Population-based asymmetric margins for moving targets in real-time tumor tracking.

BACKGROUND: Both geometric and dosimetric components are commonly considered when determining the margin for planning target volume (PTV). As dose distribution is shaped by controlling beam aperture in peripheral dose prescription and dose-escalated simultaneously integrated boost techniques, adjusting the margin by incorporating the variable dosimetric component into the PTV margin is inappropriate; therefore, geometric components should be accurately estimated for margin calculations. PURPOSE: We introduced an asymmetric margin-calculation theory using the guide to the expression of uncertainty in measurement (GUM) and intra-fractional motion. The margins in fiducial marker-based real-time tumor tracking (RTTT) for lung, liver, and pancreatic cancers were calculated and were then evaluated using Monte Carlo (MC) simulations. METHODS: A total of 74 705, 73 235, and 164 968 sets of intra- and inter-fractional positional data were analyzed for 48 lung, 48 liver, and 25 pancreatic cancer patients, respectively, in RTTT clinical trials. The 2.5th and 97.5th percentiles of the positional error were considered representative values of each fraction of the disease site. The population-based statistics of the probability distributions of these representative positional errors (PD-RPEs) were calculated in six directions. A margin covering 95% of the population was calculated using the proposed formula. The content rate in which the clinical target volume (CTV) was included in the PTV was calculated through MC simulations using the PD-RPEs. RESULTS: The margins required for RTTT were at most 6.2, 4.6, and 3.9 mm for lung, liver, and pancreatic cancer, respectively. MC simulations revealed that the median content rates using the proposed margins satisfied 95% for lung and liver cancers and 93% for pancreatic cancer, closer to the expected rates than the margins according to van Herk's formula. CONCLUSIONS: Our proposed formula based on the GUM and motion probability distributions (MPD) accurately calculated the practical margin size for fiducial marker-based RTTT. This was verified through MC simulations.

Clinical features and risk factors for interstitial lung disease spreading in low-dose irradiated areas after definitive radiotherapy with or without durvalumab consolidation therapy for patients with non-small cell lung cancer.

BACKGROUND: The current standard of care for patients with unresectable locally advanced non-small cell lung cancer (NSCLC) is chemoradiotherapy (CRT) combined with durvalumab consolidation therapy. However, radiotherapy (RT) always carries the risk of radiation pneumonitis (RP), which can preclude durvalumab continuation. In particular, the spread of interstitial lung disease (ILD) in low-dose areas or extending beyond the RT field often makes it difficult to determine the safety of continuation or rechallenging of durvalumab. Thus, we retrospectively analyzed ILD/RP after definitive RT with and without durvalumab, with assessment of radiologic features and dose distribution in RT. METHODS: We retrospectively evaluated the clinical records, CT imaging, and radiotherapy planning data of 74 patients with NSCLC who underwent definitive RT at our institution between July 2016 and July 2020. We assessed the risk factors for recurrence within one year and occurrence of ILD/RP. RESULTS: Kaplan-Meier method showed that ≥ 7 cycles of durvalumab significantly improved 1-year progression free survival (PFS) (p < 0.001). Nineteen patients (26%) were diagnosed with ≥ Grade 2 and 7 (9.5%) with ≥ Grade 3 ILD/RP after completing RT. There was no significant correlation between durvalumab administration and ≥ Grade 2 ILD/RP. Twelve patients (16%) developed ILD/RP that spread outside the high-dose (> 40 Gy) area, of whom 8 (67%) had ≥ Grade 2 and 3 (25%) had Grade 3 symptoms. In unadjusted and multivariate Cox proportional-hazards models adjusted for V20 (proportion of the lung volume receiving ≥ 20 Gy), high HbA1c level was significantly correlated with ILD/RP pattern spreading outside the high-dose area (hazard ratio, 1.842; 95% confidence interval, 1.35-2.51). CONCLUSIONS: Durvalumab improved 1-year PFS without increasing the risk of ILD/RP. Diabetic factors were associated with ILD/RP distribution pattern spreading in the lower dose area or outside RT fields, with a high rate of symptoms. Further study of the clinical background of patients including diabetes is needed to safely increase the number of durvalumab doses after CRT.

