDI, and the UDI values were compared with those of ICBT. In addition, rectum and bladder doses were compared. All the dosimetric indices were within acceptable limits except for the gradient index. The gradient index of the IMRT and ICBT plans were 8.77 ± 0.26 and 1.33 ± 0.06 respectively (p < 0.0001). The mean of combined UDI with standard deviation was 32.557 ± 8.940 and plan quality was calculated from these values. Rectum and bladder doses for ICBT were lesser than IMRT (p < 0.0001). ICBT is the gold standard for boost RT in carcinoma of the cervix patients. IMRT boost is feasible for patients who are unsuitable on medical grounds for brachytherapy.

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.

Prediction of Radiomics-Based Machine Learning for Specific Dosimetric Verification of Pelvic Intensity Modulated Radiotherapy.

To establish the different machine learning classification predict models of gamma pass rates for specific dosimetric verification of pelvic intensity modulated radiotherapy plan which based on the radiomic features and to explore the best prediction model. Retrospective analysis of the 3D dosimetric verification results based on measurements with gamma pass rate criteria of 3%/2 mm and 10% dose threshold of 196 pelvic intensity-modulated radiotherapy plans was carried. Prediction models were established by extracting radiomic features data. Four machine learning algorithms, random forest, support vector machine, adaptive boosting and gradient boosting decision trees, were used to calculate the AUC value, sensitivity and specificity respectively. The classification performance of the four prediction models were evaluated. The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision trees models were 0.93,0.85,0.93,0.96, and 0.38,0.69,0.46, and 0.46, respectively. The AUC values for the random forest model and the adaptive boosting model were 0.81 and 0.82, respectively, and the AUC values for the support vector machine and gradient boosting decision tree models were 0.87. Machine learning methods based on radiomics can be used to establish a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity modulated radiotherapy. The classification performance of support vector machine model and gradient boosting decision trees model is better than that of random forest model and adaptive boosting model. The prediction model for a specific site is helpful to improve the performance of the model.

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.

Deep Learning-Based Dose Prediction Model for Automated Spot-Scanning Proton Arc Planning

Spot-scanning proton arc (SPArc) is a novel technique that employs a planning optimization algorithm to select the energies and positions of spots along a dynamic rotational arc trajectory. The SPArc technique has the potential to achieve superior dose conformality and treatment delivery efficiency over intensity-modulated proton therapy. However, creating such a SPArc plan using existing approaches is time-consuming and computationally extensively. This study investigated the feasibility of using the deep learning (DL) technique to predict the 3D dose distribution of the SPArc treatment plan, leveraging the prior knowledge acquired from conventional intensity-modulated radiation therapy (IMRT) plans. A DL model, 3D-Unet with residual connections and attention gates, was trained using an open-source database of CT images, critical structures, and IMRT plans from 340 head and neck cancer patients (HNC) as the base model. Transfer learning technique was applied to fine-tune the model parameters using the SPArc treatment plans created on the HNC patients from an in-house dataset, where the SPArc treatment plans (including control point sampling, energy layer distribution, arc trajectory, etc.,) were optimized using a previously developed iterative approach. The performance of the DL model was evaluated by comparing predicted and planned doses over 17 SPArc treatment plans by using 4-fold cross-validation. The SPArc planning time per patient was 8∼12 hours, while the dose prediction time was reduced to 2∼3 minutes using the proposed DL model. The deviation of D95 in the target was (-1.8±1.6) %. The deviation of the mean dose in the parotids, cord, mandible, and brainstem were (2.5±6.5) %, (-0.5±4.3) %, (1.4±3.9) %, and (3.4±8) % of the prescription, respectively. The dice similarity coefficients of the 80%, 70%, and 60% isodose lines were (0.9±0.09), (0.93±0.01), and (0.94±0.01), respectively. Our results demonstrate that a DL-based dose prediction model can be created with a limited number of SPArc treatment plans through transfer learning. The DL model can directly predict the 3D dose distribution in minutes for automated planning. This study paves the roadmap to develop a quick clinical decision platform for the optimal selection among the multi-treatment modalities.

