Beginning of clinical treatment using the 1.5 Tesla MR-Linac system in Japan: a narrative review.

Background and Objective: In the field of radiation therapy, image-guided radiotherapy (IGRT) technology has been gradually improving and highly accurate radiation treatment has been possible. Research on IGRT using 1.5 Tesla magnetic resonance imaging (MRI) began in 1999, and a radiation therapy device called 1.5 Tesla magnetic resonance linear accelerator (MR-Linac), which combines a linear accelerator with 1.5 Tesla MRI, was developed in Europe. The aim of this review is to present an overview of 1.5 Tesla MR-Linac with a review of the literature and our experience. Methods: Reports related to 1.5 Tesla MR-Linac were searched for in PubMed and are discussed in relation to our experience. Key Content and Findings: The 1.5 Tesla MR-Linac enables IGRT using 1.5 Tesla MRI, further enhancing the precision of radiation therapy. Position verification by cone-beam computed tomography (CBCT) is performed in many institutions, but soft tissue contrast is often unclear in CBCT images of the abdomen and mediastinal organs. Since the 1.5 Tesla MR-Linac allows position verification using MRI, position verification can be performed using clear MRI even in regions where CBCT is unclear. With the 1.5 Tesla MR-Linac, it is possible to perform online adaptive radiotherapy (ART) using 1.5 Tesla MRI. Online ART is a method in which images are acquired while the patient is on the treatment table. The method is based on the current condition of the organs in the body on that day and an optimal treatment field is recreated. Additionally, it allows monitoring of tumor motion using cine images obtained by 1.5 Tesla MRI during the delivery of X-ray radiation. A previous report showed that patients with prostate cancer who received radiotherapy by MR-Linac had fewer side effects than those in patients who received conventional CBCT radiation therapy. Conclusions: The 1.5 Tesla MR-Linac obtained CE-mark certification in Europe in August 2018 and it has been used for clinical treatment. In Japan, clinical treatment using this device started in 2021. By using 1.5 Tesla MR-Linac, patients can be provided with higher precision radiotherapy. In this review, we provide an overview of 1.5 Tesla MR-Linac.

Current status of remote radiotherapy treatment planning in Japan: findings from a national survey†.

The purpose of this study was to investigate the status of remote-radiotherapy treatment planning (RRTP) in Japan through a nationwide questionnaire survey. The survey was conducted between 29 June and 4 August 2022, at 834 facilities in Japan that were equipped with linear accelerators. The survey utilized a Google form that comprised 96 questions on facility information, information about the respondent, utilization of RRTP between facilities, usage for telework and the inclination to implement RRTPs in the respondent's facility. The survey analyzed the utilization of the RRTP system in four distinct implementation types: (i) utilization as a supportive facility, (ii) utilization as a treatment facility, (iii) utilization as a teleworker outside of the facility and (iv) utilization as a teleworker within the facility. The survey response rate was 58.4% (487 facilities responded). Among the facilities that responded, 10% (51 facilities) were implementing RRTP. 13 served as supportive facilities, 23 as treatment facilities, 17 as teleworkers outside of the facility and 5 as teleworkers within the facility. In terms of system usage between supportive and treatment facilities, 70-80% of the participants utilized the system for emergencies or as overtime work for external workers. A substantial number of facilities (38.8%) reported that they were unfamiliar with RRTP implementation. The survey showed that RRTP utilization in Japan is still limited, with a significant number of facilities unfamiliar with the technology. The study highlights the need for greater understanding and education about RRTP and financial funds of economical compensation.

Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients.

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70-74 Gy/28-37 fr) at our hospital were used (n = 55 for training and n = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans-i.e. preDose and deliDose-with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04-6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00-15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.

Evaluation of Unity 1.5 T MR-linac plan quality in patients with prostate cancer.