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

PURPOSE: The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment. METHODS: Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50 Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14-18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model. RESULTS: The median GTV volume and motion range were 7.42 (range, 1.18-25.74) cm3 and 22 (range, 11-28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85 ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66-2.95) mm. The probability of the 3D deviation smaller than 5 mm was 93.6%. CONCLUSIONS: The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer.

A case of a pregnant woman with locally advanced cervical esophageal cancer treated with definitive chemoradiotherapy.

The information of definitive radiotherapy for a pregnant woman with malignancy was limited; however, it was reported to be potentially feasible with minimal risks. We performed definitive chemoradiotherapy for a pregnant woman with locally advanced cervical esophageal cancer. Feasibility of radiotherapy and safety of fetus were confirmed by the phantom study estimating fetal dose, and monitoring it in each radiotherapy session. The planned chemoradiotherapy completely eradicated esophageal cancer while preserving her laryngopharyngeal function. A female infant was delivered by cesarian section after planned chemoradiotherapy, and she grew without any apparent disorders 2 years after chemoradiotherapy. Chemoradiotherapy might be one of the treatment options for a pregnant woman with cervical esophageal cancer especially wishing the preservation of laryngopharyngeal function.

Multi-institutional phase II study on the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy for lung tumors.

BACKGROUND AND PURPOSE: This study aimed to evaluate the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy (DTT-SBRT) for lung tumors. MATERIALS AND METHODS: Patients with cStage I primary lung cancer or metastatic lung cancer with an expected range of respiratory motion of ≥10 mm were eligible for the study. The prescribed dose was 50 Gy in four fractions. A gimbal-mounted linac was used for DTT-SBRT delivery. The primary endpoint was local control at 2 years. RESULTS: Forty-eight patients from four institutions were enrolled in this study. Forty-two patients had primary non-small-cell lung cancer, and six had metastatic lung tumors. DTT-SBRT was delivered for 47 lesions in 47 patients with a median treatment time of 28 min per fraction. The median respiratory motion during the treatment was 13.7 mm (range: 4.5-28.1 mm). The motion-encompassing method was applied for the one remaining patient due to the poor correlation between the abdominal wall and tumor movement. The median follow-up period was 32.3 months, and the local control at 2 years was 95.2% (lower limit of the one-sided 85% confidence interval [CI]: 90.3%). The overall survival and progression-free survival at 2 years were 79.2% (95% CI: 64.7%-88.2%) and 75.0% (95% CI: 60.2%-85.0%), respectively. Grade 3 toxicity was observed in one patient (2.1%) with radiation pneumonitis. Grade 4 or 5 toxicity was not observed. CONCLUSION: DTT-SBRT achieved excellent local control with low incidences of severe toxicities in lung tumors with respiratory motion.

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy.

BACKGROUND: In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient's body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion. METHODS: From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems. RESULTS: The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively. CONCLUSIONS: The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset.

Dosiomic feature comparison between dose-calculation algorithms used for lung stereotactic body radiation therapy.

To evaluate the reproducibility of dose-based radiomic (dosiomic) features between dose-calculation algorithms for lung stereotactic body radiation therapy (SBRT). We analyzed 105 patients with early-stage non-small cell lung cancer who underwent lung SBRT between March 2011 and December 2017. Radiation doses of 48, 60, and 70 Gy were prescribed to the isocenter in 4-8 fractions. Dose calculations were performed using X-ray voxel Monte Carlo (XVMC) on the iPlan radiation treatment planning system (RTPS). Thereafter, the radiation doses were recalculated using the Acuros XB (AXB) and analytical anisotropic algorithm (AAA) on the Eclipse RTPS while maintaining the XVMC-calculated monitor units and beam arrangements. A total of 6808 dosiomic features were extracted without preprocessing (112 shape, 144 first-order, and 600 texture features) or with wavelet filters to eight decompositions (1152 first-order and 4800 texture features). Features with absolute pairwise concordance correlation coefficients-|CCcon|-values exceeding or equaling 0.85 were considered highly reproducible. Subgroup analyses were performed considering the wavelet filters and prescribed doses. The numbers of highly reproducible first-order and texture features were 34.8%, 26.9%, and 31.0% for the XVMC-AXB, XVMC-AAA, and AXB-AAA pairs, respectively. The maximum difference between the mean |CCcon| values was 0.70 and 0.11 for the subgroup analyses of wavelet filters and prescribed dose, respectively. The application of wavelet filter-based dosiomic analyses may be limited when using different types of dose-calculation algorithms for lung SBRT.

Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning.

PURPOSE: For pancreatic cancer patients, image guided radiation therapy and real-time tumor tracking (RTTT) techniques can deliver radiation to the target accurately. Currently, for the radiation therapy machine with kV X-ray imaging systems, internal markers must be implemented to facilitate tumor tracking. The purpose of this study was to develop a markerless deep learning-based pancreatic tumor positioning procedure for real-time tumor tracking with a kV X-ray imaging system. METHODS AND MATERIALS: Fourteen pancreatic cancer patients treated with intensity-modulated radiation therapy from six fixed gantry angles with a gimbal-head radiotherapy system were included in this study. For a gimbal-head radiotherapy system, the three-dimensional (3D) intrafraction target position can be determined using an orthogonal kV X-ray imaging system. All patients underwent four-dimensional computed tomography (4DCT) simulations for treatment planning, which were divided into 10 respiratory phases. After a patient's 4DCT was acquired, for each X-ray tube angle, 10 digitally reconstructed radiograph (DRR) images were obtained. Then, a data augmentation procedure was conducted. The data augmentation procedure first rotated the CT volume around the superior-inferior and anterior-posterior directions from -3° to 3° in 1.5° intervals. Then, the Super-SloMo model was adapted to interpolate 10 frames between respiratory phases. In total, the data augmentation procedure expanded the data scale 250-fold. In this study, for each patient, 12 datasets containing the DRR images from each specific X-ray tube angle based on the radiation therapy plan were obtained. The augmented dataset was randomly divided into training and testing datasets. The training dataset contained 2000 DRR images with clinical target volume (CTV) contours labeled for fine-tuning the pre-trained target contour prediction model. After the fine-tuning, the patient and X-ray tube angle-specific CTV contour prediction model was acquired. The testing dataset contained the remaining 500 images to evaluate the performance of the CTV contour prediction model. The dice similarity coefficient (DSC) between the area enclosed by the CTV contour and predicted contour was calculated to evaluate the model's contour prediction performance. The 3D position of the CTV was calculated based on the centroid of the contour in the orthogonal DRR images, and the 3D error of the prediction position was calculated to evaluate the CTV positioning performance. For each patient, the DSC results from 12 X-ray tube angles and 3D error from 6 gantry angles were calculated, representing the novelty of this study. RESULTS: The mean and standard deviation (SD) of all patients' DSCs were 0.98 and 0.015, respectively. The mean and SD of the 3D error were 0.29 mm and 0.14 mm, respectively. The global maximum 3D error was 1.66 mm, and the global minimum DSC was 0.81. The mean calculation time for CTV contour prediction was 55 ms per image. This fulfills the requirement of RTTT. CONCLUSIONS: Regarding the positioning accuracy and calculation efficiency, the presented procedure can provide a solution for markerless real-time tumor tracking for pancreatic cancer patients.

Preoperative chemoradiotherapy for locally advanced low rectal cancer using intensity-modulated radiotherapy to spare the intestines: a single-institutional pilot trial.