Cobalt Compensator-Based IMRT for Gynecologic Cancer Treatment in Low- and Middle-Income Countries: Equivalence to LINAC-Based IMRT

Cobalt intensity modulated radiation therapy (IMRT) has the potential to impact global oncology by improving access to precision radiation therapy, particularly in low- and middle-income countries, and a novel compensator-based machine has been developed. Previously, cobalt compensator-based IMRT and LINAC-based IMRT were shown to achieve similar target goals and dose constraints for head and neck clinical cases. Additionally, the fidelity of those calculations was supported by Monte Carlo simulations and gamma analysis. The current study investigates the use of cobalt compensator IMRT for another application - gynecologic cancer patients. A commercial treatment planning system was previously commissioned for the cobalt compensator-based device using Monte Carlo simulations from a simulation toolkit. Patient-specific compensators were modeled. Clinical gynecologic cancer cases were planned and compared to clinical plans with a 6MV LINAC using IMRT. The prescribed dose was 45 Gy in 25 fractions. Dosimetric plan quality endpoints for the planning target volume (PTV) and organs-at-risk (OARs) were compared. Parametric t-testing was used for statistical analysis, and criteria for significance was p<0.05. Dose statistics of 10 cases showed similar target coverage and normal tissue sparing between IMRT plans using a 6 MV LINAC and using a cobalt compensator-based device. Greater than 95% of the dose to greater than 95% of the PTV was achieved for all 10 cases using both the LINAC-based and cobalt compensator-based plans. There was no significant difference in the mean percentage of the PTV receiving 95% of the dose between LINAC-based and cobalt compensator-based plans (98.0 ± 1.88% vs 97.1 ± 1.90%, respectively, p = 0.313). Additionally, both the LINAC-based and cobalt compensator-based plans, for all 10 cases, met dose constraints for the OARs, including Spinal Cord Dmax < 4500 cGy, Kidney Dmean < 1800 cGy, Bladder Dmax < 5750 cGy, Rectum Dmax < 5500 cGy, Small Bowel Dmax < 5400 cGy, Bone Marrow V4000 < 37%, and Femoral Heads Dmax < 5000 cGy. Cobalt compensator-based IMRT may provide comparable quality treatment plans to LINAC-based IMRT for gynecologic cancer patients. Physical development of the cobalt compensator device has been completed and will be commissioned for further research to assess clinical outcomes.

Long Term Patient Reported Quality of Life Outcomes and Patient Satisfaction of 1-Week Hypofractionated Whole Breast Radiation Therapy Using IMRT for Early-Stage Breast Cancer

Novel whole breast radiation therapy (WBRT) regimens have been recently reported with the aim of improving patient convenience while preserving local control and breast cosmesis. The UK FAST Forward trial showed that 26 Gy in 5 fx using 3D conformal WBRT was equivalent to the standard 3-week regimen. However, the use of inverse planned intensity modulated radiation therapy (IMRT) for this novel ultra-hypofractionated regimen has not been previously studied. We aim to study the patient reported quality of life (QOL) outcomes and patient satisfaction of WBRT using IMRT delivering 26 Gy in 5 fractions. Patients receiving WBRT with 26 Gy in 5 fractions using IMRT at our institution from 2020 to 2022 were included. Patients were treated with 26 Gy in 5 daily fx with or without a single fx boost of 4-5.2 Gy. IMRT plans were generated using inverse planning with 2 to 3 volumetric modulated arcs (VMAT). To assess patient-reported QOL outcomes, a questionnaire was prospectively administered at 1 month post treatment and yearly thereafter including the EORTC QLQ-BR23 breast cancer module and other patient satisfaction questions. Patient assessment involved a four-point scale (not at all, a little, quite a bit, and very much; very satisfied, moderately satisfied, moderately dissatisfied, and very dissatisfied). Here, we report the 1-year patient reported acute toxicity QOL outcomes and patient satisfaction for those undergoing IMRT based WBRT. Cross-sectional analyses were used to determine the prevalence of "quite a bit" or "very much" effects versus "not at all" or "a little" effects. A total of 96 patients were included. The median age at diagnosis was 65 years. The most common histology was invasive ductal carcinoma (53.1%). ER-positivity was found in 75.5% patients. Oncoplastic reconstruction was used in 51.0% patients. The median tumor size was 1.5 cm. There were 4/98 (4.3%) node positive patients and 22.5% received adjuvant chemotherapy. Endocrine therapy was used in 74.5% patients. A boost fraction was delivered in 95.9% patients. At the 1-year assessment, only 1/35 (2.9%), 2/35 (5.7%), and 4/35 (11.4%) patients reported "quite a bit" or "very much" breast pain, skin problems, and changes in breast appearance respectively. Additionally, 32/35 (91.4%) patients were either "very satisfied" or "moderately satisfied" with their treatment, while 32/35 (91.4%) patients reported "excellent" (n = 12) or "good" (n = 20) physical appearance of the breast. WBRT using inverse planned IMRT with 26 Gy in 5 fx shows excellent patient reported QOL and patient satisfaction at 1-year post RT. Longer follow up is needed to assess the long-term toxicity, normal tissue effects, and cosmetic outcomes of IMRT based delivery of this ultra-hypofractionated regimen.

