Feasibility of treatment planning for 4D-CT high ventilation functional lung avoided radiotherapy in thoracic cancer / 中华放射医学与防护杂志

Objective:To establish a radiotherapy treatment planning process of high ventilation functional lung avoided (HVFLA) for thoracic tumors based on 4D-CT lung ventilation functional images and determine the treatment planning strategy of HVFLA radiotherapy, and so as to provide support for the clinical trials of HVFLA radiotherapy in thoracic cancer patients.Methods:A deep learning-based 4D-CT lung ventilation functional imaging model was established and integrated into the radiotherapy treatment planning process. Furthermore, ten thoracic cancer patients with 4D-CT simulation positioning were retrospectively enrolled in this study. The established model was used to obtain the 4D-CT lung ventilation functional imaging for each patient. According to the relative value of lung ventilation, the lung ventilation areas are equally segmented into high, medium and low lung ventilation and then imported them into Pinnacle 3 treatment planning system. According to the prescription dose of target and dose constraints of organ at risks (OARs), the clinical and HVFLA treatment plans were designed for each patient using volumetric modulated radiotherapy technique, and each plan should meet the clinical requirements and adding dose constraints of high ventilation functional lung for HVFLA plan. The dosimetric indexes of the target, OARs (lungs, heart and cord) and high functional lung (HFL) were used to evaluated the plan quality. The dosimetric indexes included D2, D98 and mean dose of target, V5, V10, V20, V30 and mean dose of lungs and HFL, V30, V40 and mean dose of heart, and D1 cm 3 of cord. Paired samples t-test was used for statistical analysis of the two groups of plans. Results:The target and OARs of the clinical plan and HVFLA plan meet the clinical requirements. The HVFLA plan resulted in a statistically significant reduction in the mean dose, V5, V10, V20, and V30 of the high functional lung by 1.2 Gy, 5.9%, 4.2%, 2.6%, and 2.3%, respectively ( t=-8.07, 4.02, -6.02, -7.06, -6.77, P<0.05). There was no statistical difference in the dosimetric indexes of lungs, heart and cord. Conclusions:We established the treatment planning process of HVFLA radiotherapy based on 4D-CT lung ventilation functional images. The HVFLA plan can effectively reduce the dose of HFL, while the doses of lungs, heart and cord had no significant difference compared with the clinical plan. The strategy of HVFLA radiotherapy planning is feasible to provide support for the implementation of HVFLA radiotherapy in thoracic cancer patients.

Feasibility of acceptance of multiple accelerators using Elekta AGL standard procedures / 中华放射肿瘤学杂志

Objective:To verify the feasibility of using Elekta accelerated go live (AGL) standard process for the acceptance of multiple accelerators.Methods:The beams of three accelerators were adjusted by PTW Beamscan three-dimensional water tank to reach the AGL standard. Dose verification was performed for three accelerators that met AGL standards. A simple field test example from Cancer Hospital Chinese Academy of Medical Sciences was used to compare the MapCheck 3 surface dose measurement results with the surface dose calculated by the same accelerator model. Images of 10 patients including head and neck, esophagus, breast, lung and rectum were randomly selected. volumetric-modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) treatment techniques were used for planning design, and the measured dose of ArcCheck was compared with the planned dose calculated by the same accelerator model. One-way ANOVA was used to statistically analyze the passing rates of two-dimensional and three-dimensional dose verification.Results:The 6 MV X-ray percentage depth dose at 10 cm underwater (PDD 10) of three accelerators was 67.45%, 67.36%, 67.47%, and the maximum deviation between the three accelerators was 0.11%. The 6 MV flattenting filter free (FFF) mode X-ray PDD 10 was 67.33%, 67.20%, 67.20%, and the maximum deviation between the three accelerators was 0.13%. All required discrete point doses on each energy 30 cm×30 cm Profile spindle of the three accelerator X-rays deviated less than ±1% from the standard data. Absolute γ analysis was performed on the results of MapCheck 3 two-dimensional dose matrix validation. Under the 10% threshold of 2 mm/3% standard, the average passing rate of the test cases in Cancer Hospital Chinese Academy of Medical Sciences was above 99%, and the difference was not statistically significant ( P>0.05). Absolute γ analysis was performed on the ArcCheck verification results. Under the 10% threshold, the pass rate of 2 mm/3% was all above 95%, the maximum average passing rate of the three accelerators with different energy and different treatment techniques was 0.28% (6 MV, VMAT), 0.19%(6 MV FFF, VMAT), 0.56% (6 MV, IMRT) and 0.05% (6 MV FFF, IMRT), and the difference was not statistically significant ( P>0.05). Conclusion:Compared with traditional accelerator acceptance process, the acceptance time of each accelerator is shortened by 4-6 weeks by using the AGL standard process, and the radiotherapy plan of patients can be interchangeably executed among different accelerators.