The Unity magnetic resonance (MR) linear accelerator (MRL) with MR-guided adaptive radiotherapy (MRgART) is capable of online MRgART where images are acquired on the treatment day and the radiation treatment plan is immediately replanned and performed. We evaluated the MRgART plan quality and plan reproducibility of the Unity MRL in patients with prostate cancer. There were five low- or moderate-risk and five high-risk patients who received 36.25 Gy or 40 Gy, respectively in five fractions. All patients underwent simulation magnetic resonance imaging (MRI) and five online adaptive MRI. We created plans for 5, 7, 9, 16, and 20 beams and for 60, 100, and 150 segments. We evaluated the target and organ doses for different number of beams and segments, respectively. Variation in dose constraint between the simulation plan and online adaptive plan was measured for each patient to assess plan reproducibility. The plan quality improved with the increasing number of beams. However, the proportion of significantly improved dose constraints decreased as the number of beams increased. For some dose parameters, there were statistically significant differences between 60 and 100 segments, and 100 and 150 segments. The plan of five beams exhibited limited reproducibility. The number of segments had minimal impact on plan reproducibility, but 60 segments sometimes failed to meet dose constraints for online adaptive plan. The optimization and delivery time increased with the number of beams and segments. We do not recommend using five or fewer beams for a reproducible and high-quality plan in the Unity MRL. In addition, many number of segments and beams may help meet dose constraints during online adaptive plan. Treatment with the Unity MRL should be performed with the appropriate number of beams and segments to achieve a good balance among plan quality, delivery time, and optimization time.

Development of a collapsed cone convolution/superposition dose calculation algorithm with a mass density-specific water kernel for magnetic resonance-guided radiotherapy.

This study aimed to develop and validate a collapsed cone convolution for magnetic resonance-guided radiotherapy (CCCMR). The 3D energy deposition kernels (EDKs) were generated in water in a 1.5-T transverse magnetic field. The CCCMR corrects the inhomogeneity in simulation geometry by referring to the EDKs according to the mass density between the interaction and energy deposition points in addition to density scaling. Dose distributions in a water phantom and in slab phantoms with inserted inhomogeneities were calculated using the Monte Carlo (MC) and CCCMR. The percentage depth dose (PDD) and off-axis ratio (OAR) were compared, and the gamma passing rate (3%/2 mm) was evaluated. The CCCMR simulated asymmetric dose distributions in the simulation phantoms, especially the water phantom, and all PDD and OAR profiles were in good agreement with the findings of the MC. The gamma passing rates were >99% for each field size and for the entire region. In the inhomogeneity phantoms, although the CCCMR underestimated dose in the low mass density regions, it could reconstruct dose changes at mass density boundaries. The gamma passing rate for the entire region was >95% for the field size of 2 × 2 cm2, but it was 68.9-86.7% for the field sizes of ≥5 × 5 cm2. Conclusively, in water, the CCCMR can obtain dose distributions comparable to those with the MC. Although the dose differences between them were mainly in inhomogeneity regions, the possibility of the effective use of the CCCMR in small field sizes was demonstrated.

Development and validation of an [18F]FDG-PET/CT radiomic model for predicting progression-free survival for patients with stage II - III thoracic esophageal squamous cell carcinoma who are treated with definitive chemoradiotherapy.

BACKGROUND: Radiomics is a method for extracting a large amount of information from images and used to predict treatment outcomes, side effects and diagnosis. In this study, we developed and validated a radiomic model of [18F]FDG-PET/CT for predicting progression-free survival (PFS) of definitive chemoradiotherapy (dCRT) for patients with esophageal cancer. MATERIAL AND METHODS: Patients with stage II - III esophageal cancer who underwent [18F]FDG-PET/CT within 45 days before dCRT between 2005 and 2017 were included. Patients were randomly assigned to a training set (85 patients) and a validation set (45 patients). Radiomic parameters inside the area of standard uptake value ≥ 3 were calculated. The open-source software 3D slicer and Pyradiomics were used for segmentation and calculating radiomic parameters, respectively. Eight hundred sixty radiomic parameters and general information were investigated.In the training set, a radiomic model for PFS was made from the LASSO Cox regression model and Rad-score was calculated. In the validation set, the model was applied to Kaplan-Meier curves. The median value of Rad-score in the training set was used as a cutoff value in the validation set. JMP was used for statistical analysis. RStudio was used for the LASSO Cox regression model. p < 0.05 was defined as significant. RESULTS: The median follow-up periods were 21.9 months for all patients and 63.4 months for survivors. The 5-year PFS rate was 24.0%. In the training set, the LASSO Cox regression model selects 6 parameters and made a model. The low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.019). In the validation set, the low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.040). CONCLUSIONS: The [18F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.

Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images.

PURPOSE: Radiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO). METHODS: Between 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation. RESULTS: We found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %-80 % and 0.17 with 40 %-60 % (P < 0.01). CONCLUSIONS: The introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy.

