Intensity-modulated Radiation Therapy Treatment Research Articles

Optimizing Treatment Quality in Head and Neck Cancer Therapy: Impact of Adaptive Intensity-Modulated Radiotherapy on Plan Quality and Tumor Volume Changes

Objective: This research analyses whether volumetric changes and PTV shifting affect adaptive intensity-modulated radiotherapy (IMRT) plan quality and head and neck cancer therapy. Methods: The research was carried out at the Sohag Cancer Centre in Egypt, including a sample of 36 individuals diagnosed with head and neck squamous cell carcinoma (HNSCC). Patients were chosen on the basis of the following criteria: locally progressed and node-positive malignancy, oropharyngeal cancer, oral cavity cancer, and unknown primary. The research used dynamic intensity-modulated radiation therapy (D-IMRT) to establish treatment plans and Siemens SOMATOM DEFINITION CT scanners to acquire 3D anatomical images. A range of 1.64 to 2.12 Gy was observed for the median dosage per fraction. Using the conformance index (CI) and the homogeneity index, the quality of the plan was assessed (HI). The threshold for statistical significance was set at 0.05. Results: The study analysed patients with unilateral and bilateral HNSCC tumors, with a majority having bilateral tumors. The findings demonstrated that the tumor PTV was significantly reduced as a consequence of the suggested adaptive radiation techniques. Both the homogeneity index and the conformity index were used to evaluate the plan's quality (HI). The maximal homogeneity index (HI) was reached after 7 and 14 fractions, respectively. The positive correlation between PTV reduction and CI after 7 fractions, while a negative correlation found after fractions 14 and 21. It was also shown that HI is positively correlated with PTV changes after 7 and 14. Conclusion: The use of adaptive radiotherapy in intensity-modulated radiation therapy (IMRT) treatment planning improves plan quality and decreases error rates due to tumor margin movement. Following radiation fractions, PTV shifting affects the homogeneity index (HI).

Radiation Therapy With or Without Cisplatin for Local Recurrences of Endometrial Cancer: Results From an NRG Oncology/GOG Prospective Randomized Multicenter Clinical Trial.

Pelvic recurrence is a frequent pattern of relapse for women with endometrial cancer. A randomized trial compared progression-free survival (PFS) after treatment with radiation therapy alone as compared with concurrent chemotherapy. Between February 2008 and August 2020, 165 patients were randomly assigned 1:1 to receive either radiation treatment alone or a combination of chemotherapy and radiation treatment. The primary objective of this study was to determine whether chemoradiation therapy was more effective than radiation therapy alone at improving PFS. The majority of patients had low-grade (1 or 2) endometrioid histology (82%) and recurrences confined to the vagina (86%). External beam with either the three-dimensional or intensity modulated radiation treatment technique was followed by a boost delivered with brachytherapy or external beam. Patients randomly assigned to receive chemotherapy were treated with once weekly cisplatin (40 mg/m2). Rates of acute toxicity were higher in patients treated with chemoradiation as compared with radiation treatment alone. Median PFS was longer for patients treated with radiation therapy alone as compared with chemotherapy and radiation (median PFS was not reached for RT v 73 months for chemoradiation, hazard ratio of 1.25 (95% CI, 0.75 to 2.07). At 3 years, 73% of patients treated definitively with radiation and 62% of patients treated with chemoradiation were alive and free of disease progression. Excellent outcomes can be achieved for women with localized recurrences of endometrial cancer when treated with radiation therapy. The addition of chemotherapy does not improve PFS for patients treated with definitive radiation therapy for recurrent endometrial cancer and increases acute toxicity. Patients with low-grade and vaginal recurrences who constituted the majority of those enrolled are best treated with radiation therapy alone.

Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma

BackgroundAt present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality.MethodsA total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans.ResultsThe average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P < 0.05).ConclusionWe have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.

