Evaluation of multiple-vendor AI autocontouring solutions.

BACKGROUND: Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice, three commercially available CT-based solutions were evaluated. MATERIALS AND METHODS: The following solutions were evaluated in this work: MIM-ProtégéAI+ (MIM), Radformation-AutoContour (RAD), and Siemens-DirectORGANS (SIE). Sixteen organs were identified that could be contoured by all solutions. For each organ, ten patients that had manually generated contours approved by the treating physician (AP) were identified, totaling forty-seven different patients. CT scans in the supine position were acquired using a Siemens-SOMATOMgo 64-slice helical scanner and used to generate autocontours. Physician scoring of contour accuracy was performed by at least three physicians using a five-point Likert scale. Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean distance to agreement (MDA) were calculated comparing AI contours to "ground truth" AP contours. RESULTS: The average physician score ranged from 1.00, indicating that all physicians reviewed the contour as clinically acceptable with no modifications necessary, to 3.70, indicating changes are required and that the time taken to modify the structures would likely take as long or longer than manually generating the contour. When averaged across all sixteen structures, the AP contours had a physician score of 2.02, MIM 2.07, RAD 1.96 and SIE 1.99. DSC ranged from 0.37 to 0.98, with 41/48 (85.4%) contours having an average DSC ≥ 0.7. Average HD ranged from 2.9 to 43.3 mm. Average MDA ranged from 0.6 to 26.1 mm. CONCLUSIONS: The results of our comparison demonstrate that each vendor's AI contouring solution exhibited capabilities similar to those of manual contouring. There were a small number of cases where unusual anatomy led to poor scores with one or more of the solutions. The consistency and comparable performance of all three vendors' solutions suggest that radiation oncology centers can confidently choose any of the evaluated solutions based on individual preferences, resource availability, and compatibility with their existing clinical workflows. Although AI-based contouring may result in high-quality contours for the majority of patients, a minority of patients require manual contouring and more in-depth physician review.

Fertility-sparing uterine displacement for pelvic malignancies: surgical options and radiotherapy dosimetry on a human cadaver.

BACKGROUND: Radio(chemo)therapy is often required in pelvic malignancies (cancer of the anus, rectum, cervix). Direct irradiation adversely affects ovarian and endometrial function, compromising the fertility of women. While ovarian transposition is an established method to move the ovaries away from the radiation field, surgical procedures to displace the uterus are investigational. This study demonstrates the surgical options for uterine displacement in relation to the radiation dose received.  METHODS: The uterine displacement techniques were carried out sequentially in a human female cadaver to demonstrate each procedure step by step and assess the uterine positions with dosimetric CT scans in a hybrid operating room. Two treatment plans (anal and rectal cancer) were simulated on each of the four dosimetric scans (1. anatomical position, 2. uterine suspension of the round ligaments to the abdominal wall 3. ventrofixation of the uterine fundus at the umbilical level, 4. uterine transposition). Treatments were planned on Eclipse® System (Varian Medical Systems®,USA) using Volumetric Modulated Arc Therapy. Data about maximum (Dmax) and mean (Dmean) radiation dose received and the volume receiving 14 Gy (V14Gy) were collected. RESULTS: All procedures were completed without technical complications. In the rectal cancer simulation with delivery of 50 Gy to the tumor, Dmax, Dmean and V14Gy to the uterus were respectively 52,8 Gy, 34,3 Gy and 30,5cc (1), 31,8 Gy, 20,2 Gy and 22.0cc (2), 24,4 Gy, 6,8 Gy and 5,5cc (3), 1,8 Gy, 0,6 Gy and 0,0cc (4). For anal cancer, delivering 64 Gy to the tumor respectively 46,7 Gy, 34,8 Gy and 31,3cc (1), 34,3 Gy, 20,0 Gy and 21,5cc (2), 21,8 Gy, 5,9 Gy and 2,6cc (3), 1,4 Gy, 0,7 Gy and 0,0cc (4). CONCLUSIONS: The feasibility of several uterine displacement procedures was safely demonstrated. Increasing distance to the radiation field requires more complex surgical interventions to minimize radiation exposure. Surgical strategy needs to be tailored to the multidisciplinary treatment plan, and uterine transposition is the most technically complex with the least dose received.

Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia.

In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Purpose: Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. Methods: A target conformal plan (TCP) was created based on the maximum planning target volume (PTV). Input data included TCP dose distribution, images, target structures, and organ-at-risk (OAR) information. The role of target conformal plan dose (TCPD) was assessed by comparing the TCPD-CNN (with dose information) and NonTCPD-CNN models (without dose information) using statistical analyses with the ranked Wilcoxon test (P < .05 considered significant). Results: The TCPD-CNN model showed no statistical differences in predicted target indices, except for PTV60, where differences in the D98% indicator were < 0.5%. For OARs, there were no significant differences in predicted results, except for some small-volume or closely located OARs. On comparing TCPD-CNN and NonTCPD-CNN models, TCPD-CNN's dose-volume histograms closely resembled clinical plans with higher similarity index. Mean dose differences for target structures (predicted TCPD-CNN and NonTCPD-CNN results) were within 3% of the maximum prescription dose for both models. TCPD-CNN and NonTCPD-CNN outcomes were 67.9% and 54.2%, respectively. 3D gamma pass rates of the target structures and the entire body were higher in TCPD-CNN than in the NonTCPD-CNN models (P < .05). Additional evaluation on previously unseen volumetric modulated arc therapy plans revealed that average 3D gamma pass rates of the target structures were larger than 90%. Conclusions: This study presents a novel framework for dose distribution prediction using deep learning and multichannel input, specifically incorporating TCPD information, enhancing prediction accuracy for IMRT in NPC treatment.

Deep learning based linear energy transfer calculation for proton therapy.

Objective.This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETddistributions from CT images and DW. The accuracy of the LETdof the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis.Main results.The proposed model accurately inferred LETddistributions for each proton field in the test dataset. A single-field LETdcalculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm-1and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest.Significance.This study demonstrates that deep-learning-based models can efficiently calculate LETdwith high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model's performance and evaluating its adaptability to different clinical scenarios.

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

Objective.To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Approach.Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.Main results.Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.Significance.Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

Prompt-gamma track-length estimator with time tagging from proton tracking.

The design of prompt-gamma detectors necessitates numerous Monte Carlo simulations to precisely develop and optimize the detection stages in proton therapy. Alongside the advancement of MC simulations, various variance reduction methods have been explored to speed-up calculations. Among these techniques, track-length estimators are interesting scoring methods for achieving both speed and accuracy in Monte Carlo simulations of rare events. This paper introduces an extension of the GATE vpgTLE module that incorporates the prompt-gamma emission time, which is tagged from the proton tracking, enhancing its utility for studies focused on detector design and optimization that rely on time measurements. The results obtained from a clinical radiotherapy plan are presented. We demonstrate that the new vpgTLE tally with time tagging is accurate, except for certain prompt-gamma lines corresponding to long mean-life nuclei.

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

Objective.Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources.Approach.Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates.Main results.Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively.Significance.This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation.

Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks.

Objective.Fast and accurate deformable image registration (DIR), including DIR uncertainty estimation, is essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.Approach.This study introduces a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo dropout, and Monte Carlo B-spline) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated the registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.Main results.The hyperparameter tuning of the models showed a trade-off between the estimated uncertainty's reliability and the deformation's accuracy. In the optimal trade-off, our model excelled in contour propagation and uncertainty estimation (p <0.05) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.Significance.By addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted, paving the way for safe deployment in a clinical environment.

Biological optimization for hybrid proton-photon radiotherapy.

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

The clinical practice and dosimetric outcome of the manual adaptive planning during definitive radiotherapy for cervical cancer.