Prediction of anatomical images during radiotherapy of nasopharyngeal carcinoma with deep learning method / 中华放射肿瘤学杂志

Objective:To develop a deep learning method to predict the anatomical images of nasopharyngeal carcinoma patients during the treatment course, which could detect the anatomical variation for specific patients in advance.Methods:Imaging data including planning CT (pCT) and cone-beam CT (CBCT) for each fraction of 230 patients with T 3-T 4 staging nasopharyngeal carcinoma who treated in Cancer Hospital Chinese Academy of Medical Sciences from January 1, 2020 to December 31, 2022 were collected. The anatomical images of week k+1 were predicted using a 3D Unet model with inputs of pCT, CBCT on days 1-3, and CBCT of weeks 2- k. In this experiment, we trained four models to predict anatomical images of weeks 3-6, respectively. The nasopharynx gross tumor volume (GTV nx) and bilateral parotid glands were delineated on the predicted and real images (ground truth). The performance of models was evaluated by the consistence of the delineation between the predicted and ground truth images. Results:The proposed method could predict the anatomical images over the radiotherapy course. The contours of interest in the predicted image were consistent with those in the real image, with Dice similarity coefficient of 0.96, 0.90, 0.92, mean Hausdorff distance of 3.28, 4.18 and 3.86 mm, and mean distance to agreement of 0.37, 0.70, and 0.60 mm, for GTV nx, left parotid, and right parotid, respectively. Conclusion:This deep learning method is an accurate and feasible tool for predicting the patient's anatomical images, which contributes to predicting and preparing treatment strategy in advance and achieving individualized treatment.

Predicting respiratory motion using an Informer deep learning network / 中华放射医学与防护杂志

Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.

Challenge of shielding design for FLASH radiotherapy / 中华放射医学与防护杂志

Compared with conventional radiotherapy, FLASH radiotherapy has advantages in protecting normal tissues, while the dose rate is increased by more than 100 times. If the shielding design of the treatment room is carried out according to the existing standard, the thickness and cost of the shielding wall will be significantly increased, or even hardly to meet the requirement of the standards, resultsing in the failure of the application of FLASH radiotherapy. By investigating the domestic and foreign standards and literature, this paper analyzes the challenges brought by FLASH radiotherapy technology to the shielding design of radiotherapy treatment room in China. Dose rate control standards adopted by different countries in the shielding design are emphatically compared as well. In several countries, the average dose rate under the actual treatment conditions was considered in the shielding design. In China, the method of instantaneous dose rate taking acount of occupancy factor is adopted. However, if FLASH radiotherapy technology is applied, the requirement of instantaneous dose rate will be difficult to meet. In order to improve the high dose rate radiotherapy technology such as FLASH radiotherapy, the revision of the existing standards is advised if the authorized limits are not changed. To use the average dose rate limit within a certain period of time for control, or to raise the control standard in the case of flash radiotherapy, are also avaliable.

A deep learning method for generating pseudo-CT by cone beam CT in radiotherapy / 中华放射肿瘤学杂志

Objective:To investigate the pseudo-CT generation from cone beam CT (CBCT) by a deep learning method for the clinical need of adaptive radiotherapy.Methods:CBCT data from 74 prostate cancer patients collected by Varian On-Board Imager and their simulated positioning CT images were used for this study. The deformable registration was implemented by MIM software. And the data were randomly divided into the training set ( n=59) and test set ( n=15). U-net, Pix2PixGAN and CycleGAN were employed to learn the mapping from CBCT to simulated positioning CT. The evaluation indexes included mean absolute error (MAE), structural similarity index (SSIM) and peak signal to noise ratio (PSNR), with the deformed CT chosen as the reference. In addition, the quality of image was analyzed separately, including soft tissue resolution, image noise and artifacts, etc. Results:The MAE of images generated by U-net, Pix2PixGAN and CycleGAN were (29.4±16.1) HU, (37.1±14.4) HU and (34.3±17.3) HU, respectively. In terms of image quality, the images generated by U-net and Pix2PixGAN had excessive blur, resulting in image distortion; while the images generated by CycleGAN retained the CBCT image structure and improved the image quality.Conclusion:CycleGAN is able to effectively improve the quality of CBCT images, and has potential to be used in adaptive radiotherapy.

