Thyroid cancer is caused by multiple factors, including genetics, environment, metabolism, and the immune microenvironment, among which ionizing radiation exposure is an important risk factor for thyroid cancer. As one of the most sensitive target organs of ionizing radiation, the thyroid gland may have different risks of thyroid cancer caused by different types of ionizing radiation exposures, such as medical exposure, occupational exposure, and emergency exposure. The sensitivity of children and adolescents are higher than that of adults. The dose-response relationship still needs to be further explored. The molecular mechanism between ionizing radiation and the increased risk of thyroid cancer is complex, which may involve DNA damage and repair abnormalities, gene mutations, non-coding RNA regulation, DNA methylation, cell cycle regulation imbalance, and immune microenvironment changes. This article reviews the risk and molecular mechanisms associated with different types of ionizing radiation exposure in thyroid cancer, based on literature retrieved from CNKI and PubMed databases. It aims to provide a theoretical basis for the early monitoring, prevention, and intervention of thyroid cancer related to ionizing radiation exposure.

[This corrects the article DOI: 10.1016/j.jpha.2023.08.006.].

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 evaluate the correlation and consistency between an artificial intelligence(AI)bone mineral density(BMD)measurement system based on 3D-Densenet neural network technology and quantitative computed tomography(QCT)in measuring BMD,as well as to assess its effectiveness in diagnosing osteoporosis(OP).Methods A total of 1 201 participants who underwent low dose computed tomography(LDCT)were retrospectively included.The AI BMD measurement system and QCT were utilized to measure the BMD of T12,L1,L2 vertebrae,and the average BMD.Consistency and correlation of BMD measurements between the two methods were assessed using Bland-Altman,Pearson,and Kappa analyses.With QCT results as the reference standard,the receiver operating characteristic(ROC)curve was drawn to evaluate the accuracy of AI BMD measurement system in diagnosing OP.Results The r and r2 for the average BMD measured by the two methods were 0.997 and 0.993,respectively.The Kappa value for the diagnosis of normal BMD,low bone mass,and OP using the AI BMD measurement system was 0.905.The area under the curve(AUC)for diagnosing OP using the AI BMD measurement system was 0.998,with a sensitivity of 0.888 and specificity of 0.997.Conclusion The AI BMD measurement system based on 3D-Densenet neural network technology has a high correlation and consistency with the QCT measurement result,which can accurately diagnose normal BMD,low bone mass,and OP.

Objective To observe the value of CT radiomics-based nomogram for predicting difference of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.Methods Totally 126 patients with gastric cancer diagnosed by surgical pathology were retrospectively analyzed.The patients were divided into concordant group(n=77)and inconsistent group(n=49)according to the concordance between endoscopic biopsy and postoperative pathology results or not,also divided into training set and validation set at the ratio of 2∶1.Clinical predictors were screened,then a clinical prediction model was constructed.Radiomics features were extracted based on venous-phase CT images and screened using L1 regularization.Radiomics models were constructed using 3 machine learning(ML)algorithms,i.e.decision trees,random forests and logistic regression.The nomogram based on clinical and the best ML radiomics model was constructed,and the efficacy and clinical utility of the above models and nomogram for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology were evaluated.Results Patients'age,platelet count,and arterial-phase CT values of tumors were all independent predictors of inconsistency between endoscopic biopsy and postoperative pathology of Lauren types of gastric cancer.CT radiomics model using random forests algorithm showed better predictive efficacy among 3 ML models,with the area under the curve(AUC)of 0.835 in training set and 0.724 in validation set,respectively.The AUC of clinical model,radiomics model and the nomogram in training set was 0.764,0.835 and 0.884,while was 0.760,0.724 and 0.841 in validation set,respectively.In both training set and validation set,the nomogram showed a good fit and considerable clinical utility.Conclusion CT radiomics-based nomogram had potential clinical application value for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.

