At present,precise radiotherapy has been widely used through the development with many years,but the existing technique still is limited by the limitation of tolerance dose of normal tissues,which cannot achieve the optimal goal of treating tumor.Flash radiotherapy(Flash-RT)is one kind of radiotherapy technique that uses the beam with ultra-high dose rate(UHDR)to conduct irradiation,which can furthest treat tumors while significantly reduce radiation injury of normal tissues.But until now,the biological mechanism,key physical parameters and triggering mechanism of Flash-RT are still unclear,and its principle and clinical translational application are still in the stage of research.This review clarified the technological advance and clinical translational application of Flash-RT research through summarized the relevant research of Flash-RT.

Radiotherapy is an important means to treat lung cancer,but it is easy to cause lung injury and reduce the quality of life of patients.Flash radiotherapy(FLASH-RT)has attracted attention due to its extremely short radiation duration and high dose rate,which can reduce toxicity of normal tissue while ensures treatment intensity of tumor.Whether Flash-RT can reduce radiation-induced lung injury has become an important research topic in recent years.Based on the literature analysis method,this review systematically assessed the effects and mechanisms of Flash-RT and radiotherapy with conventional dose rate on lung injury through searching relevant literatures at home and abroad,so as to provide scientific basis for the treatment of patients with lung cancer by reviewing the comparisons about the effects and mechanisms between Flash-RT and radiotherapy with conventional dose rate on lung injury.Compared with radiotherapy with conventional radiation rate,Flash-RT can significantly reduce lung injury and improve quality of life of patients.It is still demanded to explore the Flash-RT mechanism in future,so as to develop the Flash-RT instrument that is suitable for different tumors and to conduct larger-scale clinical researches.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.

Objective:To evaluate the safety and clinical efficacy of hypofractionated radiotherapy (HFRT) at 36.5 Gy in 10 fractions for the chest wall and reginal lymph nodes following modified radical mastectomy for breast cancer.Methods:This was a prospective, single-arm, phase Ⅱ clinical study. A total of 85 patients who received HFRT at 36.5 Gy in 10 fractions to the chest wall ± supraclavicular region following modified radical mastectomy for locally advanced breast cancer from March 2014 to December 2015 were included. The primary endpoint was radiotherapy toxicities. The secondary endpoints were locoregional failure-free survival (LRFFS), disease-free survival (DFS), and overall survival (OS).Results:The median follow-up period was 98 (94.0-109.0) months. Radiotherapy toxicities were mild. The incidence rates of grade 1 acute cutaneous and pulmonary toxicities were 52.9% and 40%, and those of grade 1 late cutaneous, pulmonary, and cardiac toxicities and upper extremity edema were 10.6%, 29.4%, 2.4%, and 21.2%, respectively. Only 1 (1.2%) patient suffered from grade 2 radiation-induced brachial plexus injury. Of the 85 patients, one patient had regional recurrence (supraclavicular lymph nodes), six patients had distant metastasis, and six patients died of breast cancer. The 9-year LRFFS, DFS, and OS were 97.7%, 91.8%, and 92.8%, respectively.Conclusions:HFRT at 36.5 Gy in 10 fractions following modified radical mastectomy for breast cancer is associated with mild toxicities. A phase Ⅲ study is necessary for validating HFRT's clinical efficacy.

Objective:To evaluate the dosimetric characteristics of Zap-X system and CyberKnife (CK) G4 system of stereotactic radiosurgery (SRS) for single brain metastasis.Methods:Twelve patients with single brain metastasis had been treated with CK were selected retrospectively. The prescribed dose of planning target volume (PTV) was 18-24 Gy for 1-3 fractions. The PTV was ranged from 0.44 to 11.52 cm 3. The 12 patients were re-planned in the Zap-X planning system using the same prescription dose and organs at risk constraints, and the prescription dose of PTV was normalized to 70% for both Zap-X and CK. The planning parameters and dosimetric parameters of PTV and organs at risk were compared and evaluated between two plans. All data were read at MIM Maestro. A paired Wilcoxon' signed-rank test was adopted for statistical analysis. A P value of less than 0.05 was considered as statistical significance. Results:For the target coverage, CK was significantly higher than Zap-X (99.14±0.57% vs. 97.55±1.34%, P<0.01), but Zap-X showed a higher conformity index (0.81±0.05 vs. 0.77±0.07, P<0.05), a lower Paddick gradient index (2.98±0.24 vs. 3.15±0.38), and a higher gradient score index (GSI) than CK. The total monitor unit (MU) of Zap-X was significantly lower than that of CK (11 627.63 ±5 039.53 vs. 23 522.16 ±4 542.12, P<0.01) and the treatment time was shorter than that of CK [(25.08 ±6.52) vs. (38.08 ±4.74) min, P<0.01]. Zap-X had lower dose volumes than CK for the dose of brain ( P<0.05). Zap-X had a lower D mean and D max of brainstem (both P<0.05), but a higher value of eyes and lens. For optic nerves and optic chiasm, there were no significant differences between two groups. In addition, for the protection of skin (V 22.5 Gy), Zap-X seemed better than CK [(4.15±4.48) vs. (4.37±4.50) cm 3, P<0.05]. Conclusions:For SRS treating single brain metastasis, Zap-X could provide a high quality plan equivalent to or even better than CK, especially reducing the treatment time. With continuous improvement and upgrading of Zap-X system, it may become a new SRS platform for the treatment of brain metastasis.

