Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

Objective To observe the value of T2 mapping for quantitatively evaluating the changes of junctional zone and outer myometrium caused by endometrial fibrosis.Methods A total of 73 infertility patients with endometrial fibrosis confirmed by hysteroscopy(disease group)and 33 healthy women of childbearing age(control group)were prospectively enrolled,and MR examinations were performed at the late proliferative phase of endometrium.The thickness and T2 value of junctional zone,T2 value of outer myometrium on anterior,posterior and fundus wall of midsagittal corpus uteri were measured,and the mean value of the above measurements on the three walls were calculated.Receiver operating characteristic curves were constructed,the areas under the curves(AUC)were calculated to explore the efficacy of those with significant difference among the mean thickness and the mean T2 value of junctional zone,the mean T2 value of outer myometrium and their combination for evaluating endometrial fibrosis.Results The thickness and T2 value of anterior wall,posterior wall,fundus wall and the mean junctional zone in disease group were all significantly higher than those in control group(all P＜0.001).No significant difference of T2 value of anterior wall,posterior wall,fundus wall nor the mean outer myometrium was found between groups(all P＞0.05).The mean thickness and the mean T2 value of junctional zone and their combination could be used to effectively evaluate endometrial fibrosis,with AUC of 0.839,0.822 and 0.922,respectively,and their combination had the best performance(both P＜0.01).Conclusion T2 mapping could be used to quantitatively evaluate the injury of junctional zone caused by endometrial fibrosis.

Objective To explore the application of artificial intelligence quantitative analysis in the prognostic assessment of patients with connective tissue disease-associated interstitial lung disease(CTD-ILD).Methods A total of 67 patients with CTD-ILD were retrospectively selected.All subjects underwent high-resolution computed tomography(HRCT)scanning and were categorized into three groups,namely mild,moderate and severe groups,based on the results of pulmonary function tests.The survival rates of patients in each group were compared using Kaplan-Meier curves and analysis of variance.The univariate analysis was employed to assess the rela-tionships between artificial intelligence parameters and patient prognosis.Significant results were then incorporated into a multifacto-rial Cox regression model to construct the most accurate predictive model.Results A significant difference in survival rate was observed among the three groups(P<0.05).Univariate analysis revealed that the volume and percentage of lung infection in deceased patients were significantly greater than those in surviving patients,while the lung volume in deceased patients was significantly smaller than that in surviving patients.The analysis showed left lung volume and the percentage of lesion components CT value≤-750 HU as risk factors for prognosis,and the combination of these two factors as the most effective predictive model.Conclusion The artificial intelligence analysis system for lung lesions provides a new systematic and quantitative method for the prognostic assessment of CTD-ILD patients,which can be used for the prognostic assessment and follow-up of CTD-ILD patients.

BACKGROUND:As a temporary matrix for new bone growth,the porous scaffold plays a key role in the process of bone repair.The structural design of porous scaffolds is a research priority in the process of bone repair.OBJECTIVE:To summarize traditional bone scaffolds(regular,uniform scaffolds)and bionic scaffolds(irregular,inhomogeneous scaffolds)in the field of bone tissue engineering research.METHODS:A computerized search was performed in the databases of CNKI,VIP,WanFang,Web of Science,Science Direct,PubMed,and EI.Literature published from January 2008 to March 2024 was selected.The search terms in Chinese included"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,triple-period minimal surfaces."The search terms in English were"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,TPMS."Finally,81 articles were included for review.RESULTS AND CONCLUSION:The structural design of bone scaffolds is the key to achieve bone repair and bone regeneration,and scaffold technology in bone tissue engineering has made remarkable progress.Traditional regular porous scaffolds are widely used due to their simple manufacturing process and good mechanical properties.However,these scaffolds often lack biological activity and are difficult to mimic the complex microenvironment of natural bone tissue,limiting their ability to promote cell proliferation and bone regeneration.On the contrary,bionic scaffolds provide a more suitable physiological microenvironment by mimicking the structural features of natural bone tissues,which promotes the proliferation and differentiation of osteoblasts,as well as the formation of new bone,and provides a new way of thinking for the effective treatment of bone defects.Despite the great potential of bionic scaffolds in theory,they still face many challenges in practical applications.Factors such as the scaffold's biocompatibility,bioactivity,and its long-term stability still need to be further verified through clinical trials.

Purpose To investigate correlation of ADP ribosylation-like factor 6 interacting protein 4(ARL6IP4)expression with the pathological characteristics and clinical prognosis in primary colon cancer.Methods The ARL6IP4 mRNA expression in tumor and adjacent normal tissues of 133 colon cancer patients was analyzed by RT-qPCR,and its relationship with tumor location,pathological TNM stage,and 3-year survival prognosis was assessed.Additionally,ARL6IP4 protein expression was analyzed by immunohistochemistry in 30 cases,of which 16 cases were analyzed by immunofluorescence.Results The colon cancer presented significantly higher mRNA and protein levels of ARL6IP4 than adjacent normal tissues(t=4.221,P=5.200 × 10-5;t=7.421,P=3.537 × 10-8).The relative ex-pression level of ARL6IP4 mRNA in colon cancer was positively correlated with pathological TNM stage,N stage and M stage(P＜0.05),and negatively correlated with 3-year cumulative survival probability(P＜0.01).Additionally,sig-moid colon cancer presented significantly higher ARL6IP4 expression than other colon cancers,and at the cellular lev-el,ARL6IP4 was predominantly expressed in the cell nucleus.Conclusion The ARL6IP4 expression in colon cancer is higher than that in adjacent normal tissues,which is closely related to tumor metastasis and clinical prognosis.