The irradiated volume of intestines is associated with gastrointestinal toxicity in preoperative chemoradiotherapy for rectal cancer. The current trial prospectively explored how much of the irradiated volume of intestines was reduced by intensity-modulated radiotherapy (IMRT) compared with 3-dimensional conformal radiotherapy (3DCRT) and whether IMRT might alleviate the acute gastrointestinal toxicity in this population. The treatment protocol encompassed preoperative chemoradiotherapy using IMRT plus surgery for patients with clinical T3-4, N0-2 low rectal cancer. IMRT delivered 45 Gy per 25 fractions for gross tumors, mesorectal and lateral lymph nodal regions, and tried to reduce the volume of intestines receiving 15 Gy (V15 Gy) < 120 cc and V45 Gy ≤ 0 cc, respectively, while keeping target coverage. S-1 and irinotecan were concurrently administered. Acute gastrointestinal toxicity, rates of clinical downstaging, sphincter preservation, local regional control (LRC) and overall survival (OS) were evaluated. Twelve enrolled patients completed the chemoradiotherapy protocol. The volumes of intestines receiving medium to high doses were reduced by the current IMRT protocol compared to 3DCRT; however, the predefined constraint of V15 Gy was met only in three patients. The rate of ≥ grade 2 gastrointestinal toxicity excluding anorectal symptoms was 17%. The rates of clinical downstaging, sphincter preservation, three-year LRC and OS were 75%, 92%, 92% and 92%, respectively. In conclusion, preoperative chemoradiotherapy using IMRT for this population might alleviate acute gastrointestinal toxicity, achieving high LRC and sphincter preservation; although further advancement is required to reduce the irradiated volume of intestines, especially those receiving low doses.

Chemoradiotherapy for fistula-related perianal squamous cell carcinoma with Crohn's disease.

The reports of chemoradiotherapy for anal squamous cell carcinoma with Crohn's disease are few. Severe toxicity related to radiotherapy is concerned in patients with inflammatory bowel disease. We report a case of chemoradiotherapy for locally advanced fistula-related perianal squamous cell carcinoma in a patient with long-standing Crohn's disease which was controlled by a maintenance therapy. The patient completed standard chemoradiotherapy using intensity-modulated radiotherapy without severe toxicity, and achieved complete remission. Standard chemoradiotherapy using intensity-modulated radiotherapy may be feasible and effective treatment for this population when Crohn's disease is controlled.

Reducing variability among treatment machines using knowledge-based planning for head and neck, pancreatic, and rectal cancer.

PURPOSE: This study aimed to assess dosimetric indices of RapidPlan model-based plans for different energies (6, 8, 10, and 15 MV; 6- and 10-MV flattening filter-free), multileaf collimator (MLC) types (Millennium 120, High Definition 120, dual-layer MLC), and disease sites (head and neck, pancreatic, and rectal cancer) and compare these parameters with those of clinical plans. METHODS: RapidPlan models in the Eclipse version 15.6 were used with the data of 28, 42, and 20 patients with head and neck, pancreatic, and rectal cancer, respectively. RapidPlan models of head and neck, pancreatic, and rectal cancer were created for TrueBeam STx (High Definition 120) with 6 MV, TrueBeam STx with 10-MV flattening filter-free, and Clinac iX (Millennium 120) with 15 MV, respectively. The models were used to create volumetric-modulated arc therapy plans for a 10-patient test dataset using all energy and MLC types at all disease sites. The Holm test was used to compare multiple dosimetric indices in different treatment machines and energy types. RESULTS: The dosimetric indices for planning target volume and organs at risk in RapidPlan model-based plans were comparable to those in the clinical plan. Furthermore, no dose difference was observed among the RapidPlan models. The variability among RapidPlan models was consistent regardless of the treatment machines, MLC types, and energy. CONCLUSIONS: Dosimetric indices of RapidPlan model-based plans appear to be comparable to the ones based on clinical plans regardless of energies, MLC types, and disease sites. The results suggest that the RapidPlan model can generate treatment plans independent of the type of treatment machine.

Development of a physical geometric phantom for deformable image registration credentialing of radiotherapy centers for a clinical trial.

PURPOSE: This study aimed to develop a physical geometric phantom for the deformable image registration (DIR) credentialing of radiotherapy centers for a clinical trial and tested the feasibility of the proposed phantom at multiple domestic and international institutions. METHODS AND MATERIALS: The phantom reproduced tumor shrinkage, rectum shape change, and body shrinkage using several physical phantoms with custom inserts. We tested the feasibility of the proposed phantom using 5 DIR patterns at 17 domestic and 2 international institutions (21 datasets). Eight institutions used the MIM software (MIM Software Inc, Cleveland, OH); seven used Velocity (Varian Medical Systems, Palo Alto, CA), and six used RayStation (RaySearch Laboratories, Stockholm, Sweden). The DIR accuracy was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). RESULTS: The mean and one standard deviation (SD) values (range) of DSC were 0.909 ± 0.088 (0.434-0.984) and 0.909 ± 0.048 (0.726-0.972) for tumor and rectum proxies, respectively. The mean and one SD values (range) of the HD value were 5.02 ± 3.32 (1.53-20.35) and 5.79 ± 3.47 (1.22-21.48) (mm) for the tumor and rectum proxies, respectively. In three patterns evaluating the DIR accuracy within the entire phantom, 61.9% of the data had more than a DSC of 0.8 in both tumor and rectum proxies. In two patterns evaluating the DIR accuracy by focusing on tumor and rectum proxies, all data had more than a DSC of 0.8 in both tumor and rectum proxies. CONCLUSIONS: The wide range of DIR performance highlights the importance of optimizing the DIR process. Thus, the proposed method has considerable potential as an evaluation tool for DIR credentialing and quality assurance.

