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 investigate the status of personnel, facilities, and technology of radiation therapy in Henan province in 2023.Methods:A unified online survey questionnaire was designed and distributed from March to April 2023 by the Henan Cancer Diagnosis and Treatment Quality Control Center to various medical institutions throughout the province to investigate the personnel, radiotherapy equipment, quality control equipment, imaging equipment, and radiotherapy technology development of each radiotherapy unit. Descriptive statistical methods were mainly used.Results:As of April 30, 2023, there were a total of 168 units engaged in radiation therapy in Henan province. The number of physicians involved in radiation therapy was 956, along with 365 medical physicists and 680 technicians. The equipment inventory included 212 medical linear accelerators, 1 cobalt-60 therapy machine, 32 afterloading therapy apparatuses, 4 Cyber Knife, 173 CT simulators, 2 MRI simulators and 94 conventional simulators. Linear accelerators were the primary radiotherapy equipment, 2.15 units per 1 million population on average. Additionally, there were 11 units offering 2D radiotherapy, 24 units offering 3D conformal radiotherapy, 130 units offering intensity-modulated radiotherapy, 41 units offering rotational intensity-modulated radiotherapy, and 33 units offering stereotactic radiosurgery. Regarding physical quality control equipment, 16 units were equipped with three-dimensional water tanks, 162 units were equipped with radiation dose meters, 114 units were equipped with morning check meters, 60 units were equipped with film dose meters, and 108 units were equipped with intensity adjustment plan verification systems.Conclusions:In 2023, there is a shortage of radiation therapy professionals in Henan province. Disparities are observed in the distribution of radiation therapy equipment among regions. Intensity-modulated radiotherapy has become the mainstream technology for radiation therapy in Henan province. The configuration of radiation therapy quality control equipment and standardized quality control work should be further improved.

Objective:To investigate the accuracy of magnetic resonance imaging (MRI) cine sequences in determining the range of tumor motion in radiotherapy, providing a basis for the precise delineation of the target volume in motion for radiation therapy.Methods:A modified chest motion phantom was placed in a MRI scanner, and a water-filled sphere was used to simulate a tumor. True fast imaging with steady precession (TrueFISP) MRI cine sequences from Siemens were used to capture the two-dimensional motion images of the simulated tumor. The phantom experiments were divided into three modes: head-foot motion mode, rotation motion mode, and actual respiratory waveform mode. In the head-foot motion mode, respiratory motion period (3, 4, 5, 6, 7 and 8 s), amplitude (5, 10 and 15 mm), and respiratory waveform of the simulated tumor (sin and cos4) were set, resulting in a total of 36 motion combinations. In the rotation motion mode, a cos4 waveform was used for respiration, with respiratory periods of 3, 4, 5, 6, 7 and 8 s, head-foot motion set amplitudes of 5, 10 and 15 mm, and anterior-posterior (AP) and left-right (LR) motion set amplitudes in three combinations ([2.5, 2.5] mm, [2.5, 5.0] mm, [5.0, 5.0] mm), resulting in a total of 54 motion combinations. In the actual respiratory waveform mode, respiratory waveforms of 5 randomly selected patients from Affiliated Cancer Hospital of Zhengzhou University were obtained. Under each motion combination, TrueFISP cine images (30 frames, with an acquisition time of 11 s per frame) were obtained. The code was used to automatically identify the two-dimensional coordinates of the center of the simulated tumor in each image, and sin and cos4 functions were separately employed to fit the tumor position in the motion direction, thereby obtaining the fitted motion period and amplitude. The difference between the maximum and minimum values of the tumor's center coordinates in the head-to-foot direction is taken as the range of movement, referred to as the calculated amplitude. For the actual respiratory waveform, the distance between the measured maximum and minimum positions is used to calculate the amplitude.Results:In the head-foot motion mode, the fitted amplitudes of both sin and cos4 waveforms deviated from the set amplitudes by 0-0.51 mm, with relative deviations of 0%-4.2%. The deviation range between the calculated amplitudes and the set amplitudes of the two waveforms were 0.08-0.94 mm, with relative deviations of 1.1%-6.3%. In the rotation motion mode, the fitted amplitudes deviated from the set amplitudes by 0-0.61 mm, with relative deviations of 0%-6.2%. And the deviation range between the calculated amplitudes and the set amplitudes were 0.16-0.94 mm, with relative deviations of 0%-6.3%. In the actual respiratory waveform motion mode, the deviation range between the calculated amplitudes and the set amplitudes were 0.10-0.48 mm, with relative deviations of 2.2%-8.6%.Conclusion:TrueFISP cine sequences show minimal deviations in determining the range of tumor head-foot motion and effectively captures the tumor's movement state, thereby providing important support for the precise definition of the tumor movement target area during radiotherapy .