Research progress on image standard database of artificial intelligence-assisted radiotherapy for lung cancer / 中华放射肿瘤学杂志

Lung cancer is the malignant tumor with the highest mortality rate in the world. Radiotherapy plays an important role in the comprehensive treatment of lung cancer. With the continuous advancement of radiotherapy technology and equipment, it has become one of the effective therapeutic options for lung cancer. In recent years, artificial intelligence technology has developed rapidly and has been widely applied in clinical practice, especially in the diagnosis and treatment of lung cancer imaging. The image database can be obtained by sorting and summarizing the images, which can be used in clinical work and scientific research. In this article, the application of artificial intelligence in lung cancer radiotherapy imaging and lung cancer imaging database was reviewed, aiming to provide reference for the construction of artificial intelligence radiotherapy imaging database for lung cancer.

Noise reduction in low-dose computed tomography with noise equivalent image and deep learning / 中华放射医学与防护杂志

Objective:To investigate the method of simulating low-dose CT (LDCT) images using routine dose level scanning mode to generate LDCT images with correspondence to the routine dose CT (RDCT) images in the training sets for deep learning model, which would be used for LDCT noise reduction.Methods:The CT images reconstructed by different algorithms in Philips CT Big Core had different noise levels, where the noise was larger with iDose 4 algorithm and lower with IMR(knowledge-based iterative model reconstruction)algorithm. A new method of replacing LDCT image with noise equivalent reconstructed image was proposed. The uniform module of CTP712 was scanned with the exposure of 250 mAs for RDCT, 35 mAs for LDCT. The images were reconstructed using IMR algorithm for LDCT images and iDose 4 algorithm at multiple noise reduction levels for RDCT images, respectively. The noise distribution of each image set was analyzed to find the noise equivalent images of LDCT. Then, RDCT images, those selected images were used for training cycle-consistent adversarial networks (CycleGAN)model, and the noise reduction ability of the proposed method on real LDCT images of phantom was tested. Results:The RDCT images generated with iDose 4 level 1 could substitute the LDCT images reconstructed with IMR algorithm. The radiation dose was reduced by 86% in low dose scanning. Using CycleGAN model, the noise reduction degree was 45% for uniform module, and 50%, 13%, 7% for CIRS-SBRT 038 phantom in the specific regions of brain, spinal cord, bone, respectively. Conclusions:Equivalent noise level reconstructed images could potentially serve as the alternative of LDCT images for deep learning network training to avoid additional radiation dose. The generated CT images had substantially reduced noise relative to that of LDCT.

Preliminary study of clinical application of magnetic resonance linear accelerator in liver malignancies / 中华放射肿瘤学杂志

Objective:To investigate the workflow, efficacy and safety of MR-Linac in liver malignancies.Methods:Clinical data of 15 patients with hepatocellular carcinomas (HCC) or liver metastases treated with MR-Linac between November 2019 and July 2021 were retrospectively analyzed. The workflow of MR-Linac was investigated and image identification rate was analyzed. Patients were followed up for response and toxicity assessment.Results:Fifteen patients (6 HCC, 8 liver metastases from colorectal cancer, 1 liver metastasis from breast cancer) were enrolled. A total of 21 lesions were treated, consisting of 10 patients with single lesion, 4 patients with double lesions and 1 patient with triple lesions. The median tumor size was 2.4 cm (0.8-9.8 cm). The identification rate for gross tumor volume (GTV) in MR-Linac was 13/15. Although GTV of two patients were unclearly displayed in MR-Linac images, the presence of adjacent blood vessel and bile duct assisted the precise registration. All the patients were treated with stereotactic body radiation therapy (SBRT). For HCC, the median fraction dose for GTV or planning gross tumor volume (PGTV) was 6 Gy (5-10 Gy) and the median number of fractions was 9(5-10). The median total dose was 52 Gy (50-54 Gy) and the median equivalent dose in 2 Gy fraction (EQD 2Gy) at α/ β= 10 was 72 Gy (62.5-83.3 Gy). For liver metastases, the median fraction dose for GTV or PGTV was 5 Gy (5-10 Gy) and the median number of fractions was 10(5-10). The median total dose was 50 Gy (40-50 Gy) and the median EQD 2Gy at α/ β=5 was 71.4 Gy (71.4-107.1 Gy). At 1 month after SBRT, the in-field objective response rate (ORR) was 8/13 and the disease control rate was 13/13. At 3-6 months after SBRT, the in-filed ORR was increased to 6/6. During the median follow-up of 4.0 months (0.3-11.6), 4-month local progression-free survival, progression-free survival and overall survival were 15/15, 11/15 and 15/15, respectively. Toxicities were mild and no grade 3 or higher toxicities were observed. Conclusions:MR-Linac provides a platform with high identification rates of liver lesions. Besides, the presence of adjacent blood vessel and bile duct also assists the precise registration. It is especially suitable for liver malignancies with promising local control and well tolerance.