Objective To observe the value of spectral CT multi-parameter imaging for preoperative predicting lymph node metastasis(LNM)of gastric cancer.Methods Totally 136 patients with gastric adenocarcinoma were retrospectively enrolled.The patients were further divided into LNM group(n=74)and non-LNM group(n=62)according to postoperative pathological findings of lymph nodes status.Clinical data,conventional CT findings and spectral CT parameters were compared between groups.Factors being significant different between groups were included in multivariate logistic regression analysis to screen independent predictors of gastric cancer LNM.Clinical+conventional CT model(model 1),spectrum CT model(model 2)and combined model(model 3)were constructed based on the above independent predictors,respectively.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for preoperative predicting LNM of gastric cancer.Results CT-N stage,CT-T stage,70,100 and 140 keV CT valuestumor at arterial phase(AP),arterial enhancement fraction(AEF)and normalized iodine concentration at venous phase(NICVP)were all independent predictors of gastric cancer LNM(all P＜0.05).AUC of model 3 was 0.846,higher than that of model 1 and model 2(AUC=0.767,0.774,Z=-0.368,-2.373,both P＜0.05)for preoperative predicting LNM of gastric cancer,while the latter two were not significantly different(Z=-0.152,P=0.879).Conclusion Spectral CT multi-parameter imaging could effectively predict LNM of gastric cancer preoperatively.

Objective:To investigate the effect of nuclear factor E2-related factor 2 (Nrf2) on the cellular tight junction protein Claudin-18 in endotoxin-induced acute lung injury (ALI).Methods:Eighteen healthy male C57BL/6 mice were divided into control group, endotoxin-induced ALI model group (ALI group) and Nrf2 activator tert-butylhydroquinone (tBHQ) pretreatment group (tBHQ+ALI group) according to random number table method, with 6 mice in each group. Mice endotoxin-induced ALI model was reproduced by intraperitoneal injection of lipopolysaccharide (LPS, 15 mg/kg), and the mice in the control group was injected with an equal amount of phosphate buffer solution (PBS). The mice in the tBHQ+ALI group received three intraperitoneal injections of tBHQ (a total of 50 mg/kg) at an interval of 1 hour before molding. The last injection of tBHQ was accompanied by LPS of 15 mg/kg. The mice in the control group and model group were given equal amounts of PBS, and PBS or LPS was given at the last injection. The mice were sacrificed at 12 hours after LPS injection to take lung tissues. After the lung tissue was stained with hematoxylin-eosin (HE) staining, the pathological changes were observed under light microscopy, and the lung injury score was calculated. The lung wet/dry ratio (W/D) was determined. Nrf2 protein expression in the lung tissue was detected by Western blotting. Positive expression of Claudin-18 in the lung tissue was determined by immunohistochemistry.Results:The lung tissue showed normal structure, without significant pathological change in the control group. Compared with the control group, the alveolar septum widened accompanied by inflammatory cell infiltration, capillary hyperemia and tissue edema in the ALI group, the lung injury score and lung W/D ratio were significantly increased (lung injury score: 6.50±1.05 vs. 1.83±0.75, lung W/D ratio: 3.79±0.22 vs. 3.20±0.14, both P < 0.01), and the Nrf2 protein expression and Claudin-18 positive expression in the lung tissue were significantly lowered [Nrf2 protein (Nrf2/β-actin): 0.41±0.33 vs. 1.22±0.33, Claudin-18 ( A value): 0.28±0.07 vs. 0.44±0.10, both P < 0.05]. After tBHQ pretreatment, the degree of lung histopathological injury was significantly reduced compared with the ALI group, the alveolar space slightly abnormal, inflammatory cell infiltration and tissue edema reduced, the lung injury score and lung W/D ratio were significantly decreased (lung injury score: 3.00±0.89 vs. 6.50±1.05, lung W/D ratio: 3.28±0.19 vs. 3.79±0.22, both P < 0.01), and Nrf2 protein expression and Claudin-18 positive expression in the lung tissue were significantly increased [Nrf2 protein (Nrf2/β-actin): 1.26±0.09 vs. 0.41±0.33, Claudin-18 ( A valure): 0.45±0.04 vs. 0.28±0.07, both P < 0.05]. Conclusion:Nrf2 alleviated pulmonary edema and improved endotoxin-induced ALI by up-regulating Claudin-18 expression.

Imaging omics can predict disease evolution, progression, and treatment response by extracting high-dimensional quantitative features from medical imaging. Positron emission tomography (PET) related imaging omics has been a research hotspot in recent years, and its application in urogenital system tumors is rapidly increasing. This article provides a review of the research progress on the application of PET related imaging omics in urogenital system tumors, and discusses the existing challenges and future prospects, in order to provide reference for further clinical application research.