Comprehensive Beam Modeling, Secondary Dose Computations and Treatment Planning for a 1.5 T MRgRT System

Magnetic resonance imaging guided radiotherapy (MRgRT) combines soft tissue visualization with online adaptive radiotherapy. The MRgRT workflow is characterized by many steps which needs to be performed swiftly and therefore greatly benefits from automation. The purpose of this study was to model a MRgRT system for dose computation and intensity modulated radiotherapy (IMRT) planning within a 1.5 T magnetic field in an independent treatment planning system (iTPS) with automation functionalities. A beam model for a 1.5 T MRgRT system was commissioned in an iTPS using beam profiles and output factor measurements. The cryostat of the MRgRT system was modeled using a cylindrical multi-layer combination of epoxy and aluminum and matched to source-to-surface dependent cryostat scatter measurement in air, and corrected for angular dependent cryostat transmission readings. The beam model was used with a fast Monte Carlo dose engine to account for the electron return and electron streaming effects within an external magnetic field. The multi-leaf collimator (MLC) was characterized with a novel synchronous and asynchronous sweeping gap (aSG) test. The aSGs have a shift (between 0 - 40 mm) between adjacent leaves which remains constant over the sweep. Measured doses map the shadowing due to the tongue-and-groove (TG) exposure and TG and leaf tip (LT) parameters are derived to match this within the iTPS. Clinical IMRT plans of 10 prostate, 5 lymph node and 5 rectal plans created in the default clinical TPS were recomputed in the iTPS and evaluated with the corresponding plan-specific quality assurance measurements. In addition, three IMRT plans were optimized within the iTPS, delivered with the MRgRT system and evaluated using PSQA using a 3%/3mm gamma criterium with a 10% threshold. Monte Carlo dose computation in the iTPS closely resembled the measured profiles in the presence of the 1.5 T magnetic field. The cryostat scatter simulations were <0.7% compared to the measurements and literature. The aSG/SGs test demonstrated that the TG width gradually increased up to 1 mm for a distance of 15 mm away from the leaf tip end. These effects were closely replicated in the iTPS with a difference of <1.5% for 95% of the aSGs. The difference between the clinical dose and the dose calculation in the iTPS was, on average, <0.5% within the volume receiving 50% of the maximum dose. The gamma pass rate was 100% for 98% of the PSQA results. The plans optimized within the iTPS were successfully delivered at the MRgRT system. We successfully modeled and commissioned a 1.5 T MRgRT system in an independent TPS. The beam model can be used for a second opinion dose computation in a magnetic field and for direct treatment planning. Next, the beam model will be incorporated in a semi-automated and adaptive workflow using the iTPS.