Application and basic application process of MR accelerator in lung cancer / 中华放射肿瘤学杂志

Radiotherapy is one of the most important components of cancer treatment. Image-guided radiotherapy (IGRT) is the mainstream tool in the precision radiation oncology. Magnetic resonance (MR) accelerator can perform MRI for tumors during radiotherapy, deliver real-time tracing and monitoring of tumors and thus realize the MRI-guided adaptive radiotherapy. Here, the latest research status and clinical application of MR accelerator in lung cancer were reviewed.

Clinical dosimetry commissioning of 1.5 T MR-linac / 中华放射肿瘤学杂志

Objective:To introduce the clinical dosimetry commissioning methods and results of the 1.5 T MR-linac.Methods:In May, 2019, an Elekta Unity 1.5 T MR-linac was installed in Cancer Hospital, Chinese Academy of Medical Sciences and dosimetry commissioning was performed with magnetic field compatible measuring instruments. Commissioning items include absolute dose calibration, data acquisition and planning system model verification.Results:Absolute dose calibration in magnetic field should be corrected by magnetic field correction factor. The standard output dose of Unity was 87 cGy. Gamma analysis (3%/2 mm) was performed on the beam collection data and the planning system calculation data. The average pass rate of dose verification of standard field test cases was 96.41%, and the TG119 test case was 98.24%. The IROC end to end test case was 97.5%(7%/4 mm).Conclusions:The planning system model and the beam collection data have good consistency. The dose verification results of the standard field and TG119 test cases meet the general tolerance limit requirements of the AAPM TG218 report, and the verification results of the IROC end-to-end test cases meet the IROC center standards.

Comparison of setup errors in supraclavicular regions of lung and esophageal cancer treated with radiotherapy / 中华放射肿瘤学杂志

Objective:To compare the setup errors in the supraclavicular regions of two different postures (arms placed on each side of the body, namely the body side group; arms crossed and elbows placed above forehead, namely the uplifted group) using the chest and abdomen flat frame fixation device in lung and esophageal cancer.Methods:Clinical data of patients with stage Ⅰ to Ⅳ lung or esophageal cancer who received three-dimensional radiotherapy with chest and abdomen flat frame fixation device in our institution from November 2020 to April 2021 were retrospectively analyzed. The setup errors of two postures were compared.Results:A total of 56 patients were included, including 31 patients (55%) in the body side group and 25 patients (45%) in the uplifted group. A total of 424 CBCTs were performed in the whole group. The overall setup errors in the X, Y and Z directions were similar in both groups ( P>0.05). The setup errors of sternoclavicular joint in the X and RZ directions in the body side group were significantly smaller than those in the uplifted group [(0.163±0.120) cm vs. (0.209 ±0.152) cm, P=0.033; 0.715°±0.628° vs. 0.910°±0.753°, P=0.011]. The setup errors of acromioclavicular joint in the Y, Z and RZ directions in the body side group were significantly smaller than those in the uplifted group [(0.233±0.135) cm vs. (0.284±0.193) cm, P=0.033; (0.202±0.140) cm vs. (0.252±0.173) cm, P=0.005; 0.671°±0.639° vs. 0.885°±0.822°, P=0.023]. The margins of target volume for setup errors were smaller in the X (0.45 cm vs. 0.54 cm) and Y (0.54 cm vs. 0.65 cm) directions of the sternoclavicular joint, as well as in the Y (0.59 cm vs. 0.78 cm) and Z directions (0.53 cm vs. 0.72 cm) of the acromioclavicular joint in the body side group. Conclusions:For lung and esophageal cancer patients requiring supraclavicular irradiation, the body side group yields smaller setup errors and corresponding margins of target volume than the uplifted group. In clinical practice, it is necessary to take comprehensive consideration of the accuracy of radiotherapy and additional radiation of the limbs to select appropriate posture.

