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Objective:To compare the effects of two methods of marking surface landmarks on the patient’s positional stability when using a multifunctional body board in combination with thermoplastics to fix the abdominal and pelvic areas for radiotherapy patients.Methods:50 subjects who underwent positional fixation using a multifunctional body board in combination with thermoplastics from August 2022 to January 2023. The subjects were divided into two groups, A and B, with 25 cases each, according to the different methods of body surface marking. In group A, landmarks were marked on the body surface on the top edge of the thermoplastics. In group B, three sets of surface landmarks were marked on the patient’s body according to the laser line on the projection of the patient’s body surface when the thermoplastics were completed. Manual registration is performed using L3 to L5 as the main registration targets. The pre-treatment CBCT image is used to analyze the first-time positioning pass rate, setup errors in the x-, y-, and z-axis directions, and the distribution of positive and negative setup errors in both groups of patients. Results:The pass rates of the first-time positioning of patients in Groups A and B were 76.9% and 86.1%, respectively, which met the clinical requirements. Group B had a better first-time positioning pass rate than group A, and the difference between the two groups was statistically significant ( P < 0.05). The pendulum errors of group B were smaller than those of group A in both the x-axis and y-axis (all P < 0.05), and the difference between the two groups in terms of the pendulum errors in the z-axis direction was not statistically significant (all P > 0.05). The difference in the frequency distribution of the pendulum error in the positive and negative directions of the x- and z-axis between the two groups was not statistically significant (all P > 0.05). The difference in the frequency of distribution of the pendulum error in the positive and negative directions of the y-axis between the two groups was statistically significant ( P < 0.05). Conclusions:The proposed two methods of surface landmark marking are generally in line with the positioning requirements for conventional fractionation radiotherapy for abdominal and pelvic patients. Using a laser line on the projection of the patient’s body surface for three sets of surface landmark markings produces smaller setup errors and is better than using the top edge of the thermoplastics for surface landmark markings, improving the positional stability of abdominal and pelvic patients.
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Objective:To investigate the application value of machine learning algorithms for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 277 patients with HCC who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University between May 2015 and December 2018 were collected. There were 235 males and 42 females, aged (56±10)years, with a range from 33 to 80 years. Patients underwent preoperative magnetic resonance imaging examination. According to the random numbers showed in the computer, all the 277 HCC patients were divided into training dataset consisting of 193 and validation dataset consisting of 84, with a ratio of 7∶3. Machine learning algorithms, including logistic regression nomogram, support vector machine (SVM), random forest (RF), artificial neutral network (ANN) and light gradient boosting machine (LightGBM), were used to develop models for preoperative prediction of MVI. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) analysis of risk factors for tumor MVI of the training dataset; (3) construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the paired t test. Count data were described as absolute numbers, and comparison between groups was analyzed using the chi-square test. Univariate and multivariate analyses were performed using the Logistic regression model. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: there were 157 males and 36 females in the training dataset, 78 males and 6 females in the validation dataset, showing a significant difference in the sex between the training dataset and validation dataset ( χ2=6.028, P<0.05). (2) Analysis of risk factors for tumor MVI of the training dataset: of the 193 patients, 108 had positive MVI, and 85 had negative MVI. Results of univariate analysis showed that age, the number of tumors, tumor diameter, satellite lesions, tumor margin, alpha fetaprotein (AFP), alkaline phosphatase (ALP), fibrinogen were related factors for tumor MVI [ odds ratio ( OR)=0.971, 2.449, 1.368, 4.050, 2.956, 4.083, 2.532, 1.996, 95% confidence interval ( CI): 0.943-1.000, 1.169-5.130, 1.180-1.585, 1.316-12.465, 1.310-6.670, 2.214-7.532, 1.016-6.311, 1.323-3.012, P<0.05]. Results of multivariate analysis showed that AFP>20 μg/L, multiple tumors, larger tumor diameter, unsmooth tumor margin were independent risk factors for tumor MVI ( OR=3.680, 3.100, 1.438, 3.628, 95% CI: 1.842-7.351, 1.334-7.203, 1.201-1.721, 1.438-9.150, P<0.05). Larger age was associated with lower risk of preoperative tumor MVI ( OR=0.958, 95% CI: 0.923-0.994, P<0.05). (3) Construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction: ①machine learning algorithm prediction models