PROPOSE: To evaluate the advantage of the manual adaptive plans comparing to the scheduled plans, and explored clinical factors predicting patients suitable for adaptive strategy. METHODS AND MATERIALS: Eighty two patients with weekly online cone-beam computed tomography (CBCT) were enrolled. The re-CT simulation was performed after 15 fractions and a manual adaptive plan was developed if a significant deviation of the planning target volume (PTV) was found. To evaluate the dosimetric benefit, D98, homogeneity index (HI) and conformity index (CI) for the planning target volume (PTV), as well as D2cc of the bowel, bladder, sigmoid and rectum were compared between manual adaptive plans and scheduled ones. The clinical factors influencing target motion during radiotherapy were analyzed by chi-square test and logistic regression analysis. RESULTS: The CI and HI of the manual adaptive plans were significantly superior to the scheduled ones (P = 0.0002, 0.003, respectively), demonstrating a better dose coverage of the target volume. Compared to the scheduled plans, D98 of the manual adaptive plans increased by 3.3% (P = 0.0002), the average of D2cc to the rectum, bladder decreased 0.358 Gy (P = 0.000034) and 0.240 Gy (P = 0.03), respectively. In addition, the chi-square test demonstrated that age, primary tumor volume, and parametrial infiltration were the clinical factors influencing target motion during radiotherapy. Multivariate analysis further identified the large tumor volume (≥ 50cm3, OR = 3.254, P = 0.039) and parametrial infiltration (OR = 3.376, P = 0.018) as the independent risk factors. CONCLUSION: We found the most significant organ motion happened after 15 fractions during treatment. The manual adaptive plans improved the dose coverage and decreased the OAR doses. Patients with bulky mass or with parametrial infiltration were highly suggested to adaptive strategy during definitive radiotherapy due to the significant organ motion.

Dose averaged linear energy transfer optimization for large sacral chordomas in carbon ion therapy.

BACKGROUND: Carbon ion beams are well accepted as densely ionizing radiation with a high linear energy transfer (LET). However, the current clinical practice does not fully exploit the highest possible dose-averaged LET (LETd) and, consequently, the biological potential in the target. This aspect becomes worse in larger tumors for which inferior clinical outcomes and corresponding lower LETd was reported. PURPOSE: The vicinity to critical organs in general and the inferior overall survival reported for larger sacral chordomas treated with carbon ion radiotherapy (CIRT), makes the treatment of such tumors challenging. In this work it was aimed to increase the LETd in large volume tumors while maintaining the relative biological effectiveness (RBE)-weighted dose, utilizing the LETd optimization functions of a commercial treatment planning system (TPS). METHODS: Ten reference sequential boost carbon ion treatment plans, designed to mimic clinical plans for large sacral chordoma tumors, were generated. High dose clinical target volumes (CTV-HD) larger than 250 cm 3 $250 \,{\rm cm}^{3}$ were considered as large targets. The total RBE-weighted median dose prescription with the local effect model (LEM) was D RBE , 50 % = 73.6 Gy $\textrm {D}_{\rm RBE, 50\%}=73.6 \,{\rm Gy}$ in 16 fractions (nine to low dose and seven to high dose planning target volume). No LETd optimization was performed in the reference plans, while LETd optimized plans used the minimum LETd (Lmin) optimization function in RayStation 2023B. Three different Lmin values were investigated and specified for the seven boost fractions: L min = 60 keV / µ m $\textrm {L}_{\rm min}=60 \,{\rm keV}/{\umu }{\rm m}$ , L min = 80 keV / µ m $\textrm {L}_{\rm min}=80 \,{\rm keV}/{\umu }{\rm m}$ and L min = 100 keV / µ m $\textrm {L}_{\rm min}=100 \,{\rm keV}/{\umu }{\rm m}$ . To compare the LETd optimized against reference plans, LETd and RBE-weighted dose based goals similar to and less strict than clinical ones were specified for the target. The goals for the organs at risk (OAR) remained unchanged. Robustness evaluation was studied for eight scenarios ( ± 3.5 % $\pm 3.5\%$ range uncertainty and ± 3 mm $\pm 3 \,{\rm mm}$ setup uncertainty along the main three axes). RESULTS: The optimization method with L min = 60 keV / µ m $\textrm {L}_{\rm min}=60 \,{\rm keV}/{\umu }{\rm m}$ resulted in an optimal LETd distribution with an average increase of LET d , 98 % ${\rm {LET}}_{{\rm {d,}}98\%}$ (and LET d , 50 % ${\rm {LET}}_{{\rm {d,}}50\%}$ ) in the CTV-HD by 8.9 ± 1.5 keV / µ m $8.9\pm 1.5 \,{\rm keV}/{\umu }{\rm m}$ ( 27 % $27\%$ ) (and 6.9 ± 1.3 keV / µ m $6.9\pm 1.3 \,{\rm keV}/{\umu }{\rm m}$ ( 17 % $17\%$ )), without significant difference in the RBE-weighted dose. By allowing ± 5 % $\pm 5\%$ over- and under-dosage in the target, the LET d , 98 % ${\rm {LET}}_{{\rm {d,}}98\%}$ (and LET d , 50 % ${\rm {LET}}_{{\rm {d,}}50\%}$ ) can be increased by 11.3 ± 1.2 keV / µ m $11.3\pm 1.2 \,{\rm keV}/{\umu }{\rm m}$ ( 34 % $34\%$ ) (and 11.7 ± 3.4 keV / µ m $11.7\pm 3.4 \,{\rm keV}/{\umu }{\rm m}$ ( 29 % $29\%$ )), using the optimization parameters L min = 80 keV / µ m $\textrm {L}_{\rm min}=80 \,{\rm keV}/{\umu }{\rm m}$ . The pass rate for the OAR goals in the LETd optimized plans was in the same level as the reference plans. LETd optimization lead to less robust plans compared to reference plans. CONCLUSIONS: Compared to conventionally optimized treatment plans, the LETd in the target was increased while maintaining the RBE-weighted dose using TPS LETd optimization functionalities. Regularly assessing RBE-weighted dose robustness and acquiring more in-room images remain crucial and inevitable aspects during treatment.