Consensus on taxonomy of planning automation for radiotherapy / 中华放射肿瘤学杂志

Powered by big data and artificial intelligence, the research and clinical application of treatment planning automation for radiation therapy are rapidly growing. The application and supervision of planning automation systems necessitate careful consideration of different levels of automation, as well as the context for use. For autonomous vehicles, the levels of automation have been defined at home and abroad. Nevertheless, no such definitions exist for radiotherapy planning automation. To promote and standardize the development of radiotherapy planning automation and initiate discussion within the community, we developed this recommendation with reference to the taxonomy of driving automation for vehicles and divided the radiotherapy planning automation into six levels (level 1 to 6).

An automatic VMAT planning method for primary liver cancer radiotherapy based on predicting the feasibility DVH / 中华放射肿瘤学杂志

Objective:To establish an automatic planning method using volumetric-modulated arc therapy (VMAT) for primary liver cancer (PLC) radiotherapy based on predicting the feasibility dose-volume histogram (DVH) and evaluate its performance.Methods:Ten patients with PLC were randomly chosen in this retrospective study. Pinnacle Auto-Planning was used to design the VMAT automatic plan, and the feasibility DVH curve was obtained through the PlanIQ dose prediction, and the initial optimization objectives of the automatic plan were set according to the displayed feasible objectives interval. The plans were accessed according to dosimetric parameters of the planning target volume and organs at risk as well as the monitor units. All patients′ automatic plans were compared with clinically accepted manual plans by using the paired t-test. Results:There was no significant difference of the planning target volume D 2%, D 98%, D mean or homogeneity index between the automatic and manual plans ((58.55±2.81) Gy vs.(57.98±4.17) Gy, (47.15±1.58) Gy vs.(47.82±1.38) Gy, (53.14±0.95) Gy vs.(53.44±1.67) Gy and 1.15±0.05 vs. 1.14±0.07, all P>0.05). The planning target volume conformity index of the manual plan was slightly higher than that of the automatic plan (0.77±0.08 vs. 0.69±0.06, P<0.05). The mean doses of normal liver, V 30Gy, V 20Gy, V 10Gy, V 5Gy and V< 5Gy of the automatic plan were significantly better than those of the manual plan ((26.68±11.13)% vs.(28.00±10.95)%, (29.96±11.50)% vs.(31.89±11.51)%, (34.88±11.51)% vs.(38.66±11.67)%, (45.38±12.40)% vs.(50.74±13.56)%, and (628.52±191.80) cm 3vs.(563.15±188.39) cm 3, all P<0.05). The mean doses of the small intestine, the duodenum, and the heart, as well as lung V 10 of the automatic plan were significantly less than those of the manual plan ((1.83±2.17) Gy vs.(2.37±2.81) Gy, (9.15±9.36) Gy vs.(11.18±10.49) Gy, and (5.44±3.10) Gy vs.(6.25±3.26) Gy, as well as (12.70±7.08)% vs.(14.47±8.11)%, all P<0.05). Monitor units did not significantly differ between two plans ((710.67±163.72) MU vs.(707.53±155.89) MU, P>0.05). Conclusions:The automatic planning method using VMAT for PLC radiotherapy based on predicting the feasibility DVH enhances the quality for PLC plans, especially in terms of normal liver sparing. Besides, it also has advantages for the protection of the intestine, whole lung and heart.

Dosimetric comparison of three techniques in radiotherapy for breast cancer after modified mastectomy / 中华放射肿瘤学杂志