Dosimetric comparison of 3D-Conformal and IMRT techniques used in radiotherapy of gastric cancer: A retrospective study

This study aimed to compare three-dimensional conformal radiotherapy (3D-CRT) and intensity-modulated radiotherapy (IMRT) planning techniques commonly used in gastric cancer radiotherapy with dose volume histograms. Computed tomography (CT) images of 20 gastric cancer patients were retrospectively analyzed. 3D-CRT, 5F- IMRT and 7F- IMRT treatment plans were created for each patient. The 3 plans were compared on dose volume histogram (DVH). 3D-CRT, 5F-IMRT and 7F-IMRT plans achieved a prescribed dose of 45 Gy for 95% of PTV volume. D95 values were 45.06±0.47 Gy for 3DCRT, 46.39±0.38 Gy for 5F-IMRT and 45.20±0.11 Gy for 7F-IMRT. In 3D-CRT, 5F-IMRT and 7F-IMRT techniques, the 13 Gy receiving volumes of the right kidney were found to be 35.08+9.59, 36.25+7.97 and 37.03+9.03 respectively. Moreover, the volume of the right kidney receiving a 20 Gy and 30 Gy dose received less dose with 5F-IMRT than with 7F-IMRT. Since each patient's critical organs are at different distances from the target and each technique has its own advantages in terms of critical organs, we suggest that the useful technique should be decided by clinical consensus.

A reinforcement learning agent for head and neck intensity-modulated radiation therapy

Head and neck (HN) cancers pose a difficult problem in the planning of intensity-modulated radiation therapy (IMRT) treatment. The primary tumor can be large and asymmetrical, and multiple organs at risk (OARs) with varying dose-sparing goals lie close to the target volume. Currently, there is no systematic way of automating the generation of IMRT plans, and the manual options face planning quality and long planning time challenges. In this article, we present a reinforcement learning (RL) model for the purposes of providing automated treatment planning to reduce clinical workflow time as well as providing a better starting point for human planners to modify and build upon. Several models with progressing complexity are presented, including the relevant plan dosimetry analysis and model interpretations of the resulting strategies learned by the auto-planning agent. Models were trained on a set of 40 patients and validated on a set of 20 patients. The presented models are shown to be consistent with the requirements of an RL model to be underpinned by a Markov decision process (MDP). In-depth interpretability of the models is presented by examination of the decision space using action hyperplanes. The auto-planning agent was able to generate plans with superior reduction in the mean dose of the left and right parotid glands by approximately 7 Gy ± 2.5 Gy (p &amp;lt; 0.01) over a starting, static template plan with only pre-defined general prescription information. RL plans were comparable to a human expert’s clinical plans for the primary (44 Gy), boost (26 Gy) , and the summed plans (70 Gy) with p-values of 0.43, 0.72, and 0.67, respectively, for the dosimetric endpoints and uniform target coverage normalization. The RL planning agent was able to produce the plans used in validation in an average of 13.58 min, with a minimum and a maximum planning time of 2.27 and 44.82 min, respectively.

Study the Quality of IMRT and VMAT Treatment Planning Techniques (TPS) Using Indices of Achievement (IOA) Nasopharyngeal Cancer Plans

Volumetric Modulated Arc Therapy (VMAT) and Intensity Modulated Radiation Therapy (IMRT) are comparable for nasopharyngeal cancerous radiation therapy. This research intends to analyze the high-quality plan using accomplishment, conformance, and homogeneity criteria. The study involved 40 patients with a postnasal cancerous tumor. The patients underwent computed tomography (CT) simulation to scan the anatomical details of the patients' heads. Then, their data was forwarded to the treatment planning system (TPS) workstation for IMRT and VMAT planning. The plans were evaluated using the IOA, HI, and CI indices. The nasopharynx coverage results consist of the GTV and PTV at 95%. The statistical study reveals that VMAT provides much more coverage than IMRT for 95% GTV and 95% PTV. The results reveal that VMAT has a substantially better-quality plan (IOA) than IMRT. IMRT provides a superior CI, but VMAT protects the cochlea and optic nerves more effectively. In addition, the IMRT is advantageous for the preservation of additional OARs. There is no statistical difference in protection for the mandible and parotid glands between the two procedures. The VMAT has superior coverage for the gross and planned target volumes and achievement indices. The conformity of IMRT in the tumor target area is better, while VMAT can better protect the cochlea and optic nerves.