CERN-based experiments and Monte-Carlo studies on focused dose delivery with very high energy electron (VHEE) beams for radiotherapy applications.

Very High Energy Electron (VHEE) beams are a promising alternative to conventional radiotherapy due to their highly penetrating nature and their applicability as a modality for FLASH (ultra-high dose-rate) radiotherapy. The dose distributions due to VHEE need to be optimised; one option is through the use of quadrupole magnets to focus the beam, reducing the dose to healthy tissue and allowing for targeted dose delivery at conventional or FLASH dose-rates. This paper presents an in depth exploration of the focusing achievable at the current CLEAR (CERN Linear Electron Accelerator for Research) facility, for beam energies >200 MeV. A shorter, more optimal quadrupole setup was also investigated using the TOPAS code in Monte Carlo simulations, with dimensions and beam parameters more appropriate to a clinical situation. This work provides insight into how a focused VHEE radiotherapy beam delivery system might be achieved.

Comparison of Acute Toxicities, Overall Treatment Time and Quality of Life in Head and Neck Cancer Patients Treated with IMRT and Helical Tomotherapy.

INTRODUCTION: Technical innovations in radiation therapy treatment planning and delivery over the last two decades have changed the practice of radiation therapy dramatically. The benefit of improved dose homogeneity and better sparing of critical structures in helical tomotherapy compared with conventional linac-based IMRT has been reported. This study was conducted to compare acute toxicities (skin, mucous membrane, salivary gland and hematological) during treatment and overall treatment time in Head and Neck Cancer patients treated with IMRT and Helical Tomotherapy and to assess the quality of life of patients during treatment between two groups. MATERIALS AND METHODS: The study involved thirty patients with histologically proven Squamous cell carcinomas of Head and Neck. They were treated with concurrent chemoradiotherapy, to a dose of 60-70 Gray in 30-35 fractions. The study consists of 2 arms which are standard IMRT and Tomotherapy arm. Fifteen consecutive patients were treated under IMRT and 15 patients were treated under Helical tomotherapy, along with concurrent chemotherapy. After completion of planning, plans were evaluated and dose to the targets, organs at risk were tabulated. Patients were assessed weekly for acute toxicities (skin reactions, mucositis, xerostomia, haematological toxicities) during the course of the treatment as per RTOG criteria. Quality of life of patients were assessed using FACT/ NCCN HNSI questionnaire in local language at day 1, day 21 and at completion of radiotherapy. RESULTS: Grade 2-3 skin reactions, mucositis, anemia, leukopenia and thrombocytopenia were predominant in both arms. Treatment time from start of radiotherapy to completion of radiotherapy varied from 39 days to 68 days. Majority of patients completed radiotherapy within 50-56 days. Mean quality of life score did not show much difference between IMRT and tomotherapy arms. CONCLUSION: The study did not show any statistically significant difference in overall treatment time, acute toxicities- skin reactions, xerostomia, mucositis& hematological toxicities and quality of life of patients during radiotherapy between IMRT and Helical Tomotherapy. Dosimetric benefits of Tomotherapy over IMRT do not translate into clinical benefit in terms of reduced acute toxicities, lesser overall treatment time and better quality of life of patients. KEY WORDS: Head and Neck Carcinoma, IMRT, Tomotherapy, RTOG, toxicity, FACT/ NCCN HNSI, quality of life.