Longitudinal analyses and predictive factors of radiation-induced lung toxicity-related parameters after stereotactic radiotherapy for lung cancer.

BACKGROUND AND PURPOSE: The purpose of this prospective study was to investigate changes in longitudinal parameters after stereotactic radiotherapy for lung cancer and to identify possible pretreatment factors related to radiation-induced lung toxicity and the decline in pulmonary function after radiotherapy. MATERIALS AND METHODS: Protocol-specified examinations, including 4-D CT, laboratory tests, pulmonary function tests (PFTs) and body composition measurements, were performed before SRT and at 1 month, 4 months and 12 months after stereotactic radiotherapy. Longitudinal differences were tested by using repeated-measures analysis of variance. Correlations were examined by using the Pearson product-moment correlation coefficient (r). RESULTS: Sixteen patients were analyzed in this study. During a median follow-up period of 26.6 months, grade 1 and 2 lung toxicity occurred in 11 patients and 1 patient, respectively. The mean Hounsfield units (HU) and standard deviation (SD) of the whole lung, as well as sialylated carbohydrate antigen KL-6 (KL-6) and surfactant protein-D (SP-D), peaked at 4 months after radiotherapy (p = 0.11, p<0.01, p = 0.04 and p<0.01, respectively). At 4 months, lung V20 Gy (%) and V40 Gy (%) were correlated with changes in SP-D, whereas changes in the mean HU of the lung were related to body mass index and lean body mass index (r = 0.54, p = 0.02; r = 0.57, p = 0.01; r = 0.69, p<0.01; and r = 0.69, p<0.01, respectively). The parameters of PFTs gradually declined over time. When regarding the change in PFTs from pretreatment to 12 months, lung V5 Gy (cc) showed significant correlations with diffusion capacity for carbon monoxide (DLCO), DLCO/alveolar volume and the relative change in DLCO (r = -0.72, p<0.01; r = -0.73, p<0.01; and r = -0.63, p = 0.01, respectively). CONCLUSIONS: The results indicated that some parameters peaked at 4 months, but PFTs were the lowest at 12 months. Significant correlations between lung V5 Gy (cc) and changes in DLCO and DLCO/alveolar volume were observed.

[Evaluation of Accuracy of Deformable Image Registration with Newly Developed Treatment Planning Support Software for Thoracic Images].

This study evaluated accuracy of deformable image registration (DIR) with twelve parameter settings for thoracic images. We used peak-inhale and peak-exhale images for ten patients provided by DIR-lab. We used a prototype version of iCView software (ITEM Corporation) with DIR to perform intensity, structure, and hybrid-based DIR with the twelve parameter settings. DIR accuracy was evaluated by a target registration error (TRE) using 300 bronchial bifurcations and the Dice similarity coefficient (DSC) of the lungs. For twelve parameter settings, TRE ranged from 2.83 mm to 5.27 mm, whereas DSC ranged from 0.96 to 0.98. These results demonstrated that DIR accuracy differed among parameter settings and show that appropriate parameter settings are required for clinical practice.

Stereotactic Radiosurgery for Lung Cancer with a Risk-Adapted Strategy Using the Volumetric Modulated Arc Therapy Technique: A Single Arm Phase II Study.

PURPOSE: A phase II study carried out to assess the efficacy of a risk-adapted strategy of stereotactic radiosurgery (SRS) for lung cancer. The primary endpoint was 3-year local recurrence, and the secondary endpoints were overall survival (OS), disease-free survival (DFS), rate of start of systemic therapy or best supportive care (SST-BSC), and toxicity. MATERIALS AND METHODS: Eligible patients fulfilled the following criteria: performance status of 2 or less, forced expiratory volume in 1 s of 700 mL or more, and tumor not located in central or attached to the chest wall. Twenty-eight Gy was prescribed for primary lung cancers with diameters of 3 cm or less and 30 Gy was prescribed for primary lung cancers with diameters of 3.1-5.0 cm or solitary metastatic lung cancer diameters of 5 cm or less. RESULTS: Twenty-one patients were analyzed. The patients included 7 patients with adenocarcinoma, 2 patients with squamous cell carcinoma, 1 patient with metastasis, and 11 patients with clinical diagnosis. The median tumor diameter was 1.9 cm. SRS was prescribed at 28 Gy for 18 tumors and 30 Gy for 3 tumors. During the median follow-up period of 38.9 months for survivors, 1 patient had local recurrence, 7 patients had regional or distant metastasis, and 5 patients died. The 3-year local recurrence, SST-BSC, DFS, and OS rates were 5.3% (95% confidence interval [CI]: 0.3-22.2%), 20.1% (95% CI: 6.0-40.2%), 59.2% (95% CI: 34.4-77.3%), and 78.2% (95% CI: 51.4-91.3%), respectively. The 95% CI upper value of local recurrence was lower than the null local recurrence probability. There was no severe toxicity, and grade 2 radiation pneumonitis occurred in 1 patient. CONCLUSIONS: Patients who received SRS for lung cancer had a low rate of 3-year local recurrence and tolerable toxicity.