Comparison of thoracic and abdominal deep inspiration breath holds in whole-breast irradiation for patients with left-sided breast cancer.

BACKGROUND: The deep inspiration breath hold (DIBH) technique is effective for heart dose reduction in patients with left-sided breast cancer. In deep breathing, some women breathe in thoracic respiration; and others, in abdominal respiration. This study evaluated differences in dose reduction in organs at risk (OAR) and reproducibilities of the target and OAR between thoracic DIBH (T-DIBH) and abdominal DIBH (A-DIBH). METHODS: Fourteen patients with left-sided breast cancer who had planned to receive whole-breast irradiation were included. Computed tomography (CT) was performed in free breathing (FB), T-DIBH, and A-DIBH, and the dosimetric indexes of the target and OAR for three treatment plans were compared. In T-DIBH and A-DIBH, two series CTs were taken in each breathing method and the displacements of the target and heart were calculated. RESULTS: The averaged mean heart doses (MHDs) were 1.5 Gy and 1.6 Gy in T-DIBH and A-DIBH, respectively, significantly lower than 2.7 Gy in FB (p < 0.001 for both breathing methods). Between T-DIBH and A-DIBH, no significant difference in MHD was found (p = 0.95); however, the percentage increase in lung volume positively moderately correlated with the reduction in MHD (R = 0.68). The three-dimensional target displacements were 2.3 mm in T-DIBH and 2.0 mm in A-DIBH (p = 0.64). The three-dimensional heart displacements were 1.7 mm in T-DIBH and 1.8 mm in A-DIBH (p = 0.85). CONCLUSION: The present study demonstrates that the MHD and reproducibility did not differ between T-DIBH and A-DIBH. However, the superior breathing method for increasing lung volume should be determined for each patient.

Statistical shape model-based planning organ-at-risk volume: application to pancreatic cancer patients.

PURPOSE: To introduce the concept of statistical shape model (SSM)-based planning organ-at-risk volume (sPRV) for pancreatic cancer patients. METHODS: A total of 120 pancreatic cancer patients were enrolled in this study. After correcting inter-patient variations in the centroid position of the planning target volume (PTV), four different SSMs were constructed by registering a deformable template model to an individual model for the stomach and duodenum. The sPRV, which focused on the following different components of the inter-patient variations, was then created: Scenario A: shape, rotational angle, volume, and centroid position; Scenario B: shape, rotational angle, and volume; Scenario C: shape and rotational angle; and Scenario D: shape. The conventional PRV (cPRV) was created by adding an isotropic margin R (3-15 mm) to the mean shape model. The corresponding sPRV was created from the SSM until the volume difference between the cPRV and sPRV was less than 1%. Thereafter, we computed the overlapping volume between the PTV and cPRV (OLc) or sPRV (OLs) in each patient. OLs being larger than OLc implies that the local shape variations in the corresponding OAR close to the PTV were large. Therefore, OLs/OLc was calculated in each patient for each R-value, and the median value of OLs/OLc was regarded as a surrogate for plan quality for each R-value. RESULTS: For R = 3 and 5 mm, OLs/OLc exceeded 1 for the stomach and duodenum in all scenarios, with a maximum OLs/OLc of 1.21. This indicates that smaller isotropic margins did not sufficiently account for the local shape changes close to the PTV. CONCLUSIONS: Our results indicated that, in contrast to conventional PRV, SSM-based PRVs, which account for local shape changes, would result in better dose sparing for the stomach and duodenum in pancreatic cancer patients.