Managing Inconsistent Bladder Volumes in the Prostate Cancer Patient Using Daily Online Adaptive RT: A Case Report.

PURPOSE: We report the use of online adaptive radiotherapy (OART) aiming to improve dosimetric parameters in the prostate cancer patient who had lower urinary tract symptoms that caused him not to adhere to the standard bladder filling protocol. METHODS AND MATERIALS: The reference treatment plan for adaptive radiotherapy plan was generated for the pelvis and the solitary bony lesion using the Ethos treatment planning system. For each treatment session, high-quality iterative reconstructed cone beam CT (CBCT) images were acquired, and the system automatically generated an optimal adaptive plan after verification of contours. Image-guided RT (IGRT) plans were also created using the reference plan recalculated on the CBCT scan and were compared with adaptive plans. RESULTS: The reference bladder volume in the planning CT scan was 173 cc, and the mean bladder volume difference over the course was 25.4% ± 16.6%. The ART offered superior target coverage for PTV 70 Gy over online IGRT (V95: 90.5 ± 3.2 % Vs 97.3 ± 0.4%; p=0.000) and the bladder was also better spared from the high dose (V65 Gy: 17.9 ± 9.1% vs 14.8 ± 3.6%; p=0.03). However, the mean rectum V65 doses were very similar in both plans. CONCLUSION: Managing the inconsistent bladder volume was feasible in the prostate cancer patient using the CBCT-guided OART and our analysis confirmed that adaptive plans offered better target coverage while sparing the bladder from high radiation doses in comparison to online IGRT plans. KEY WORDS: radiotherapy, CBCT, online adaptive radiotherapy, image-guided RT.

Dosimetric Effect of Target Position Accuracy on Single-Isocenter Multiple Liver Metastases SBRT.

Purpose: To evaluate the dosimetric effects of intrafraction baseline shifts combined with rotational errors on Four-dimensional computed tomography-guided stereotactic body radiotherapy for multiple liver metastases (MLMs). Methods: A total of 10 patients with MLM (2 or 3 lesions) were selected for this retrospective study. Baseline shift errors of 0.5, 1.0, and 2.0 mm; and rotational errors of 0.5°, 1°, and 1.5°, were simulated about all axes. All of the baseline shifts and rotation errors were simulated around the planned isocenter using a matrix transformation of 6° of freedom. The coverage degradation of baseline shifts and rotational errors were analyzed according to the dose to 95% of the planning target volume (D95) and the volume covered by 95% of the prescribed dose (V95), and related changes in gross tumor volume were also analyzed. Results: At the rotation error of 0.5° and the baseline offset of less than 0.5 mm, the D95 and V95 values of all targets were >95%. For rotational errors of 1.0° (combined with all baseline shift errors), 36.3% of targets had D95 and V95 values of <95%. Coverage worsened substantially when the baseline shift errors were increased to 1.0 mm. D95 and V95 values were >95% for about 77.3% of the targets. Only 11.4% of the D95 and V95 values were >95% when the baseline shift errors were increased to 2.0 mm. When the rotational error was increased to 1.5° and baseline shift errors increased to 1.0 mm, the D95 and V95 values were >95% in only 3 cases. Conclusions: The multivariate regression model analysis in this study showed that the coverage of the target decreased further with reduced target volume, increasing the baseline drift, the rotation error, and the distance to the target.

