Objective:To investigate the feasibility and optimal expansion width of replacing the left anterior descending coronary artery (LADCA) with the region of heart sparing (RHS) to reduce cardiac radiation dose during breast cancer radiotherapy.Methods:Retrospective analysis was conducted on data from 88 patients with left-sided breast cancer who underwent radiotherapy at 2 centers: Nanfang Hospital of Southern Medical University (50 cases for the training set, 15 cases for the internal test set) and Ganzhou Hospital of Nanfang Hospital (23 cases for the external test set) from March 2022 to January 2024. All patients had left-sided invasive ductal carcinoma with axillary lymph node metastasis, and had undergone modified radical mastectomy and chemotherapy. Based on simulation CT images, 2 radiation oncologists delineated the LADCA and 8 RHSs. The RHSs were delineated by expanding the LADCA contour by 0.5 cm increments, totaling 8 expansions. The RHS widths were defined as 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 cm. The nnU-Net model was trained for 3D automatic segmentation of the LADCA and RHSs. Model performance was evaluated using the Dice similarity coefficient (DSC), relative volume error (RVE), sensitivity, specificity, and 95% Hausdorff distance (HD95). Additionally, the minimum, maximum, and average relative dose variations (RDV) as well as V5% and V20% indicators were calculated for the LADCA and each RHS. Correlation analysis was performed using the least squares regression, with the slope and coefficient of determination ( R2) employed to evaluate the accuracy of the model fitting, the relationship between the LADCA and RHS, and the degree of their correlation, thereby assessing the substitutive effect of the RHS for the LADCA. Results:The DSC for the LADCA was 0.415, while the DSCs for RHS widths of 0.5 cm and 4.0 cm were 0.718 and 0.835, respectively. Overall, the automatic segmentation performance improved with increasing RHS width. The DSC, RVE, sensitivity, specificity, and HD95 for the external test set were largely consistent with those of the internal test set, demonstrating the model's good robustness across different datasets. All RDVmin values were negative, while RDVmax and RDVmean showed a positive correlation with RHS width. RDVmean increased from 39.01% to 75.89% as the RHS width increased. In the correlation analysis, the slopes for RHS widths of 1.5 cm and 2.0 cm were 0.95 and 1.05, respectively, with R2 values and coefficients of variation of 0.79 and 0.73, and 21.11% and 24.03%, respectively. Conclusions:The automatic segmentation model trained on nnU-Net can accurately segment RHSs. Based on geometric and dosimetric indicators, a 1.5 cm-wide RHS is the most suitable substitute for the LADCA, effectively limiting the radiation dose to the LADCA without compromising target dose coverage.

Objective:To propose a semi-supervised learning method for dynamic generation of organ geometric contours, leveraging bladder volume variations and its relative position to the uterus to accurately generate uterine contours in cervical cancer radiotherapy.Methods:A total of 120 sets of pelvic planning CT images (including both full and empty bladder scans) from 60 patients with cervical cancer treated at the Department of Radiation Oncology, Nanfang Hospital of Southern Medical University between January and December 2023 were retrospectively collected. A conditional generative adversarial network (CGAN) based on a squeeze-and-excitation channel attention mechanism was proposed to accurately generate uterine geometric contours under varying bladder filling states. By emphasizing the critical spatial relationships between the bladder and uterus, the model learned the relative anatomical positions of pelvic organs and their motion correlations. The generative performance was quantitatively evaluated using the average Dice similarity coefficient (DSC), intersection over union (IoU), and the 95 th percentile Hausdorff distance (HD95), and was compared with GAN model, CGAN model, and Pix2Pix model. Pairwise comparisons were perfomed by paired-sample t-test. Results:The proposed SE-CGAN model achieved the best performance on the test set, with DSC of 0.83±0.09, IoU of 0.71±0.05, HD95 of (6.74±1.23) mm, improving DSC by 7.5%, 4.9%, and 3.6% compared to the GAN, CGAN, and Pix2Pix models, respectively (all P<0.001), and reducing the mean HD95 by 32.9%-45.3%. Statistical analysis revealed significant differences between SE-CGAN model and the other 3 baseline models, whereas no significant difference was observed between CGAN model and Pix2Pix model. The visualization results further demonstrated that the GAN model produced uterine contours deviated greatly from the real shape, and the edge was fuzzy; CGAN and Pix2Pix model achieved better overlap but lacked of precision in boundary reconstruction. In contrast, the contours generated by SE-CGAN model closely matched the ground truth with clearly defined edges, indicating superior reconstruction accuracy. Conclusions:In this study, we propose a generative adversarial network method that establishes a dynamic modulation mechanism by which the bladder state influences the uterine geometric contour, enabling accurate generation of the uterine contours from the bladder contours of any given localization CT scan. This approach effectively addresses the uncertainty in radiotherapy target delineation caused by pelvic organ motion.