Comparison of acute gastrointestinal toxicities between 3-dimensional conformal radiotherapy and intensity-modulated radiotherapy including prophylactic regions in chemoradiotherapy with S-1 for pancreatic cancer-importance of dose volume histogram parameters in the stomach as the predictive factors.

The purpose of this study was to compare acute gastrointestinal (GI) toxicities in patients who underwent 3-dimensional conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) in chemoradiotherapy (CRT) with S-1 including prophylactic regions for pancreatic cancer. We also investigated the predictive factor of acute GI toxicities in dose volume histogram (DVH) parameters. Patients who received CRT with S-1 for pancreatic cancer between January 2014 and March 2021 were included. Radiotherapy (RT) with a total dose of 50-54 Gy was delivered. We examined the differences in the frequencies of acute GI toxicity of grade 2 or higher and DVH parameters of the stomach (ST) and duodenum (DU) between the 3DCRT group and the IMRT group. The RT-related predictive factors of acute GI toxicities were investigated by univariate and multivariate analyses. There were 25 patients in the 3DCRT group and 31 patients in the IMRT group. The frequencies of acute GI toxicity of G2 or higher were 36% in the 3DCRT group and 9.7% in the IMRT group (p = 0.035). ST V50 was the most predictive factor (p = 0.001), and the incidences of acute GI toxicity of G2 or higher in ST V50 ≥ 4.1 cc and < 4.1cc were 43.7% and 7.7%, respectively. ST V40 was also a significant predictive factor of acute GI toxicity (p = 0.002). IMRT could reduce acute GI toxicities in CRT with S-1 including prophylactic regions for pancreatic cancer. Acute GI toxicities may be affected by moderate to high doses to the ST.

Feasibility of Differential Dose-Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence.

The purpose of this study is to introduce differential dose−volume histogram (dDVH) features into machine learning for radiation pneumonitis (RP) prediction and to demonstrate the predictive performance of the developed model based on integrated cumulative dose−volume histogram (cDVH) and dDVH features. Materials and methods: cDVH and dDVH features were calculated for 153 patients treated for non-small-cell lung cancer with 60−66 Gy and dose bins ranging from 2 to 8 Gy in 2 Gy increments. RP prediction models were developed with the least absolute shrinkage and selection operator (LASSO) through fivefold cross-validation. Results: Among the 152 patients in the patient cohort, 41 presented ≥grade 2 RP. The interdependencies between cDVH features evaluated by Spearman's correlation were significantly resolved by the inclusion of dDVH features. The average area under curve for the RP prediction model using cDVH and dDVH model was 0.73, which was higher than the average area under curve using cDVH model for 0.62 with statistically significance (p < 0.01). An analysis using the entire set of regression coefficients determined by LASSO demonstrated that dDVH features represented four of the top five frequently selected features in the model fitting, regardless of dose bin. Conclusions: We successfully developed an RP prediction model that integrated cDVH and dDVH features. The best RP prediction model was achieved using dDVH (dose bin = 4 Gy) features in the machine learning process.

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with 81m Kr-gas SPECT ventilation imaging.