Impact of different respiratory gating methods on target delineation and a radiotherapy plan for solitary pulmonary tumors.

BACKGROUND AND PURPOSE: Respiratory movement has an important impact on the radiotherapy for lung tumor. Respiratory gating technology is helpful to improve the accuracy of target delineation. This study investigated the value of prospective and retrospective respiratory gating simulations in target delineation and radiotherapy plan design for solitary pulmonary tumors (SPTs) in radiotherapy. METHODS: The enrolled patients underwent CT simulation with three-dimensional (3D) CT non gating, prospective respiratory gating, and retrospective respiratory gating simulation. The target volumes were delineated on three sets of CT images, and radiotherapy plans were prepared accordingly. Tumor displacements and movement information obtained using the two respiratory gating approaches, as well as the target volumes and dosimetry parameters in the radiotherapy plan were compared. RESULTS: No significant difference was observed in tumor displacement measured using the two gating methods (p > 0.05). However, the internal gross tumor volumes (IGTVs), internal target volumes (ITVs), and planning target volumes (PTVs) based on the retrospective respiratory gating simulation were larger than those obtained using prospective gating (group A: pIGTV = 0.041, pITV = 0.003, pPTV = 0.008; group B: pIGTV = 0.025, pITV = 0.039, pPTV = 0.004). The two-gating PTVs were both smaller than those delineated on 3D non gating images (p < 0.001). V5Gy, V10Gy, V20Gy, V30Gy, and mean lung dose in the two gated radiotherapy plans were lower than those in the 3D non gating plan (p < 0.001); however, no significant difference was observed between the two gating plans (p > 0.05). CONCLUSIONS: The application of respiratory gating could reduce the target volume and the radiation dose that the normal lung tissue received. Compared to prospective respiratory gating, the retrospective gating provides more information about tumor movement in PTV.

Inter-fractional error and intra-fractional motion of prostate and dosimetry comparisons of patient position registrations with versus without fiducial markers during treatment with carbon-ion radiotherapy.

A few reports have discussed the influence of inter-fractional position error and intra-fractional motion on dose distribution, particularly regarding a spread-out Bragg peak. We investigated inter-fractional and intra-fractional prostate position error by monitoring fiducial marker positions. In 2020, data from 15 patients with prostate cancer who received carbon-ion beam radiotherapy (CIRT) with gold markers were investigated. We checked marker positions before and during irradiation to calculate the inter-fractional positioning and intra-fractional movement and evaluated the CIRT dose distribution by adjusting the planning beam isocenter and clinical target volume (CTV) position. We compared the CTV dose coverages (CTV receiving 95% [V95%] or 98% [V98%] of the prescribed dose) between skeletal and fiducial matching irradiation on the treatment planning system. For inter-fractional error, the mean distance between the marker position in the planning images and that in a patient starting irradiation with skeletal matching was 1.49 ± 1.11 mm (95th percentile = 1.85 mm). The 95th percentile (maximum) values of the intra-fractional movement were 0.79 mm (2.31 mm), 1.17 mm (2.48 mm), 1.88 mm (4.01 mm), 1.23 mm (3.00 mm), and 2.09 mm (8.46 mm) along the lateral, inferior, superior, dorsal, and ventral axes, respectively. The mean V95% and V98% were 98.2% and 96.2% for the skeletal matching plan and 99.5% and 96.8% for the fiducial matching plan, respectively. Fiducial matching irradiation improved the CTV dose coverage compared with skeletal matching irradiation for CIRT for prostate cancer.