Objective:To analyze and compare the dosimetric differences based on volumetric-modulated arc therapy (VMAT), fixed field intensity-modulated radiotherapy (F_IMRT), and electron irradiation combined with VMAT (E&VMAT) in radiotherapy for breast cancer after modified mastectomy, aiming to provide reference for clinical selection of treatment plan.Methods:Ten patients with the left breast cancer who received radiotherapy after modified mastectomy were randomly selected. The target areas included chest wall and supraclavicular region, and the prescribed dose was 43.5 Gy in 15 fractions (2.9 Gy/F). Based on the Pinnacle 3 planning system, the VMAT, F_IMRT and E&VMAT plans (electron beam for chest wall, VMAT for supraclavicular area) were designed for each patient. The conformity and homogeneity of the target areas, the dose of organs at risk and treatment time were compared. Results:The VMAT plan could improve the dose distribution of the target areas. The conformity index and homogeneity index of the target dose were significantly better than those of the F_IMRT and E&VMAT plans (all P<0.05). The average dose, V 30Gy, V 20Gy, V 10Gy of the left lung in the VMAT plan were significantly better than those in the F_IMRT and E&VMAT plans (all P<0.05). The V 5Gy of the left lung in the VMAT plan was significantly better than that in the F_IMRT plan ( P<0.05). There was no statistical difference in the V 5Gy of the left lung between the VMAT and E&VMAT plans ( P>0.05). The heart, right breast and right lung of the VMAT plan could meet the clinical dose limit requirements. The treatment time of the VMAT, F_IMRT and E&VMAT plans was (326±27) s, (1 082±169) s, and (562±48) s, respectively. Conclusions:Compared with the F_IMRT and E&VMAT plans, the VMAT plan has better quality and shorter treatment time. VMAT plan has higher value in clinical application compared with the F_IMRT and E&VMAT plans.

Clinical application of machine learning in radiation oncology / 中华放射医学与防护杂志

Radiation therapy is one of the main treatment methods for cancer. Machine learning can be used in all aspects of clinical practice in radiation therapy, including clinical decision support, automatic segmentation of target volumes, prediction of treatment efficacy and side effects. Despite the challenges of lacking structured data and poor interpretability of models, the application of machine learning in radiotherapy will become increasingly profound and extensive. This review contains three aspects: introduction of machine learning, the clinical application of machine learning in radiotherapy, challenges and solutions.

A method of determining the prescription isodose line for stereotactic radiotherapy VMAT plan / 中华放射医学与防护杂志

Objective:To establish a method of determining the prescription isodose line for steretactic radiotherapy (SRT) volumetric modulated arc radiotherapy (VMAT) plan.Methods:Eight patients with brain metastases treated with SRT were enrolled. The volume of planning target volume (PTV) ranged from 3.5 to 11.7 cm 3 (median 6.1 cm 3). Reference VMAT plans were designed for each patient with identical prescription dose. Then, the original PTV was contracted by a few millimeters to form a new target for optimization to get plans with different IDLs. The minimum margin which was needed to be contracted to achieve optimal IDL range for each PTV was also studied. Results:To achieve the optimal IDL range, 4 or 5 mm PTV contraction was needed for all patients, and the average IDL was (66.05±0.02)%. Compared with reference plans, the average gradient index (GI) of optimal IDL plans decreased by 20% from 4.05±0.39 to 3.37±0.24 ( Z=-2.521, P<0.05). The V40, V30, V5 and mean dose in normal brain tissue decreased by 11.5% ( Z=-1.973, P<0.05), 7.2% ( Z=-2.105, P<0.05), 12.8% ( Z=-2.521, P<0.05) and 8.1%, respectively ( Z=-2.382, P<0.05), and there was no statistically significant difference with V20, V10 and conformity index ( P>0.05). Conclusions:The optimization of IDL for SRT-VMAT plan can be achieved with the method of contracting PTV to form new target for planning. 4 or 5 mm is needed to be contracted to achieve the optimal IDL range, and to get lower GI and protect the normal brain tissue.

Clinical application and evaluation of automatic segmentation model based on deep learning for breast cancer radiotherapy / 中华放射肿瘤学杂志

