Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Objective:To evaluate the detection capability of the contrast-enhanced three-dimensional turbo spin-echo (CE-SPACE) sequence for brain metastases (BM), aiming to provide evidence for precise target delineation in stereotactic radiotherapy (SRT).Methods:A total of 123 BM patients who received radiotherapy at the Affiliated Cancer Hospital of Zhengzhou University from May to November 2024 were enrolled. All patients underwent contrast-enhanced (CE) MRI and CT scans in the same treatment position, with images rigidly registered in the Eclipse planning system. Two experienced radiation oncologists independently delineated BM lesions on CE-MPRAGE and CE-SPACE sequences in a blinded manner. Patients were divided into the delayed group (10 min, n=61) and a priority group (5 min, n=62) based on the time interval between gadolinium injection and CE-SPACE acquisition. The non-parametric Wilcoxon rank-sum test was used to compare the lesion counts and volume differences between the two imaging sequences. Point-biserial correlation analysis was performed to assess the correlation between the additional lesions identified by CE-SPACE and lesion volume. Results:The overall analysis demonstrated that CE-SPACE detected 421 BM lesions, achieving an 8.2% higher detection rate than CE-MPRAGE ( Z=3.78, P<0.001). In terms of lesion volume, the median BM lesions volume identified by CE-SPACE [0.30(0.07,1.53)cm 3] was 8.7% larger than that by CE-MPRAGE [0.23 (0.04, 1.34) cm 3] ( Z=12.88, P<0.001). CE-SPACE demonstrated superior sensitivity for lesions ≤ 0.06 cm3, with negative correlation between the number of additional lesions detected and lesion volume ( r=-0.104, P=0.034). Subgroup analysis revealed that in the delayed group, CE-SPACE detected significantly more lesions [median 2 (1, 3.5) vs. 2 (1, 3), P=0.002] and larger volumes [0.39 (0.08, 2.24) cm3 vs. 0.29 (0.05, 1.99) cm3, P<0.001] than CE-MPRAGE. In the priority group, CE-SPACE detected significantly larger lesion volumes [0.55 (0.09, 2.06) cm3 vs. 0.45 (0.08, 1.88) cm3, P<0.001], but no significant difference was observed in lesion counts between two sequences ( P=0.059). Conclusions:Three-dimensional CE-SPACE sequence offers superior detection sensitivity for small BM (≤ 0.06 cm3), providing crucial guidance for accurate target delineation in SRT.

Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Objective:To evaluate the detection capability of the contrast-enhanced three-dimensional turbo spin-echo (CE-SPACE) sequence for brain metastases (BM), aiming to provide evidence for precise target delineation in stereotactic radiotherapy (SRT).Methods:A total of 123 BM patients who received radiotherapy at the Affiliated Cancer Hospital of Zhengzhou University from May to November 2024 were enrolled. All patients underwent contrast-enhanced (CE) MRI and CT scans in the same treatment position, with images rigidly registered in the Eclipse planning system. Two experienced radiation oncologists independently delineated BM lesions on CE-MPRAGE and CE-SPACE sequences in a blinded manner. Patients were divided into the delayed group (10 min, n=61) and a priority group (5 min, n=62) based on the time interval between gadolinium injection and CE-SPACE acquisition. The non-parametric Wilcoxon rank-sum test was used to compare the lesion counts and volume differences between the two imaging sequences. Point-biserial correlation analysis was performed to assess the correlation between the additional lesions identified by CE-SPACE and lesion volume. Results:The overall analysis demonstrated that CE-SPACE detected 421 BM lesions, achieving an 8.2% higher detection rate than CE-MPRAGE ( Z=3.78, P<0.001). In terms of lesion volume, the median BM lesions volume identified by CE-SPACE [0.30(0.07,1.53)cm 3] was 8.7% larger than that by CE-MPRAGE [0.23 (0.04, 1.34) cm 3] ( Z=12.88, P<0.001). CE-SPACE demonstrated superior sensitivity for lesions ≤ 0.06 cm3, with negative correlation between the number of additional lesions detected and lesion volume ( r=-0.104, P=0.034). Subgroup analysis revealed that in the delayed group, CE-SPACE detected significantly more lesions [median 2 (1, 3.5) vs. 2 (1, 3), P=0.002] and larger volumes [0.39 (0.08, 2.24) cm3 vs. 0.29 (0.05, 1.99) cm3, P<0.001] than CE-MPRAGE. In the priority group, CE-SPACE detected significantly larger lesion volumes [0.55 (0.09, 2.06) cm3 vs. 0.45 (0.08, 1.88) cm3, P<0.001], but no significant difference was observed in lesion counts between two sequences ( P=0.059). Conclusions:Three-dimensional CE-SPACE sequence offers superior detection sensitivity for small BM (≤ 0.06 cm3), providing crucial guidance for accurate target delineation in SRT.