Objective:To investigate the feasibility and optimal expansion width of replacing the left anterior descending coronary artery (LADCA) with the region of heart sparing (RHS) to reduce cardiac radiation dose during breast cancer radiotherapy.Methods:Retrospective analysis was conducted on data from 88 patients with left-sided breast cancer who underwent radiotherapy at 2 centers: Nanfang Hospital of Southern Medical University (50 cases for the training set, 15 cases for the internal test set) and Ganzhou Hospital of Nanfang Hospital (23 cases for the external test set) from March 2022 to January 2024. All patients had left-sided invasive ductal carcinoma with axillary lymph node metastasis, and had undergone modified radical mastectomy and chemotherapy. Based on simulation CT images, 2 radiation oncologists delineated the LADCA and 8 RHSs. The RHSs were delineated by expanding the LADCA contour by 0.5 cm increments, totaling 8 expansions. The RHS widths were defined as 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 cm. The nnU-Net model was trained for 3D automatic segmentation of the LADCA and RHSs. Model performance was evaluated using the Dice similarity coefficient (DSC), relative volume error (RVE), sensitivity, specificity, and 95% Hausdorff distance (HD95). Additionally, the minimum, maximum, and average relative dose variations (RDV) as well as V5% and V20% indicators were calculated for the LADCA and each RHS. Correlation analysis was performed using the least squares regression, with the slope and coefficient of determination ( R2) employed to evaluate the accuracy of the model fitting, the relationship between the LADCA and RHS, and the degree of their correlation, thereby assessing the substitutive effect of the RHS for the LADCA. Results:The DSC for the LADCA was 0.415, while the DSCs for RHS widths of 0.5 cm and 4.0 cm were 0.718 and 0.835, respectively. Overall, the automatic segmentation performance improved with increasing RHS width. The DSC, RVE, sensitivity, specificity, and HD95 for the external test set were largely consistent with those of the internal test set, demonstrating the model's good robustness across different datasets. All RDVmin values were negative, while RDVmax and RDVmean showed a positive correlation with RHS width. RDVmean increased from 39.01% to 75.89% as the RHS width increased. In the correlation analysis, the slopes for RHS widths of 1.5 cm and 2.0 cm were 0.95 and 1.05, respectively, with R2 values and coefficients of variation of 0.79 and 0.73, and 21.11% and 24.03%, respectively. Conclusions:The automatic segmentation model trained on nnU-Net can accurately segment RHSs. Based on geometric and dosimetric indicators, a 1.5 cm-wide RHS is the most suitable substitute for the LADCA, effectively limiting the radiation dose to the LADCA without compromising target dose coverage.