Objective To observe the reproducibility of radiomics feature(RF)extracted from virtual monoenergetic image(VMI)of rabbit VX2 hepatoma models obtained with 3 different dual-energy CT(DECT)systems,and to explore relationship of reproducibility and diagnostic performance of RF.Methods Fifteen rabbits with VX2 hepatoma were randomly divided into 3 groups(each n=5).Contrast-enhanced abdominal CT scanning under volume CT dose index(CTDIvol)levels of 6,9 and 12 mGy were performed with dual-source DECT(dsDECT),rapid kV switching DECT(rsDECT)and dual-layer detector DECT(dlDECT),respectively.VMI were reconstructed at 10 keV increments from 40 to 140 keV.RF were extracted from VMI,the reproducibility was assessed using intra-class correlation coefficient(ICC),and those with ICC≥0.8 were considered as reproducible RF.The percentage of reproducible features(denoted by R)were compared among different scanner pairings and different CTDIvol levels.Within each CTDIvol group,the reconstruction energy levels yielding the maximum number(denoted by N)of common RF across different scanner pairings were identified.The receiver operating characteristic(ROC)curve was drawn,the area under the curve(AUC)was calculated,and the diagnostic efficacies of reproducible RF and other RF were compared under optimal reproducible conditions.Spearman correlation coefficient between ICC and the corresponding AUC of RF were calculated.Results RrsDECT-dsDECT(6.45％,95％CI[2.36％,8.87％])was higher than RdlDECT-dsDECT(0.72％,95％CI[0.15％,1.79％])and RrsDECT-dlDECT(1.43％,95％CI[0.60％,4.06％])(all adjusted P＜0.05),R9mGy(3.70％,95％CI[1.31％,5.73％])and R12mGy(2.63％,95％CI[0.60％,6.69％])were higher than R6mGy(1.31％,95％CI[0.12％,1.55％])(all adjusted P＜0.05).The optimal reproducible reconstruction energy levels of RF under CTDIvol of 6,9 and 12 mGy concentrated at 50-70 keV.AUC of reproducible RFs were higher than of other RF(all adjusted P＜0.05)and had certain correlation with the reproducibility(rs=0.102-0.516,P＜0.05).Conclusion The reproducibility of RF extracted from contrast-enhanced VMI CT images of rabbit VX2 hepatoma models associated with DECT scanner,CTDIvol level and reconstruction energy level.RF with higher reproducibility might have better diagnostic performance.

Objective To investigate the value of CT radiomics combined with CT and preoperative pathological features for predicting postoperative early recurrence(ER)of local advanced esophageal squamous cell carcinoma(LAESCC).Methods Data of 334 patients with LAESCC were retrospectively analyzed.The patients were divided into training set(n=234)and verification set(n=100)at the ratio of 7:3 and were followed up to observe ER(recurrence within 12 months after surgery)or not.Univariate and multivariate logistic regression were used to analyze clinical,CT and preoperative pathological features of LAESCC in patients with or without ER in training set.The independent risk factors of ER were screened,and a CT-preoperative pathology model was constructed.Based on venous phase CT in training set,the radiomics features of lesions were extracted and screened to establish radiomics model,and finally a combined model was established based on radiomics model and the independent risk factors.Receiver operating characteristic(ROC)curves were drawn,and the area under the curve(AUC)was calculated to evaluate the diagnostic efficacy of each model.Results Among 334 cases,168 were found with but 166 without ER.In training set,117 cases were found with while the rest 117 without ER,while in verification set,51 were found with but 49 without ER.The length of lesions,cT stage and cN stage shown on CT and tumor differentiation degree displayed with preoperative pathology were all independent risk factors for ER of LAESCC(all P＜0.05).The AUC of CT-preoperative pathology model in training set and validation set was 0.759 and 0.783,respectively.Ten best radiomics features of LAESCC were selected,and AUC of the established radiomics model in training set and validation set was 0.770 and 0.730,respectively.The AUC of combined model in training and validation set was 0.838 and 0.826,respectively.The AUC of CT radiomics combined with CT and preoperative pathological features in training set was higher than that of CT-preoperative pathologymodel and radiomics model(both P＜0.01).Conclusion CT radiomics combined with CT and preoperative pathological features could effectively predict postoperative ER of LAESCC.