Objective:To compare the dosimetric characteristics of beams between Zap-X and G4 CyberKnife and provide reference for clinical application of Zap-X.Methods:PTW three-dimensional water tank and dosimetry diode ionization chamber were used to measure the two orthogonal off-axis ratio and field size at isocenter of 7 different collimators (5 mm, 7.5 mm, 10 mm, 12.5 mm, 15 mm, 20 mm and 25 mm) of Zap-X and CyberKnife at the water depth of maximum dose, 50 mm, 100 mm, and 200 mm. The penumbra, flatness, symmetry and field size under each parameter condition were analyzed by using PTW supporting software PTW MEPHYSTO (version 5.1). Data analysis and graph were performed using Origin 2021 software.Results:With the same collimator, the dose plateau area of Zap-X was wider than that of G4 CyberKnife, and the dose fall-off at the field edge of Zap-X system was faster. With the increase of the collimator, the penumbra of Zap-X and CyberKnife tended to become larger, and the flatness tended to become smaller, the penumbra and flatness of Zap-X were significantly smaller than those of CyberKnife. Both of them had excellent symmetry (<1%), and the symmetry results of CyberKnife (<0.39%) were better than that of Zap-X (0.99%). The accuracy of Zap-X collimator size at isocenter was better than that of CyberKnife.Conclusion:Compared with G4 CyberKnife, Zap-X system has smaller penumbra, better flatness and higher accuracy of collimator size, which is suitable for stereotactic radiosurgery.

To study an automatic plan(AP) method for radiotherapy after breast-conserving surgery based on TiGRT system and and compare with manual plan (MP). The dosimetry parameters of 10 patients and the evaluation of scoring table were analyzed, it was found that the targets dose of AP were better than that of MP, but there was no statistical difference except for CI, The V5, V20 and V30 of affected lungs and whole lungs in AP were lower than all that in MP, the Dmean of hearts was slightly higher than that of MP, but the difference was not statistically significant, the MU of AP was increase by 16.1% compared with MP, the score of AP evaluation was increase by 6.1% compared with MP. So the AP could be programmed and automated while ensuring the quality of the plan, and can be used to design the plans for radiotherapy after breast-conserving surgery.
Breast Neoplasms/surgery* ; Female ; Humans ; Mastectomy, Segmental ; Organs at Risk ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted ; Radiotherapy, Intensity-Modulated

The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency and repeatability. Therefore, a deep convolutional neural network (DCNN) is proposed for the automatic and accurate segmentation of head and neck organs at risk. The data of 496 patients with nasopharyngeal carcinoma were reviewed. Among them, 376 cases were randomly selected for training set, 60 cases for validation set and 60 cases for test set. Using the three-dimensional (3D) U-NET DCNN, combined with two loss functions of Dice Loss and Generalized Dice Loss, the automatic segmentation neural network model for the head and neck organs at risk was trained. The evaluation parameters are Dice similarity coefficient and Jaccard distance. The average Dice Similarity coefficient of the 19 organs at risk was 0.91, and the Jaccard distance was 0.15. The results demonstrate that 3D U-NET DCNN combined with Dice Loss function can be better applied to automatic segmentation of head and neck organs at risk.

Objective:To evaluate the efficacy of preoperative neoadjuvant chemoradiotherapy for low and locally advanced rectal cancer.Methods:Clinical data of 46 patients with low rectal tumors located within 6 cm from the edge of anal admitted to our hospital between February 2014 and December 2018 were retrospectively analyzed. SIB-IMRT technique was adopted for preoperative radiotherapy. Rectal tumors and positive lymph nodes were irradiated with a dose of 58.75 Gy in 25 fractions (2.35 Gy/fraction), and pelvic lymphatic drainage area was given with 50 Gy in 25 fractions (2.0 Gy/fraction). Oral administration of capecitabine was delivered for concurrent chemotherapy. Radical surgery for rectal cancer was performed at 6 to 12 weeks after the end of chemoradiotherapy. The overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), local recurrence-free survival (LRFS) and metastasis-free survival (MFS) were calculated by using Kaplan- Meier method. Univariate analysis was conducted by log-rank test, and multivariate analysis was performed by Cox’s regression model. Results:After a median follow-up of 47 months, local recurrence occurred in 3 patients and distant metastasis in 6 patients. The ypCR rate was 26%(12/46), the sphincter-preservation rate was 74%(34/46), the R 0 resection rate was 100%(44/44), the overall tumor response TN down staging rate was 87%(40/46), and the postoperative complication rate was 13%(6/46). The 3-year OS, DFS, and PFS were 93%, 91% and 87%, respectively. In univariate analysis, ypN staging was an important factor affecting OS, DFS, PFS, LRFS and MFS (all P<0.05). In multivariate analysis, ypN staging was significantly correlated with DFS, PFS, LRFS and MFS (all P<0.05). Conclusions:Preoperative SIB-IMRT 58.75 Gy in 25 fractions combined with capecitabine chemotherapy is a safe and efficacious treatment for patients with low and locally advanced rectal cancer, which improves the ypCR rate and quality of life, and yields tolerable adverse reactions. Nevertheless, the long-term survival benefits remain to be validated.

We use a dense and fully connected convolutional network with good feature learning in small samples, to automatically pre-deline CTV of cervical cancer patients based on CT images and evaluate the effect. The CT data of stage IB and IIA postoperative cervical cancer with similar delineation scope were selected to be used to evaluate the pre-sketching accuracy from three aspects:sketching similarity, sketching offset and sketching volume difference. It has been proved that the 8 most representative parameters are superior to those with single network and reported internationally before. Dense V-Net can accurately predict CTV pre-delineation of cervical cancer patients, which can be used clinically after simple modification by doctors.
Automation ; Female ; Humans ; Machine Learning ; Patients ; Tomography, X-Ray Computed ; Uterine Cervical Neoplasms/diagnostic imaging*