Objective To investigate the effectiveness of organ dose modulation (ODM) technology with respect to dose reduction and image quality of the sensitive organs during CTA of craniocervical arteries. Methods Ninety one patients suspected of arterial vascular disease and evaluated by CTA of craniocervical arterires were enrolled in this study. Patients were divided into group A (n=46) and group B (n=45) according to the order of examination. All patients hadCTA examination of craniocervical arteries. The ODM technique was used only in plain phase in group A and was used both in the plain phase and enhanced phase in group B. The tube current in the four direction (anterior,posterior,left,right) of thyroid and orbital area and radiation dose were recorded. The image quality of group A and group B was evaluated by objective indicators [CT attenuation values and contrast-to-noise ratio (CNR) of vessels in the thyroid area and orbital region ]and the subjective score. The independent samples t test was used for the statistical analysis of the objective indicators and nonparametric rank sum Mann-Whitney U test were used for subjective score. Results For A group, There were significant differences for the tube current at all directions of the thyroid gland and orbital area between the plain phase and the enhanced phase except the current at the posterior of the thyroid gland area(P value all<0.05),and the current in plain phase lower than that in enhanced phase. The mean current of thyroid gland and the orbital area were (604±43) mA and (656± 22) mA in the plain phase and (341±54) mA and (409±63) mA in enhanced phase. The differences were significant (t values were -10.909 and -38.454,P<0.01).CTDIvol and ED were (15.6±1.4) mGy and (1.44± 0.17) mSv in the plain phase and (17.4 ± 1.4) mGy and (1.60 ± 0.18) mSv in the enhanced phase, the differences were significant (t values were - 42.008 and - 32.130, P<0.01) . There was no significant differences for the objective indicators and the subjective score between group A and group B(P>0.05). Conclusion ODM technique can provide protective effect on the sensitive organs during craniocervical CTA examination without compromising the image quality.

Objective To explore the application value of deep learning image reconstruction(DLIR)combined with Smart metal artifact reduction(Smart MAR)algorithm in maxillofacial CT images.Methods A total of 34 patients with maxillofacial lesions affected by oral metal implants who underwent maxillofacial enhanced CT scans were included.The images of four groups in venous phase were reconstructed with 50％adaptive statistical iterative reconstruction(ASIR-V)(IR group),50％ASIR-V combined with Smart MAR(IR+S group),DLIR(at medium strength)combined with Smart MAR(D-M+S group)and DLIR(at high strength)combined with Smart MAR(D-H+S group)respectively.The artifact index(AI)was worked out by measuring the standard deviation(SD)of CT values in maxillofacial lesions and longhead muscle.The subjective scores of overall image quality,lesion conspicuity and diagnostic confidence were assessed.The image quality of different algorithms was compared.Results Compared with IR+S group,the AI value of IR group was significantly increased(P＜0.05),while the noise had no significant difference(P＞0.05).Compared with IR+S group,the AI value and noise of D-M+S group and D-H+S group both were significantly decreased(P＜0.05),and the AI value of D-M+S group and D-H+S group reduced by 13.70％and 19.06％respectively,the noise reduced by 16.37％and 30.78％respectively.The subjective scores of overall image quality,lesion conspicuity and diagnostic confidence in IR+S group were significantly lower than those in D-M+S group and D-H+S group,but significantly higher than those in IR group(P＜0.05).There were 6 patients'(17.64％)lesions were detected only in the groups with Smart MAR algorithm,while 9 patients(26.47％)had introduced new artifacts in the tongue with Smart MAR algorithm.Conclusion DLIR combined with Smart MAR can improve the CT image quality of maxillofacial region,enhance the conspicuity and diagnosis confidence of maxillofacial lesions in patients with oral metal implants.Smart MAR algorithm may produce new artifacts that need to be analyzed along with the images not added Smart MAR algorithm.

Objective To analyze the genetic characteristics of chromosomes and related fusion genes in acute myeloid leukemia (AML) (non-M3), and to evaluate the prognosis of patients with chemotherapy of DA regimen with different doses of daunorubicin. Methods Fifty-six patients with newly diagnosed non-M3 AML from January 2013 to January 2015 were collected. Adopted short-term culture method was used to treat bone marrow, R-binding chromosome karyotyping was used to detect cytogenetic. Thirty-one types of fusion gene were identified by PCR and 10 % agarose gel electrophoresis. All patients treated by DA regimen were divided into group A, group B and group C according to different dosage of daunorubicin. Then, complete remission (CR) rate and survival time in the 3 groups were observed. The effect of cytogenetic and molecular biology abnormality on the chemotherapy, CR rate and overall survival (OS) of the 3 groups were analyzed by the chi-square test. Results Among the 56 patients, 18 cases (32.1%) had abnormal chromosome karyotype, 6 cases (10.7 %) had abnormal number of chromosome, 16 cases (28.6 %) had abnormal structure of chromosome, and 4 cases (7.1 %) had both abnormal number and structure of chromosome. Meanwhile, the most common abnormal structure was t(8;21), and the most common abnormal quantity were+8, -Y. Detective rate of genetic abnormality was raised to 62.00 % through fusion gene and chromosome karyotype analysis. The total CR rate of DA-induced chemotherapeutic regimen was 73.2 %, and the two-year OS rate was 42.9%. The remission rate of chemotherapy in the middle-risk group was significantly lower than that in the low-risk group (χ 2 = 8.976, P = 0.002), but there was no significant difference between the low-dose chemotherapy group and the standard dose chemotherapy group (P>0.05). The standard dose group showed a significant advantage in the OS rate (χ2= 8.045, P= 0.005). Conclusions Adult acute leukemia has its unique cytogenetic characteristics, which can assist in guiding clinical diagnosis, classification and prognosis. The prognosis of middle-risk patients is significantly lower than the low-risk group. Low-risk patients could benefit from a reduced dose of DA regimen, but the standard dose DA regimen has a significant advantage in long-term survival.