involving logistic regression nomogram, SVM, RF, ANN and LightGBM were constructed based on results of multivariate analysis including age, AFP, the number of tumors, tumor diameter, tumor margin, and consistency analysis of the logistic regression nomogram prediction model showed a good stability. For the training dataset and validation dataset, the area under curve (AUC) of logistic regression nomogram model, SVM model, RF model, ANN model, LightGBM model was 0.812, 0.794, 0.807, 0.814, 0.810 and 0.784, 0.793, 0.783, 0.803, 0.815, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.731-0.849, 0.744-0.860, 0.752-0.867, 0.747-0.862, Z=0.995, 0.245, 0.130, 0.102, P>0.05) and (95% CI: 0.690-0.873, 0.679-0.865, 0.702-0.882, 0.715-0.891, Z=0.325, 0.026, 0.744, 0.803, P>0.05)]. ② Clinicopathological factors were selected using RF, LightGBM machine learning algorithm to construct corresponding prediction models. According to importance scale of factors to prediction models, factors with importance scale>0.01 were selected to construct RF model, including age, tumor diameter, AFP, white blood cell, platelet, total bilirubin, aspartate transaminase, γ-glutamyl transpeptidase, ALP, and fibrinogen. Factors with importance scale>5.0 were selected to construct LightGBM model, including age, tumor diameter, AFP, white blood cell, ALP, and fibrinogen. Due to lack of factor selection ability, factors based on results of univariate analysis were secected to construct SVM model and ANN model, including age, the number of tumors, tumor diameter, satellite lesions, tumor margin, AFP, ALP, and fibrinogen. For the training dataset and validation dataset, the AUC of SVM model, RF model, ANN model, LightGBM model was 0.803, 0.838, 0.793, 0.847 and 0.810, 0.802, 0.802, 0.836, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.740-0.857, 0.779-0.887, 0.729-0.848, 0.789-0.895, Z=0.421, 0.119, 0.689, 1.517, P>0.05) and (95% CI: 0.710-0.888, 0.700-0.881, 0.701-0.881, 0.740-0.908, Z=0.856, 0.458, 0.532, 1.306, P>0.05)]. Conclusion:Machine learning algorithms can predict MVI of HCC preoperatively, but its application value needs to be further verified by large sample data from multi centers.
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Objective To investigate MRI characteristics of subacute combined degeneration(SCD)with secondary spinal canal stenosis.Methods The clinical and MRI imaging data of 56 patients with SCD were collected to analyze the performance characteristics between spinal cord lesions and spinal canal stenosis,which depended on the synergism of lumbar disc bluge or herniation,degenerative thickening of the ligament flavum and posterior longitudinal ligament.Results Among 56 SCD cases underwent MRI scan,45 cases were combined with spinal cord lesions which showed typical signs of SCD.37 patients were secondary spinal canal stenosis with typical signs,but 2 showed no typical signs.8 patients were no secondary spinal canal stenosis and showed typical.9 cases showed neither spinal cord lesions nor secondary spinal canal stenosis.There was significant difference (P <0.05)between relative secondary spinal canal stenosis and spinal anomaly signal.The course of 1 5 cases were shortened after treated by physical in 37 cases of SCD with secondary spinal canal. Conclusion The secondary spinal canal stenosis can cause microcirculation dysfunction of the spinal cord,which is a key factor contributing to the imaging manifestation.
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Objective To evaluate the immunogenic stability and hereditary stability of Neisseria meningitides serogroup W135/Y[CMCC(B)29037/CMCC(B)29028]within all the passages,which isolated from china.Methods The toxicity of the 3rd,5th,10th,15th,20th,25th and 30th passage of the Neisseria meningitidis was assayed in mice.Serological detection and biochemical detection were measured,and immunized mice subcutaneously.The antigeeicity of each passage of Neisseria meningitides serogroup W135/Y were measured by serum bactericidal test and the indirect ELISA.With the 30 passage of Neisseria meningitides serogroup W135/Y,the effect to the encephalic tissue was measured in mice.Fermented the Neisseria meningitides serogroup W135/Y with 30 passage and purified the capsular polysaccharide,then analyzed the quality respectively.Results The LD50 of the strains CMCC(B)29037/29028 of each passage was low(LD50 ≥ 109),and all the 30logical detection and all the 30 passage of the two strains were half in the tube agglutination.Glucose and maltose fermentation test were positive.Fructose,sucrose and lactose fermentation test were negative.The GMT of immunogenicity were 1114 and 2229 respectively and all the 30 passage were more than 640 and 1040 respectively.After Immunization with individual 30 passage of the Neisseria meningitides,the titer in serum bactericidal assay(SBA)and indirect ELISA were no difference.The capsular polysaccharide purified from Neisseria meningitides serogroup W135/Y met the quality standard.Conclusion Neisseria meningitides serogroup W135/Y,CMCC(B)29037/29028,used in the manufacture of the meningococcal conjugate vaccine,are stable in the toxicity,antigenicity,immunogenicity.Serological detection and biochemical detection are qulified,and the capsular polysaccharide has met the quality standard.