PURPOSE: This study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI). METHODS: A total of 71 cases that underwent single-photon emission CT 81m Kr-gas ventilation (SPECT V) and CT imaging were included. Sixty cases were assigned to the training and validation sets, and the remaining 11 cases were assigned to the test set. To directly transform three-dimensional (3D) CT (free-breathing CT) images to SPECT V images, a DL-based model was implemented based on the U-Net architecture. The input and output data were 3DCT- and SPECT V-masked, respectively, except for whole-lung volumes. These data were rearranged in voxel size, registered rigidly, cropped, and normalized in preprocessing. In addition to a standard estimation method (i.e., without dropout during the estimation process), a Monte Carlo dropout (MCD) method (i.e., with dropout during the estimation process) was used to calculate prediction uncertainty. To evaluate the two models' (CTVIMCD U-Net , CTVIU-Net ) performance, we used fivefold cross-validation for the training and validation sets. To test the final model performances for both approaches, we applied the test set to each trained model and averaged the test prediction results from the five trained models to acquire the mean test result (bagging) for each approach. For the MCD method, the models were predicted repeatedly (sample size = 200), and the average and standard deviation (SD) maps were calculated in each voxel from the predicted results: The average maps were defined as test prediction results in each fold. As an evaluation index, the voxel-wise Spearman rank correlation coefficient (Spearman rs ) and Dice similarity coefficient (DSC) were calculated. The DSC was calculated for three functional regions (high, moderate, and low) separated by an almost equal volume. The coefficient of variation was defined as prediction uncertainty, and these average values were calculated within three functional regions. The Wilcoxon signed-rank test was used to test for a significant difference between the two DL-based approaches. RESULTS: The average indexes with one SD (1SD) between CTVIMCD U-Net and SPECT V were 0.76 ± 0.06, 0.69 ± 0.07, 0.51 ± 0.06, and 0.75 ± 0.04 for Spearman rs , DSChigh , DSCmoderate , and DSClow , respectively. The average indexes with 1SD between CTVIU-Net and SPECT V were 0.72 ± 0.05, 0.66 ± 0.04, 0.48 ± 0.04, and 0.74 ± 0.06 for Spearman rs , DSChigh , DSCmoderate , and DSClow , respectively. These indexes between CTVIMCD U-Net and CTVIU-Net showed no significance difference (Spearman rs , p = 0.175; DSChigh , p = 0.123; DSCmoderate , p = 0.278; DSClow , p = 0.520). The average coefficient of variations with 1SD were 0.27 ± 0.00, 0.27 ± 0.01, and 0.36 ± 0.03 for the high-, moderate-, and low-functional regions, respectively, and the low-functional region showed a tendency to exhibit larger uncertainties than the others. CONCLUSION: We evaluated DL-based framework for estimating lung-functional ventilation images only from CT images. The results indicated that the DL-based approach could potentially be used for lung-ventilation estimation.

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor regression and thereby facilitate ART. Primary gross tumor volume (GTVp) was monitored in 96 patients and nodal GTV (GTVn) in 79 patients during treatment. All patients underwent two computed tomography (CT) scans: one before the start of radiotherapy for initial planning and one during radiotherapy for boost planning. Patients were assigned to regression and nonregression groups according to their median tumor regression rate (ΔGTV/treatment day from initial to boost CT scan). We input a GTV image into the convolutional neural network model, which was pretrained using natural image datasets, via transfer learning. The deep features were extracted from the last fully connected layer. To clarify the prognostic power of the deep features, machine learning models were trained. The models then predicted the regression and nonregression of GTVp and GTVn and evaluated the predictive performance by 0.632 + bootstrap area under the curve (AUC). Predictive performance for GTVp regression was highest using the InceptionResNetv2 model (mean AUC = 0.75) and that for GTVn was highest using NASNetLarge (mean AUC = 0.73). Both models outperformed the handcrafted radiomics features (mean AUC = 0.63 for GTVp and 0.61 for GTVn) or clinical factors (0.64 and 0.67, respectively). DLR may facilitate ART for improved radiation side-effects and target coverage.

Durvalumab after chemoradiotherapy for locally advanced non-small cell lung cancer prolonged distant metastasis-free survival, progression-free survival and overall survival in clinical practice.