Quantification and correction of the scattered X-rays from a megavoltage photon beam to a linac-mounted kilovoltage imaging subsystem.

OBJECTIVE: To quantify and correct megavoltage (MV) scattered X-rays (MV-scatter) on an image acquired using a linac-mounted kilovoltage (kV) imaging subsystem. METHODS AND MATERIALS: A linac-mounted flat-panel detector (FPD) was used to acquire an image containing MV-scatter by activating the FPD only during MV beam irradiation. 6-, 10-, and 15 MV with a flattening-filter (FF; 6X-FF, 10X-FF, 15X-FF), and 6- and 10 MV without an FF (6X-FFF, 10X-FFF) were used. The maps were acquired by changing one of the irradiation parameters while the others remained fixed. The mean pixel values of the MV-scatter were normalized to the 6X-FF reference condition (MV-scatter value). An MV-scatter database was constructed using these values. An MV-scatter correction experiment with one full arc image acquisition and two square field sizes (FSs) was conducted. Measurement- and estimation-based corrections were performed using the database. The image contrast was calculated at each angle. RESULTS: The MV-scatter increased with a larger FS and dose rate. The MV-scatter value factor varied substantially depending on the FPD position or collimator rotation. The median relative error ranges of the contrast for the image without, and with the measurement- and estimation-based correction were -10.9 to -2.9, and -1.5 to 4.8 and -7.4 to 2.6, respectively, for an FS of 10.0 × 10.0 cm2. CONCLUSIONS: The MV-scatter was strongly dependent on the FS, dose rate, and FPD position. The MV-scatter correction improved the image contrast. ADVANCES IN KNOWLEDGE: The MV-scatters on the TrueBeam linac kV imaging subsystem were quantified with various MV beam parameters, and strongly depended on the fieldsize, dose rate, and flat panel detector position. The MV-scatter correction using the constructed database improved the image quality.

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans.

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

Validation of the clinical applicability of knowledge-based planning models in single-isocenter volumetric-modulated arc therapy for multiple brain metastases.

PURPOSE: To validate the clinical applicability of knowledge-based (KB) planning in single-isocenter volumetric-modulated arc therapy (VMAT) for multiple brain metastases using the k-fold cross-validation (CV) method. METHODS: This study comprised 60 consecutive patients with multiple brain metastases treated with single-isocenter VMAT (28 Gy in five fractions). The patients were divided randomly into five groups (Groups 1-5). The data of Groups 1-4 were used as the training and validation dataset and those of Group 5 were used as the testing dataset. Four KB models were created from three of the training and validation datasets and then applied to the remaining Groups as the fourfold CV phase. As the testing phase, the final KB model was applied to Group 5 and the dose distributions were calculated with a single optimization process. The dose-volume indices (DVIs), modified Ian Paddick Conformity Index (mIPCI), modulation complexity scores for VMAT plans (MCSv), and the total number of monitor units (MUs) of the final KB plan were compared to those of the clinical plan (CL) using a paired Wilcoxon signed-rank test. RESULTS: In the fourfold CV phase, no significant differences were observed in the DVIs among the four KB plans (KBPs). In the testing phase, the final KB plan was statistically equivalent to the CL, except for planning target volumes (PTVs) D2% and D50% . The differences between the CL and KBP in terms of the PTV D99.5% , normal brain, and Dmax to all organs at risk (OARs) were not significant. The KBP achieved a lower total number of MUs and higher MCSv than the CL with no significant difference. CONCLUSIONS: We demonstrated that a KB model in a single-isocenter VMAT for multiple brain metastases was equivalent in dose distribution, MCSv, and total number of MUs to a CL with a single optimization.

Improvement of prediction and classification performance for gamma passing rate by using plan complexity and dosiomics features.