Plan Comparison of Left Breast Postmastectomy Radiotherapy: Halcyon IMRT and VMAT Plan vs. Truebeam Fixed-Jaws Plan

The purpose of this work is to provide a Varian Halcyon planning solution for left breast postmastectomy radiotherapy by comparing the plan quality of Varian Truebeam plan with Halcyon plan. Ten patients who previously received left breast postmastectomy intensity-modulated radiotherapy (IMRT) on Truebeam were retrospectively selected and replanned using Halcyon. The planning target volume (PTV) included chest wall and supraclavicular region, and the prescribed dose was 50 Gy in 25 fractions (2 Gy/F). In addition to the original Truebeam fixed-jaws IMRT (IMRT_T), a Halcyon IMRT (IMRT_H) plan and a Halcyon volumetric-modulated arc therapy (VMAT_H) plan were designed for each patient. The conformity index (CI) and homogeneity index (HI) of the PTVs, and the dose of organs at risk were calculated and compared. Statistical significance was performed using the LSD-t test or Wilcoxon signed-rank test with a significance level of 0.05. The V20 and V30 of ipsilateral lung, the V20, V30 and Dmean of heart, and CI of target area in IMRT_H plan were significantly lower than those in IMRT_T plan (P<0.05). No significant difference was found in the V5, V10 and Dmean of ipsilateral lung, the V5 and Dmean of contralateral lung, the V5 and Dmean of contralateral breast, HI of PTV (P>0.05), but the Dmean of left humeral head in IMRT_H plan was significantly higher than that of IMRT_T plan (P<0.05). All the metrics mentioned above showed higher level in VMAT_H plan than the IMRT_T plan and IMRT_H plan (P<0.05), except for HI (P>0.05) and Dmean of left humeral head (P>0.05 when compared to IMRT_H plan). The IMRT_H plan acts better in OAR sparing and PTV conformity than the IMRT_T plan, while the VMAT-H plan can only parallel the other two techniques in HI. The IMRT_H plan is promising in treating left breast postmastectomy patients with dosimetric improvements.

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.

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.

Dosimetric Evaluation Study of 10-MV FFF Used in SBRT for Lung Tumours

Purpose: The objective of this research was to conduct a comparative and dosimetric analysis of three different radiotherapy techniques used in lung stereotactic body radiotherapy (SBRT), the three-dimensional conformal radiotherapy (3DCRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT), using a 10 MV flattening filter-free (FFF) photon beam. Materials and methods: The present study employed computed tomography (CT) images of a humanoid phantom for the purpose of treatment planning. The gross tumour volumes (GTVs) delineated in both the central and peripheral positions of the lungs. The determination of Planning Target Volumes (PTVs) involved the addition of a margin of 0.5 cm to the Gross Tumour Volume (GTV). Three-dimensional conformal radiotherapy (3DCRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT) treatment plans produced employing a 10-megavolt (MV) flattening filter-free (FFF) photon beam. The calculation of dosage for all plans Performed using the anisotropic analytical algorithm (AAA). Results: IMRT and VMAT had better PTV dose conformation than 3DCRT for both central and peripheral targets. PTV conformity improved in VMAT compared to IMRT, and CI values were acceptable for VMAT, IMRT, and 3DCRT plans. VMAT plans had slightly better CI than IMRT, with better results in peripheral lung PTVs compared to central PTVs. VMAT and IMRT are superior for treating HDV and D2cm, with lower HDV for peripheral lung tumours. Both 3DCRT and IMRT improved outcomes for peripheral lung PTVs, while VMAT was better for central lung PTVs. The former proved better with less low lung doses and improved D2cm results. 3DCRT plans demonstrated higher precision in dose distribution than VMAT and IMRT plans, with superior average GI values. VMAT and IMRT had higher HI, Dmax, and D2% than 3DCRT. VMAT plans compared to IMRT plans, with similar HI values for central lung PTVs. VMAT better spares OARs than other techniques, but V20 and V30 lung doses were lower with 3DCRT. VMAT increases lung dose, but OAR stays below thresholds.&#x0D; Conclusion: The investigation found that all three treatment techniques can deliver SBRT plans that meet RTOG dose constraints. However, VMAT is a better treatment strategy than IMRT and 3DCRT for both peripheral and central lung PTVs, based on dosimetric indices like CI, D2cm, HI, and HDV. The study found that 3DCRT improves dosimetric indices, especially gradient index (GI), more than VMAT. Despite the need for more monitor units (MUs) in VMAT plans, treatment time reduced due to faster gantry velocity and higher dose rates (2400cGy/min) via free flatting filter energy.