Objective In this study,the deep learning algorithm and the commercial planning system were integrated to establish and validate an automatic segmentation platform for clinical target volume (CTV) and organs at risk (OARs) in breast cancer patients.Methods A total of 400 patients with left and right breast cancer receiving radiotherapy after breast-conserving surgery in Cancer Hospital CAMS were enrolled in this study.A deep residual convolutional neural network was used to train CTV and OARs segmentation models.An end-to-end deep learning-based automatic segmentation platform (DLAS) was established.The accuracy of the DLAS platform delineation was verified using 42 left breast cancer and 40 right breast cancer patients.The overall Dice Similarity Coefficient (DSC) and the average Hausdorff Distance (AHD) were calculated.The relationship between the relative layer position and the DSC value of each layer (DSC_s) was calculated and analyzed layer-by-layer.Results The mean overall DSC and AHD of global CTV in left/right breast cancer patients were 0.87/0.88 and 9.38/8.71 mm.The average overall DSC and AHD range for all OARs in left/right breast cancer patients were ranged from 0.86 to 0.97 and 0.89 to 9.38 mm.The layer-by-layer analysis of CTV and OARs reached 0.90 or above,indicating that the doctors were only required to make slight or no modification,and the DSC_s ≥ 0.9 of CTV automatic delineation accounted for approximately 44.7％ of the layers.The automatic delineation range for OARs was 50.9％-89.6％.For DSC_s ＜ 0.7,the DSC_s values of CTV and the regions of interest other than the spinal cord were significantly decreased in the boundary regions on both sides (layer positions 0-0.2,and 0.8-1.0),and the level of decrease toward the edge was more pronounced.The spinal cord was delineated in a full-scale manner,and no significant decrease in DSC_s was observed in a particular area.Conclusions The end-to-end automatic segmentation platform based on deep learning can integrate the breast cancer segmentation model and achieve excellent automatic segmentation effect.In the boundary areas on both sides of the superior and inferior directions,the consistency of the delineation decreases more obviously,which needs to be further improved.

A study on fast cone beam CT / 中华放射肿瘤学杂志

Objective:To evaluate the effect of fast cone beam CT (CBCT) scan mode on image quality and registration results, and to establish the scanning pre-settings for fast CBCT.Methods:Three scanning modes were utilized to the CBCT phantom, and the registration accuracy and image quality were quantitatively evaluated. The correlation and consistency of measurement results under different scanning modes were further verified by 278 sets of CBCT data from 33 clinical tumor patients.Results:The maximum deviation between the measurement results of three scanning models and the actual value was 0.70 mm (0.51 mm on average). The measurement results of the same location were consistent among three scanning modes (0.00 mm). For the uniformity, the results of the normal mode were the best (3.62% on average), followed by the fast 1 mode (3.90% on average) and the fast 2 mode (4.84% on average). For the noise, the results of the normal mode were the best (15.69 on average), followed by the fast 2 mode (17.23 on average) and the fast 1 mode (21.74 on average). Regarding the high contrast resolution, the measurement results of three scanning modes were consistent (at least 3 pairs could be distinguished). For the low contrast resolution, the results of the fast 1 mode were the best (1.69 on average), followed by the normal mode (2.10 on average), and the fast 2 mode (2.31 on average). For the geometric accuracy, the measurement results of the three scanning modes were basically consistent with a mean deviation of 0.05 mm. The correlation of the measurement results between normal mode and fast 1 mode was the highest in clinical cases ( R2>0.90, P<0.01) with a high degree of consistency (95% consistency limit of the above two scanning modes< 1 mm threshold). Conclusion:Compared with the normal mode, the fast 1 mode can yield equivalent image quality, consistent registration results, faster scanning speed and lower scanning dose. Therefore, the fast 1 mode is recommended as the scan mode in clinical practice.

Dosimetric characteristics test of 1.5T magnetic resonance accelerator / 中华放射肿瘤学杂志

Objective:The Lorentz force produced by magnetic field deflects the paths of secondary electrons. The X-ray beam dosimetry characteristics of the magnetic resonance accelerator (MR-Linac) are different from conventional accelerators. The purpose of this study was to measure and analyze the X-ray beam dosimetry characteristics of 1.5T MR-Linac.Methods:In May 2019, our hospital installed a Unity 1.5T MR-Linac and measured it with magnetic field compatible tools. The measurement indexes include: surface dose, maximum dose point depth, beam quality, off-axis dose profile center, beam symmetry, penumbra width, output changes of different gantry angles.Results:The average surface dose was 40.48%, and the average maximum dose depth was 1.25 cm. The center of the 10 cm×10 cm beam field was offset by 1.47 mm to the x2 side and 0.3 mm to the y2 side. The x-axis symmetry was 101.33%, and the penumbra width on both sides was 6.86 mm and 7.14 mm, respectively. The y-axis symmetry was 100.85%, and the penumbra width on both sides was 5.92 mm and 5.95 mm, respectively. The maximum deviation of output dose with different gantry angles reached 1.50%. Conclusions:The surface dose of MR-Linac tend to be consistent, and the depth of the maximum dose point became shallower. The off-axis in the x-axis direction was shifted to the x2 side, which resulting in worse symmetry and penumbra asymmetry. The output dose at different angles has obvious variation and needs correction.