Objective:To propose a semi-supervised learning method for dynamic generation of organ geometric contours, leveraging bladder volume variations and its relative position to the uterus to accurately generate uterine contours in cervical cancer radiotherapy.Methods:A total of 120 sets of pelvic planning CT images (including both full and empty bladder scans) from 60 patients with cervical cancer treated at the Department of Radiation Oncology, Nanfang Hospital of Southern Medical University between January and December 2023 were retrospectively collected. A conditional generative adversarial network (CGAN) based on a squeeze-and-excitation channel attention mechanism was proposed to accurately generate uterine geometric contours under varying bladder filling states. By emphasizing the critical spatial relationships between the bladder and uterus, the model learned the relative anatomical positions of pelvic organs and their motion correlations. The generative performance was quantitatively evaluated using the average Dice similarity coefficient (DSC), intersection over union (IoU), and the 95 th percentile Hausdorff distance (HD95), and was compared with GAN model, CGAN model, and Pix2Pix model. Pairwise comparisons were perfomed by paired-sample t-test. Results:The proposed SE-CGAN model achieved the best performance on the test set, with DSC of 0.83±0.09, IoU of 0.71±0.05, HD95 of (6.74±1.23) mm, improving DSC by 7.5%, 4.9%, and 3.6% compared to the GAN, CGAN, and Pix2Pix models, respectively (all P<0.001), and reducing the mean HD95 by 32.9%-45.3%. Statistical analysis revealed significant differences between SE-CGAN model and the other 3 baseline models, whereas no significant difference was observed between CGAN model and Pix2Pix model. The visualization results further demonstrated that the GAN model produced uterine contours deviated greatly from the real shape, and the edge was fuzzy; CGAN and Pix2Pix model achieved better overlap but lacked of precision in boundary reconstruction. In contrast, the contours generated by SE-CGAN model closely matched the ground truth with clearly defined edges, indicating superior reconstruction accuracy. Conclusions:In this study, we propose a generative adversarial network method that establishes a dynamic modulation mechanism by which the bladder state influences the uterine geometric contour, enabling accurate generation of the uterine contours from the bladder contours of any given localization CT scan. This approach effectively addresses the uncertainty in radiotherapy target delineation caused by pelvic organ motion.

Objective:To verify the clinical value of the good outcome following attempted resuscitation (GO-FAR) score in predicting the neurological status of patients with in-hospital cardiac arrest (IHCA) in the Chinese population.Methods:The clinical data of patients with IHCA who were admitted to the Zigong Fourth People's Hospital from January 1 to December 31, 2020 were retrospectively analyzed. Used Glasgow-Pittsburgh cerebral performance category (CPC) score 1 point as the end point, the subjects were divided into 4 groups according to the score: ≤ 0 group, 1-8 group, 9-20 group and ≥ 21 group. Taken the group which GO-FAR score ≤ 0 as the reference group, the odds ratio ( OR) of the other three groups compared with this group was calculated. The receiver operator characteristic curve (ROC curve) was performed to evaluate the predictive value of the GO-FAR score in favorable neurological outcome. A calibration curve was drawn for the Hosmer-Lemeshow test to analyze the degree of calibration of the GO-FAR score for predicting good neurological outcome. Results:A total of 230 IHCA patients were enrolled in the study, including 130 males, aged 74 (65, 81) years old, and 23 case (10.0%) had good neurological prognosis. There were statistically significant differences in GO-FAR-related variables, including age, a normal neurological function on admitted, acute stroke, metastatic cancer, septicemia, medical noncardiac admission, hepatic insufficiency, hypotension, renal insufficiency or dialysis, respiratory insufficiency, pneumonia, etc (all P < 0.05). Taken the GO-FAR score ≤ 0 group as the reference group, the OR values of good neurological prognosis in the GO-FAR score 1-8 group were 0.54 [95% confidence interval (95% CI) was 0.17-1.53, P = 0.250], 9-20 group were 0.17 (95% CI was 0.02-0.67, P = 0.009) and ≥ 21 group were 0.25 (95% CI was 0.05-0.85, P = 0.025). The area under the ROC curve (AUC) of the GO-FAR score for predicting favorable neurological outcome in IHCA patients was 0.653 (95% CI was 0.529-0.777, P = 0.015) and there was no significant difference in Hosmer-Lemeshow test ( P = 0.311). All these suggested that there was no significant difference between the predicted value and the actual value. Conclusions:GO-FAR score can be applied to predict neurological prognosis of IHCA patients in Chinese population. It can help clinicians to predict the prognosis of cardio-pulmonary resuscitation (CPR) and propose critical recommendations in treatment for these patients or their families.