Objective To investigate the effect of standardized lymph node sorting on postoperative results of gas-troesophageal junction malignant tumors.Methods The data of all patients with malignant gastroesophageal junc-tion in gastric cancer database were analyzed retrospectively.Lymph nodes were sorted according to whether sur-geons were present immediately after surgery.Patients were divided into lymph node sorting group(sorting group)and lymph node unsorting group(unsorting group).General data included gender,age,body mass index(BMI),carcinogenic antigen(CEA),postoperative albumin level,preoperative hemoglobin,etc.Perioperative and patho-logical data included operation time,intraoperative blood loss,postoperative hospital stay,tumor differentiation,distance from superior incisal margin,total number of lymph nodes,number of positive lymph nodes,etc.Kaplan-Meier curve and Log-rank test were used for survival analysis,and propensity score matching analysis adjusted for confounding factors between groups.Results A total of 386 patients were included,including 133 in lymph node sorting group and 253 in non-sorting group.The median follow-up time was 40.18 months.The total number of lymph nodes and the number of positive lymph nodes in the sorting group were(26.38±12.18)and(6.63±10.14),respectively,while the total number and the number of positive lymph nodes in the non-sorting group were(12.25±7.06)and(3.07±3.77),respectively.There were statistically significant differences in the total num-ber of lymph nodes and the number of positive lymph nodes between the sorting group and the non-sorting group(P＜0.05).There was no statistically significant difference in survival between the sorting group and the non-sorting group before matching.There were 112 and 203 patients with advanced gastric cancer in the two groups,respec-tively.The overall survival curve of patients in the sorting group was better than that in the non-sorting group,and the difference in median survival time was statistically significant(P＜0.05).The caliper value was set to 0.02,and 94 pairs of patients were preferentially matched.After matching,the total number of lymph nodes and the num-ber of positive lymph nodes in the sorting group were(24.71±12.03)and(5.70±9.95),respectively,while the total number and the number of positive lymph nodes in the non-sorting group were(13.05±7.63)and(3.37±4.32),respectively.The difference between the two groups was statistically significant(P＜0.05).The overall survival curve of patients in the sorting group was better than that in the non-sorting group,and the median survival time was statistically significant(P＜0.05).Conclusion Postoperative lymph node sorting for gastric cancer can significantly increase the number of total lymph nodes and positive lymph nodes,reduce lymph node migration,and improve postoperative survival time.

OBJECTIVE: To compare the performance of machine learning models and traditional Cox regression model in predicting postoperative outcomes of patients with esophagogastric junction adenocarcinoma (AEG). METHODS: This study was conducted among 203 AEG patients with complete clinical and follow-up data, who were treated in our hospital between September, 2015 and October, 2020. The clinicopathological data of the patients were processed for analysis using R language package and divided into training and validation datasets at the ratio of 3:1. The Cox proportional hazards regression model and 4 machine learning models were constructed for analyzing the datasets. ROC curves, calibration curves and clinical decision curves (DCA) were plotted. Internal validation of the machine learning models was performed to assess their predictive efficacy. The predictive performance of each model was evaluated by calculating the area under the curve (AUC), and the model fitting was assessed using the calibration curve. RESULTS: For predicting 3-year survival based on the validation dataset, the AUC was 0.870 for Cox proportional hazard regression model, 0.901 for eXtreme Gradient Boosting (XGBoost), 0.791 for random forest, 0.832 for support vector machine, and 0.725 for multilayer perceptron; For predicting 5-year survival, the AUCs of these models were 0.915, 0.916, 0.758, 0.905, and 0.737, respectively. For internal validation, the AUCs of the 4 machine learning models decreased in the order of XGBoost (0.818), random forest (0.758), support vector machine (0.0.804), and multilayer perceptron (0.745). CONCLUSION The machine learning models show better predictive efficacy for survival outcomes of patients with AEG than Cox proportional hazard regression model, especially when proportional odds assumption or linear regression models are not applicable. XGBoost models have better performance than the other machine learning models, and the multi-layer perception model may have poor fitting results for a limited data volume.
Humans ; Adenocarcinoma ; Prognosis ; Machine Learning ; Esophagogastric Junction