BACKGROUND: In clinical practice, the effect of durvalumab and radiation pneumonitis (RP) on survival after intensity-modulated radiotherapy (IMRT) is not fully understood. The purpose of this retrospective study was to investigate factors related to distant metastasis-free survival (DMFS), progression-free survival (PFS) and overall survival (OS) after IMRT for locally advanced non-small cell lung cancer (LA-NSCLC). METHODS: All patients who were treated with conventional fractionated IMRT for LA-NSCLC between April 2016 and March 2021 were eligible. Time-to-event data were assessed by using the Kaplan-Meier estimator, and the Cox proportional hazards model was used for prognostic factor analyses. Factors that emerged after the start of IMRT, such as durvalumab administration or the development of RP, were analysed as time-dependent covariates. RESULTS: A total of 68 consecutive patients treated with conventional fractionated IMRT for LA-NSCLC were analysed. Sixty-six patients completed radiotherapy, 50 patients received concurrent chemotherapy, and 36 patients received adjuvant durvalumab. During the median follow-up period of 14.3 months, 23 patients died, and tumour progression occurred in 37 patients, including 28 patients with distant metastases. The 1-year DMFS rate, PFS rate and OS rate were 59.9%, 48.7% and 84.2%, respectively. Grade 2 RP occurred in 16 patients, grade 3 in 6 patients and grade 5 in 1 patient. The 1-year cumulative incidences of grade 2 or higher RP and grade 3 or higher RP were 33.8% and 10.3%, respectively. The results of multivariate analyses showed that durvalumab had a significantly lower hazard ratio (HR) for DMFS, PFS and OS (HR 0.31, p < 0.01; HR 0.33, p < 0.01 and HR 0.32, p = 0.02), respectively. Grade 2 or higher RP showed significance for DMFS and a nonsignificant trend for OS (HR 2.28, p = 0.04 and HR 2.12, p = 0.13), respectively, whereas a higher percentage of lung volume receiving 20 Gy or higher was significant for PFS (HR 2.25, p = 0.01). CONCLUSIONS: In clinical practice, durvalumab administration following IMRT with concomitant chemotherapy showed a significant survival benefit. Reducing the risk of grade 2 or higher RP would also be beneficial.

Evaluation of the electron transport algorithm in magnetic field in EGS5 Monte Carlo code.

PURPOSE: To evaluate the accuracy of electron transport in the magnetic field of Electron Gamma Shower version 5 (EGS5) by using the special Fano cavity test. METHODS: To simulate electron transport in the magnetic field, the trajectory of the electron was reconstructed with a short step length to restrict fractional energy loss, and the maximum user step length (mxustep) was set at 0.01 cm or 0.001 cm. For the special Fano cavity test, three-layer slab Fano test geometry was used, and uniform and isotropic per unit mass mono-energetic electrons with 0.01, 0.1, 1.0, and 10 MeV were permitted from the central axis of geometry in 0.35 T and 1.5 T. Furthermore, the magnetic field strength was scaled based on the mass density of the material. The relative difference between the calculated dose to gap and the theoretical value was evaluated. Furthermore, the special Fano cavity test was also performed using EGSnrc with the electron-enhanced electric and magnetic field macros under the same conditions, and the results were compared with those of EGS5. RESULTS: Deviations in 0.35 T were within 0.3% regardless of the parameter settings. In 1.5 T, stable results within 0.3% were obtained using 0.001 cm as the mxustep, except for one at 10 MeV. Further, the accuracy of EGSnrc was within 0.2%, except for 10 MeV for a 0.2-cm gap in 1.5 T. CONCLUSIONS: EGS5 with the appropriate parameter settings enable electron transport in magnetic fields similar with the accuracy of EGSnrc.

Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features.

In this article, we highlight the fundamental importance of the simultaneous use of dose-volume histogram (DVH) and dose-function histogram (DFH) features based on functional images calculated from 4-dimensional computed tomography (4D-CT) and deformable image registration (DIR) in developing a multivariate radiation pneumonitis (RP) prediction model. The patient characteristics, DVH features and DFH features were calculated from functional images by Hounsfield unit (HU) and Jacobian metrics, for an RP grade ≥ 2 multivariate prediction models were computed from 85 non-small cell lung cancer patients. The prediction model is developed using machine learning via a kernel-based support vector machine (SVM) machine. In the patient cohort, 21 of the 85 patients (24.7%) presented with RP grade ≥ 2. The median area under curve (AUC) was 0.58 for the generated 50 prediction models with patient clinical features and DVH features. When HU metric and Jacobian metric DFH features were added, the AUC improved to 0.73 and 0.68, respectively. We conclude that predictive RP models that incorporate DFH features were successfully developed via kernel-based SVM. These results demonstrate that effectiveness of the simultaneous use of DVH features and DFH features calculated from 4D-CT and DIR on functional image-guided radiotherapy.

A Pilot Study of Synchronization of Respiration-Induced Motions in the Duodenum and Stomach for the Primary Tumor in Radiation Therapy for Pancreatic Cancer Using 4-Dimensional Computed Tomography.