A comprehensive Study of the Out of Field Non Target Dose Associated with 6 and 10 MV Flattened and Flattening Filter Free X Ray Beam in a True Beam Linear Accelerator.

AIM: To evaluate the out-of-field dose associated with flattened (FF) and flattening filter-free (FFF) 6 and 10 MV X-ray beams in a TrueBeam linear accelerator (Linac). MATERIALS AND METHODS: Measurements were taken in a slab phantom using the metal oxide semiconductor field effect transistor (MOSFET) detector at varying depths (dmax, 5 cm, and 10 cm) for clinically relevant field sizes and up to 30 cm from the field edges for 6 and 10 MV FF and FFF beams in TrueBeam Linac. Dose calculation accuracy of the analytic anisotropic algorithm (AAA) and Acuros algorithm was investigated in the out-of-field region. Similarly, the out-of-field dose associated with volumetric modulated arc therapy (VMAT) head-and-neck plan delivered to a body phantom was evaluated. RESULTS: The out-of-field dose for both FF and FFF photon beams (6 and 10 MV) decreased with increasing distance from the field boundary and size. Furthermore, regardless of FF in the field, higher-energy photon beams were associated with lower out-of-field dose. Both algorithms underestimated the dose in the out-of-field region, with AAA failing to calculate the out-of-field dose at 15 cm from the field edge and Acuros failing to calculate out-of-field radiation at 20 cm. At 5 cm from the field edge, an average of 50% underestimation was observed, and at 10 cm, an average of 60% underestimation was observed for both FF and FFF (6 and 10 MV) beams. The VMAT head-and-neck plan performed with the FFF beam resulted in a lower out-of-field dose than the FF beam for a comparable dose distribution. CONCLUSION: Compared with flattened beams, the FFF modes on TrueBeam Linac exhibited a clinically relevant reduction in the out-of-field dose. Further dosimetric studies are warranted to determine the significant benefit of FFF beams across different cancer sites.

Assessment of the Dosimetric Index from IMRT and Rapid arc Plan for Oropharyngeal Cancer with Simultaneous Integrated Boost (SIB) Technique in Combination with EUD-based NTCP and TCP Radiobiological Models.

PURPOSE: The current research compared radiobiological and dosimetric results for simultaneous integrated boost (SIB) plans employing RapidArc and IMRT planning procedures in oropharyngeal cancer from head-and-neck cancer (HNC) patients. MATERIALS AND METHODS: The indigenously developed Python-based software was used in this study for generation and analysis. Twelve patients with forty-eight total plans with SIB were planned using Rapid arc (2 and 3 arcs) and IMRT (7 and 9 fields) and compared with radiobiological models Lyman, Kutcher, Burman (LKB) and EUD (Equivalent Uniform Dose) along with physical index such as homogeneity index(HI), conformity index(CI) of target volumes. RESULTS: These models' inputs are the dose-volume histograms (DVHs) calculated by the treatment planning system (TPS). The values obtained vary from one model to the other for the same technique and patient. The maximum dose to the brainstem and spinal cord and the mean dose to the parotids were analysed both dosimetrically and radiobiologically, such as the LKB model effective volume, equivalent uniform dose, EUD-based normal tissue complication probability, and normal tissue integral dose. The mean and max dose to target volume with conformity, homogeneity index, tumor control probability compared with treatment times, and monitor units. CONCLUSION: Rapid arc (3 arcs) resulted in significantly better OAR sparing, dose homogeneity, and conformity. The findings indicate that the rapid arc plan has improved dose distribution in the target volume compared with IMRT, but the tumor control probability obtained for the two planning methods, Rapid arc (3 arcs) and IMRT (7 fields), are similar. The treatment time and monitor units for the Rapid arc (3 arcs) were superior to other planning methods and considered to be standard in head & neck radiotherapy.