Objective:To develop a verification platform based on Monte Carlo (MC) for independent dose verification of volumetric modulated arc therapy (VMAT) plans.Methods:The head model including collimator of Varian TrueBeam linear accelerator was constructed by using EGSnrc/BEAMnrc, and the independent dose verification platform for the patients’ VMAT plans was built based on the head model and an in-house code. The percent depth dose (PDD) curves and off-axis ratios for different field sizes, the dose distribution of two irregular fields and three VMAT plans of the head and neck, chest, and pelvis were simulated using the platform. The simulated results of the PDD curves and the off-axis ratios of different field sizes were compared with the blue water measurement results. The difference between the irregular fields and the actual ArcCHECK measurements was also investigated. Besides, the differences among the MC simulated dose, TPS calculated dose and the ArcCHECK measured dose were analyzed by several methods, such as γ analysis and dose-volume histogram to verify whether the platform could be independently employed for dose verification.Results:The MC simulated results of PDD curves and off-axis ratios from 4 cm×4 cm to 40 cm×40 cm were in good agreement with the measured results. And the γ passing rates between the MC simulation and the ArcCHECK measurement for the irregular fields were above 98.1% and 99.1% for 3%/2 mm and 3%/3 mm, respectively. For VMAT plans of three patients, the γ results between the MC simulated dose and ArcCHECK measured dose were better than 93.8% and 95.9% under the criteria of 3%/2 mm and 3%/3 mm respectively. At the same time, the γ passing rates of nasopharyngeal, lung, and rectal cancers were 95.2%, 98.6% and 98.9% based on 3D γ analysis using TPS calculated dose and MC simulated dose under the criteria of 3%/3 mm; the passing rates of these three were 90.3%, 95.1% and 96.7% for 3%/2 mm, respectively.Conclusions:The simulation results of the MC-based verification platform developed in this study show a good agreement with the actual measurement results, and the simulation results are closer to the real dose distribution using the patients’ data. The preliminary results demonstrate that the platform can be used for accurate independent dose verification of VMAT plans.

Narcolepsy is the most common cause of excessive daytime sleepiness (EDS) following obstructive sleep apnea. Its treatment aims to reduce EDS and cataplexy, improve nighttime sleep disturbance, sleep paralysis and sleep-related hallucinations. Pitolisant (a histamine H3 receptor antagonist) and solriamfetol (a norepinephrine reuptake inhibitor) have recently been approved effective for narcolepsy in the United States and the European Union. Pitolisant has proved to be effective for both EDS and cataplexy. Besides being effective on EDS, solriamfetol seems to have advantages in abuse potential and withdrawal syndrome. As potential treatments for EDS and cataplexy associated with narcolepsy, several new drugs are being developed and tested. These new drugs include new hydroxybutyrate preparations (controlled release sodium hydroxybutyrate FT218, low sodium hydroxybutyrate JZP-258), selective norepinephrine reuptake inhibitor (AXS-12), and modafinil combined with astroglial junction protein inhibitor (THN102). This paper reviews the recently approved drugs and potential treatments for narcolepsy.

Objective Feedback of 35 graduates from a Sino-Australia cooperation program was investigated at 1 -year fulfillment of clinical practice,and reflect some of the issues in the process of cultivation and utilization of nursing talents from the program. Methods 32 nursing talents from a Sino-Australia cooperation program at 1 -year fulfillment of clinical practice was selected as research object.Self made questionnaires baaed on the goal of the program were used,which included both open and close questions.The investigation results were analyzed. Results The nursing talents considered that the main advantages of the program were as followed:cultivation of their ability in active communication,improving English level,broaden international vision,and internationalization of courses. Conclusions Stratified and individualized teaching strategies need to be implemented.English study and nursing specialty study are to be appropriately combined.Routes of employment should be broadened.In addition,imported courses are to be adjusted according to domestic situation.While attaching importance in teaching,faculty should also emphasize advising.Finally,it is not to be ignored for the hospitals that they are responsible for greater utilization and cultivation of the nurses with both nursing and English ability.

Objectives By survey the head nurses or nursing instructors,to know the one year professional performance of nurses graduated from Sino-Australia cooperation program of Jinhua Polytechnic,which can help us to make the educational plan and further cultivation.MethodSelf-composed questionnaire is provided to 32 head nurses or nursing instructors from secondary and tertiary hospitals of Zhejiang province.SPSS 18 is applied for data analysis.ResultThe nurses' performance is generally consistent to the objectives ofthe Sino-Australia nursing program and the rating scores are normally distributed. There is no significant difference in the average performance of nurses comparing to a test value of 71,P≤0.08.The scores of professional quality,nursing competency,English ability,humanitarian caring,active learning,team work and adaptability to clinical responsibilities were especially well rated.ConclusionResults indicated that the school should appropriately adjust the direction for students＇ employment,consider to localize the nursing courses introduced from foreign countries.It is necessary for the hospitals to ponder on how to further cultivate and utilize the English competency of the nurses,and use the individualized strategies to retain the nurses who are proficient in English.