An initial study on common concrete, titanium dioxide and tungsten dioxide for shielding of a standard external photon beam radiotherapy room

High-energy ionizing radiations have several applications including the treatment of various types of cancer. Advanced techniques such as IMRT (Intensity Modulated Radiation Therapy) and VMAT (Volumetric Modulated Arc Therapy) using high-energy photon beams have been largely used for radiation therapy of cancer. An important issue in radiotherapy is the evaluation of the shielding of the walls of the facility room housing a linear accelerator that produces high-energy photon beams. In this study we performed an estimation of the thicknesses of a standard radiotherapy room considering three different materials: common concrete, titanium dioxide (TiO2) and tungsten dioxide (WO2). It is assumed that patients are treated with photon beams of energies varying from 4 to 30 MeV and considering two types of treatments, only conventional and conventional plus IMRT and VMAT treatment. Results have shown an overall growth of thicknesses with energy, workload, area and type of treatment. At 1,000 Gy/week and 30 MeV photon energy the primary barrier thickness using concrete as the shielding material showed to be substantially higher as compared to TiO2 (143%) and WO2 (1,360%). Results have also indicated an overall linear increasing of volumes with the area used to construct the facility room.

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.

Locoregional Ablative Radiation Therapy for Patients With Breast Cancer Unsuitable for Surgical Resection

Breast cancer patients unsuitable for surgical resection are typically managed with palliative systemic therapy alone. We report outcomes of 5-fraction ablative radiotherapy for non-resected breast cancers. Retrospective analysis of an institutional registry of breast cancer patients unsuitable for resection who underwent 35-40 Gy/5 fractions to the primary breast tumour and/or regional lymph nodes from 2014-2021. Primary outcomes were cumulative incidence of local failure (LF) and grade ≥3 toxicity (CTCAE v5.0). We reviewed 57 patients who received 61 treatment courses (median age of 81 years, range 38-99). Unresectable tumour (10%), patient refusal (18%), medical inoperability (35%), and metastatic disease (37%) were the causes of not having surgery. Five patients (8%) had previously undergone adjuvant locoregional radiotherapy. Fifty-four percent (n=33/61) of treatment courses targeted the breast only, 31% (n=19/61) both the breast and lymph nodes, and 15% (n=9/61) the lymph nodes only. Sixty-seven percent (n=35/52) of the courses that targeted the breast were delivered with partial breast irradiation and 33% (n=17/52) with whole breast radiotherapy (median dose of 25Gy in 5 fractions) ± simultaneous integrated boost to the primary tumour. Most primary tumours (65%, n=34/52) and target lymph nodes (61%, n=17/28) were treated with a dose of 35 Gy in 5 fractions. Most treatments (52%) were delivered with intensity-modulated radiotherapy (IMRT). Radiotherapy was delivered daily (20%), every other day (18%), twice weekly (36%) or weekly (26%). The 2-year cumulative incidence of LF was 11.4% and grade≥3 toxicity was 15.1%. The grade ≥3 toxicity was 6.5% for IMRT treatments, versus 7.7% for non-IMRT treatments targeting partial breast and/or lymph nodes (HR 1.13, P=0.92), versus 38.9% for non-IMRT treatments targeting the entire breast (HR 6.91, P=0.023). All grade ≥3 toxicity cases were radiation dermatitis. No cases of brachial plexopathy were observed. 35-40 Gy in 5 fractions is a safe and effective breast stereotactic body radiotherapy regimen and may be an attractive option for patients who are not surgical candidates. Highly conformal techniques (i.e. IMRT or partial breast irradiation) were associated with a reduced risk of toxicity and should be the preferred treatment approaches.

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.