PURPOSE: We investigated the synchronization of respiration-induced motions at the primary tumor and organs at risk at radiation planning for pancreatic cancer. METHODS AND MATERIALS: Four-dimensional computed tomography images were acquired under the condition of shallow free breathing in patients with pancreatic cancer. The gross tumor volume (GTV), duodenum (DU), and stomach (ST) were contoured. The center of mass was computed for each 4-dimensional volume of interest. The respiration dependence of coordinates for the center of each volume of interest was computed relative to its location at the 50% (maximum exhalation) phase. Based on the shift of the GTV, we investigated the synchronization of respiration-induced motions between each contouring target. We examined the differences in the volume averaged dose to the ST and DU in each respiratory phase. RESULTS: Nine patients with pancreatic cancer were analyzed in this study. The mean maximum 3-dimensional excursions at the GTV, DU, and ST were 9.6, 9.8, and 11.4 mm, respectively. At phase 0% and 90% (inhale phases), mean distance changes in the positional relationship with the GTV were 0.3 and 0.7 mm respectively for the DU and -2.5 and -2.4 mm respectively for the ST. There was no significant respiration associated change (RAC) between each respiratory phase in the DU (P = .568), and there was a significant RAC in the ST (P < .001). There was a significant RAC of the volume averaged dose to the ST (P = .023). CONCLUSIONS: Our results indicate that the DU but not the ST might move synchronously with GTV due to respiration.

Evaluation of the dosimetric impact of heart function-based volumetric modulated arc therapy planning in patients with esophageal cancer.

Radiotherapy for esophageal cancer entails high-dose irradiation of the myocardium owing to its close anatomical proximity to the esophagus. This study aimed to evaluate the dosimetric impact of functional avoidance planning for the myocardium with volumetric-modulated arc therapy (VMAT) in patients with esophageal cancer and determine the feasibility of functional planning. Ten patients with early stage esophageal cancer were included in this study. The prescribed dose was 60 Gy administered in 30 fractions. An experienced physician contoured the left ventricle (LV) of the myocardium. For each patient, an anatomical plan (non-LV-sparing plan) and a functional plan (LV-sparing plan) were created using the VMAT. In the functional plan, the mean percentage of LV volume receiving a dose of ≥ 30 and ≥ 40 Gy was 6.0% ± 6.7% and 2.4% ± 2.7%, respectively, whereas in the anatomical plan, they were 11.7% ± 13.1% and 4.9% ± 6.5%, respectively. There were no significant differences with respect to the dose to the hottest 1 cm3 of the planning target volume (PTV) and the minimum dose of the gross tumor volume and the dosimetric parameters of other normal tissues between the anatomical and functional plans. We compared the anatomical and functional plans of patients with esophageal cancer undergoing VMAT. Our results demonstrated that the functional plan reduced the dose to the LV with no significant differences in the organs at risk and PTV, indicating that avoidance planning can be safely performed when administering VMAT in patients with esophageal cancer.

Dose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy.

PURPOSE: This study aimed to develop a deep convolutional neural network (CNN)-based dose distribution conversion approach for the correction of the influence of a magnetic field for online MR-guided adaptive radiotherapy. METHODS: Our model is based on DenseNet and consists of two 2D input channels and one 2D output channel. These three types of data comprise dose distributions without a magnetic field (uncorrected), electron density (ED) maps, and dose distributions with a magnetic field. These data were generated as follows: both types of dose distributions were created using 15-field IMRT in the same conditions except for the presence or absence of a magnetic field with the GPU Monte Carlo dose in Monaco version 5.4; ED maps were acquired with planning CT images using a clinical CT-to-ED table at our institution. Data for 50 prostate cancer patients were used; 30 patients were allocated for training, 10 for validation, and 10 for testing using 4-fold cross-validation based on rectum gas volume. The accuracy of the model was evaluated by comparing 2D gamma-indexes against the dose distributions in each irradiation field with a magnetic field (true). RESULTS: The gamma indexes in the body for CNN-corrected uncorrected dose against the true dose were 94.95% ± 4.69% and 63.19% ± 3.63%, respectively. The gamma indexes with 2%/2-mm criteria were improved by 10% in most test cases (99.36%). CONCLUSIONS: Our results suggest that the CNN-based approach can be used to correct the dose-distribution influences with a magnetic field in prostate cancer treatment.