Objective: To investigate the stability of icariin, 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-D-glucoside and hyperoside in Huangmai mixture.Methods: TLC was used to identify astrogaloside IV and Morindae officinalis Radix, and HPLC was used to determine the contents of icariin, 2,3,5,4'-retrahydroxy-stilbene-2-O-β-D-glucoside and hyperoside.The stability of Huangmai mixture under the conditions of accelerated testing and long-term testing was assessed.Results: Astrogaloside IV, Morindae officinalis Radix and icariin showed good stability during the accelerated testing and long-term testing.The contents of 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-D-glucoside and hyperoside declined quickly during the stability testing.Conclusion: Hyperoside and 2,3,5,4'-retrahydroxy-stilbene-2-O-β-D-glucoside are unstable in Huangmai mixture.A novel solid dosage form of Huangmai should be developed to obtain better stability.

OBJECTIVE:To establish a method for the quality control of Huangmai granules and provide reference for the qual-ity control. METHODS:TLC was conducted to identify the Leguminosae in Huangmai granules and HPLC was used to determine the contents of icariin,hyperin and stilbene glycoside in Huangmai granules. RESULTS:The spots on TLC plates were clear with good reproducibility. The results of HPLC showed that the linear range was 9.969-319.0 μg/ml(r=0.999 9) for icariin , 12.3-196.8 μg/ml(r=0.999 9)for hyperin and 12.64-202.2 μg/ml(r=0.999 8)for stilbene glycoside;RSDs of precision,stability and reproducibility tests were all no more than 2.92%;the average recoveries were respectively 95.44%(RSD=1.46%,n=6), 101.06%(RSD=1.90%,n=6)and 100.51%(RSD=1.73%,n=6). CONCLUSIONS:The method is simple,reproducible,accu-rate and reliable,and can be used for the quality control of Huangmai granules.

Objective: To determine the optimal preparation conditions for Huangmai granules. Methods: Using single factor screening method and orthogonal design method, the extraction process and granulation process were optimized. Results:The optimal preparation process was as follows:the medicinal materials in the prescription were extracted 3 times with 8-fold amount of water with 2h for each time. The water extract was combined followed by vacuum concentration, and then dried to obtain dry extract. The powder of dry extract was evenly mixed with dextrin at appropriate amount, and the wet granules were prepared with the 40% ethanol followed by drying and size stabilization to obtain the products. Conclusion:The selected preparation process is scientific, reasonable and sta-ble, and suitable for the industrial production.

Objective: To establish the preparation method of the placebos for Shenbawei capsules and objectively evaluate the simulation effects. Methods:Using the placebos for Shenbawei capsules as the example, a blind trial was used to assess and score the appearance, color and smell of the placebos. Results: The placebos were basically the same in the appearance, color and taste with the testing drugs. The results of the clinical trial simulation showed that the placebos were difficult to be unblinded. Conclusion:The placebos can be used in the clinical trial for Shenbawei capsules.

Objective: To analyze the uncertainty of quercetin concentration determination in Sicao Tongmai capsules by HPLC. Methods: The source of uncertainty was confirmed by analyzing the HPLC determination process. The uncertainty components were quantified by statistics, and the extended uncertainty and confidence level were finally obtained. Results: The extended uncertainty of the measurement results was 0. 19 μg · g-1 . Quercetin concentration in Sicao Tongmai capsules was (353. 65 ± 0. 19)μg·g-1 . Conclusion: The uncertainty analysis method is suitable for the standard limit formulation for Sicao Tongmai capsules, and it is important to establish uncertainty analysis methods for traditional Chinese medicines.