Objective:To propose an automatic planning approach for Eclipse15.6 planning system based on Eclipse scripting application programming interface (ESAPI) and evaluate its clinical application.Methods:20 patients with nasopharyngeal carcinoma and 20 cases of rectal cancer were selected in the clinical planning. The developed automatic planning script SmartPlan and RapidPlan were used for automatic planning and dosimetric parameters were compared with manual planning. The differences were compared between two groups by using Wilcoxon signed rank test. Results:The dosimetric results of automatic and manual plans could meet clinical requirements. There was no significant difference in target coverage in nasopharyngeal carcinoma planning between two groups ( P>0.05), and automatic plans were superior to manual plans in organs at risk sparing ( P<0.05). Except for the homogeneity index of PTV and the maximum dose of bowel in rectal cancer plans, the other dosimetric parameters of the automatic plans were better than those of the manual plans (all P<0.05). Conclusions:Compared with the manual plans, the automatic plans have the same or similar target coverage, similar or better protection of organs at risk, and more convenient implementation. The developed SmartPlan based on ESAPI has clinical feasibility and effectiveness.

Objective:To explore the value of adrenocorticotrophic hormone (ACTH) stimulation in adrenal vein sampling (AVS) with its effect on the sampling success rate and lateralization determination.Methods:The clinical data of 54 patients with primary aldosteronism (PA) who underwent AVS in Nanjing Drum Tower Hospital from July 2018 to June 2020 were collected retrospectively. Blood samples from bilateral adrenal veins were collected simultaneously at baseline and after ACTH stimulation. The selectivity index (SI), lateralization index (LI), and relative aldosterone secretion index (RASI) were examined.Results:The concentration of serum cortisol level in left and right adrenal vein and peripheral vein increased significantly after ACTH stimulation ( P<0.001). SI of left adrenal vein increased from 18.00 (2.29, 20.29) to 34.76 (12.10, 46.86) , and the SI of right adrenal vein increased from 26.61(5.24, 31.85) to 28.40 (27.65, 56.05, P<0.001). The bilateral vein sampling success rate increased from 80%(43/54) to 93%(50/54). LI decreased from 2.85(1.78, 6.20) at baseline to 2.45(1.40, 6.10) after ACTH stimulation without significant difference( P>0.05). Eleven patients who identified unilateral secretion at baseline demonstrated bilateral after ACTH stimulation, and the RASI of these patients decreased from 0.50 (0.38, 1.25 ) to 0.37 (0.22, 0.84, P=0.019). Conclusion:ACTH stimulation significantly increased SI and the AVS success rate in patients with PA: ACTH stimulation decreased the relative aldosterone secretion in the dominant side of some patients with aldosterone producing adenoma, thus reduced the proportion of identified unilateral PA.

Objective:To build a systemic and automatic importing scheme for importing CT images and structures into the treatment planning systems (TPSs) of Eclipse and Monaco.Methods:Based on two TPSs of Eclipse and Monaco, the files of CT images and structures were automatically transported from OAR auto-delineation system to the importing directory of these two TPSs using batch script in Windows system. Following the standard importing procedures of these two TPSs, the automatically importing script of CT images and structures were developed using the application of UiBot. Finally, the CT images and structures were imported into these two TPSs opportunely.Results:By comparing the importing time using script and manual methods, the script not only achieved auto-importing CT images and structures into TPSs, but also yielded almost the same efficiency to manual method. The number of imaging layers in most patients was between 130 and 180, and the average manual and automatic importing time within this interval was 76 s and 75 s.Conclusions:Automatic scripts can be developed by using the automation function of UiBot combined with the actual problems of radiotherapy and repeated workflow. The efficiency of radiotherapy work can be significantly improved. Manual and time costs can be saved. It provides a novel alternative for the automation of radiotherapy procedures.

Objective:To propose an automatic planning method of intensity-modulated radiotherapy (IMRT) for esophageal cancer based on dose volume histogram prediction and beam angle optimization in Raystation treatment planning system.Methods:50 IMRT plans of esophageal cancer were selected as the training set to establish a dose prediction model for organs at risk. Another 20 testing plans were optimized in Raystation using RuiPlan and manual method, and the beam angle optimization and dose volume histogram prediction functions of RuiPlan were used for automatic planning. Dosimetric differences and planning efficiency between two methods were statistically compared with paired t-test. Results:There were no significant dosimetric differences in the conformity index (CI), homogeneity index (HI) of PTV, V 5Gy of both lungs and D max of the spinal cord between automatic and manual plans (all P>0.05). Compared with those in the manual plans, the V 20Gy and D mean of the left and right lungs generated from automatic plans were reduced by 1.1%, 0.37 Gy and 1.2%, 0.38 Gy (all P<0.05), and the V 30Gy, V 40Gy and D mean of the heart in automatic plans were significantly decreased by 5.1%, 3.0% and 1.41 Gy, respectively (all P<0.05). The labor time, computer working time, and monitor unit (MU) number of automatic plans were significantly decreased by 65.8%, 14.1%, and 17.2%, respectively (all P<0.05). Conclusion:RuiPlan automatic planning scripts can improve the efficiency of esophageal cancer planning by dose prediction and beam angle optimization, providing an alternative for esophageal cancer radiotherapy planning.