PURPOSE: The purpose of this study was to predict and classify the gamma passing rate (GPR) value by using new features (3D dosiomics features and combined with plan and dosiomics features) together with a machine learning technique for volumetric modulated arc therapy (VMAT) treatment plans. METHODS AND MATERIALS: A total of 888 patients who underwent VMAT were enrolled comprising 1255 treatment plans. Further, 24 plan complexity features and 851 dosiomics features were extracted from the treatment plans. The dataset was randomly split into a training/validation (80%) and test (20%) dataset. The three models for prediction and classification using XGBoost were as follows: (i) plan complexity features-based prediction method (plan model); (ii) 3D dosiomics feature-based prediction model (dosiomics model); (iii) a combination of both the previous models (hybrid model). The prediction performance was evaluated by calculating the mean absolute error (MAE) and the correlation coefficient (CC) between the predicted and measured GPRs. The classification performance was evaluated by calculating the area under curve (AUC) and sensitivity. RESULTS: MAE and CC at γ2%/2 mm in the test dataset were 4.6% and 0.58, 4.3% and 0.61, and 4.2% and 0.63 for the plan model, dosiomics model, and hybrid model, respectively. AUC and sensitivity at γ2%/2 mm in test dataset were 0.73 and 0.70, 0.81 and 0.90, and 0.83 and 0.90 for the plan model, dosiomics model, and hybrid model, respectively. CONCLUSIONS: A combination of both plan and dosiomics features with machine learning technique can improve the prediction and classification performance for GPR.

Radiotherapy planning techniques to reduce lung irradiation in head and neck cancer patients with mediastinal involvement.

The number of patients with head and neck squamous cell carcinoma (HNC) with mediastinal involvement is small, and appropriate treatment techniques have not been widely discussed. This study aimed to compare the efficacy of radiotherapy planning techniques in reducing lung irradiation while retaining target coverage. Among all HNC patients with mediastinal involvement who underwent radiotherapy between 2007 and 2014 at our hospital, seven consecutive patients were included in this study. Four different treatment plans were generated for each patient as follows: seven-field intensity-modulated radiation therapy (IMRT), modified IMRT in which the lateral beams avoided the lungs, three-full-arc volumetric-modulated arc therapy (VMAT), and VMAT with lung avoidance. We compared the outcomes of IMRT and VMAT plans using the paired t-test. After modifications were made to avoid lung irradiation, IMRT values for V5Gy and V20Gy decreased from 713.2 to 503.6 cm3 (p = 0.011) and from 338.8 cm3 to 267.0 cm3 (p = 0.058), respectively. In the case of VMAT, lung V5Gy and V20Gy values decreased from 754.8 to 601.0 cm3 (p = 0.004) and from 328.5 to 255.7 cm3 (p = 0.020), respectively. Other factors did not significantly differ between the plans. In both IMRT and VMAT planning, lung doses were significantly reduced following the modification of the beams that cross the lungs with target coverage maintenance.

Investigation of 4D dose in volumetric modulated arc therapy-based stereotactic body radiation therapy: does fractional dose or number of arcs matter?

The aim of this study was to assess the impact of fractional dose and the number of arcs on interplay effects when volumetric modulated arc therapy (VMAT) is used to treat lung tumors with large respiratory motions. A three (fractional dose of 4, 7.5 or 12.5 Gy) by two (number of arcs, one or two) VMAT plan was created for 10 lung cancer cases. The median 3D tumor motion was 17.9 mm (range: 8.2-27.2 mm). Ten phase-specific subplans were generated by calculating the dose on each respiratory phase computed tomography (CT) scan using temporally assigned VMAT arcs. We performed temporal assignment of VMAT arcs using respiratory information obtained from infrared markers placed on the abdomens of the patients during CT simulations. Each phase-specific dose distribution was deformed onto exhale phase CT scans using contour-based deformable image registration, and a 4D plan was created by dose accumulation. The gross tumor volume dose of each 4D plan (4D GTV dose) was compared with the internal target volume dose of the original plan (3D ITV dose). The near-minimum 4D GTV dose (D99%) was higher than the near-minimum 3D internal target volume (ITV) dose, whereas the near-maximum 4D GTV dose (D1%) was lower than the near-maximum 3D ITV dose. However, the difference was negligible, and thus the 4D GTV dose corresponded well with the 3D ITV dose, regardless of the fractional dose and number of arcs. Therefore, interplay effects were negligible in VMAT-based stereotactic body radiation therapy for lung tumors with large respiratory motions.