Objective:To evaluate the value of a combined model based on primary lesion 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer (NSCLC) . Methods:A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set ( n=142) and the validation set ( n=61) at a ratio of 7∶3. A predictive model was developed in the training set, and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score (Radscore). Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic (ROC) curves and clinical application value was assessed by calibration curves and decision curve analysis (DCA) . Results:Among 203 NSCLC patients, 116 had lymph node metastasis, with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening, and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that, sex ( OR=0.48, 95% CI: 0.24-0.95, P=0.036), tumor marker levels ( OR=3.81, 95% CI: 1.84-7.91, P<0.001), long diameter of tumor ( OR=2.56, 95% CI: 1.27-5.16, P=0.009), short diameter of tumor ( OR=3.73, 95% CI: 1.75-7.92, P=0.001), vacuolar sign ( OR=0.32, 95% CI: 0.12-0.86, P=0.024), ring-like metabolism ( OR=3.67, 95% CI: 1.33-10.13, P=0.012), maximum standardized uptake value (SUV max) ( OR=6.57, 95% CI: 3.03-14.25, P<0.001), metabolic tumor volume (MTV) ( OR=2.91, 95% CI: 1.43-5.92, P=0.003), total lesion glycolysis (TLG) ( OR=4.23, 95% CI: 2.08-8.59, P<0.001), Radscore-PET ( OR=21.93, 95% CI: 9.04-53.20, P<0.001) and Radscore-CT ( OR=13.72, 95% CI: 6.12-30.76, P<0.001) were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that, tumor marker levels ( OR=2.55, 95% CI: 1.11-5.90, P=0.028), vacuolar sign ( OR=0.26, 95% CI: 0.08-0.83, P=0.023), SUV max ( OR=5.94, 95% CI: 1.99-17.75, P=0.001), Radscore-PET ( OR=25.51, 95% CI: 5.92-110.22, P<0.001), and Radscore-CT ( OR=8.68, 95% CI: 2.73-27.61, P<0.001) were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors, models were constructed: the traditional model (tumor marker levels, vacuolar sign, SUV max), the PET model (SUV max, Radscore-PET), the CT model (vacuolar sign, Radscore-CT), and the combined model (tumor marker levels, vacuolar sign, SUV max, Radscore-PET, Radscore-CT). ROC curve analysis showed that, the area under curve (AUC) of the traditional, PET, CT, and combined models in the training set were 0.75 (95% CI: 0.67-0.82), 0.90 (95% CI: 0.84-0.95), 0.85 (95% CI: 0.78-0.90), and 0.94 (95% CI: 0.88-0.97), respectively. The predictive value of the combined model was higher than that of the traditional model ( Z=5.01, P<0.001), the PET model ( Z=1.99, P=0.047), and the CT model ( Z=3.25, P=0.001). In the validation set, the AUCs for the traditional model, PET model, CT model, and combined model were 0.65 (95% CI: 0.52-0.77), 0.86 (95% CI: 0.74-0.93), 0.85 (95% CI: 0.73-0.93), and 0.90 (95% CI: 0.80-0.96), respectively. The predictive value of the combined model was superior to that of the traditional model ( Z=3.23, P=0.001). The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%, while in the validation set, the sensitivity and specificity were 82.61% and 94.74%, respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions:Tumor marker levels, vacuolar sign, SUV max, Radscore-PET, and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.

Purpose To investigate correlation of ADP ribosylation-like factor 6 interacting protein 4(ARL6IP4)expression with the pathological characteristics and clinical prognosis in primary colon cancer.Methods The ARL6IP4 mRNA expression in tumor and adjacent normal tissues of 133 colon cancer patients was analyzed by RT-qPCR,and its relationship with tumor location,pathological TNM stage,and 3-year survival prognosis was assessed.Additionally,ARL6IP4 protein expression was analyzed by immunohistochemistry in 30 cases,of which 16 cases were analyzed by immunofluorescence.Results The colon cancer presented significantly higher mRNA and protein levels of ARL6IP4 than adjacent normal tissues(t=4.221,P=5.200 × 10-5;t=7.421,P=3.537 × 10-8).The relative ex-pression level of ARL6IP4 mRNA in colon cancer was positively correlated with pathological TNM stage,N stage and M stage(P＜0.05),and negatively correlated with 3-year cumulative survival probability(P＜0.01).Additionally,sig-moid colon cancer presented significantly higher ARL6IP4 expression than other colon cancers,and at the cellular lev-el,ARL6IP4 was predominantly expressed in the cell nucleus.Conclusion The ARL6IP4 expression in colon cancer is higher than that in adjacent normal tissues,which is closely related to tumor metastasis and clinical prognosis.

Objective To explore the application of artificial intelligence quantitative analysis in the prognostic assessment of patients with connective tissue disease-associated interstitial lung disease(CTD-ILD).Methods A total of 67 patients with CTD-ILD were retrospectively selected.All subjects underwent high-resolution computed tomography(HRCT)scanning and were categorized into three groups,namely mild,moderate and severe groups,based on the results of pulmonary function tests.The survival rates of patients in each group were compared using Kaplan-Meier curves and analysis of variance.The univariate analysis was employed to assess the rela-tionships between artificial intelligence parameters and patient prognosis.Significant results were then incorporated into a multifacto-rial Cox regression model to construct the most accurate predictive model.Results A significant difference in survival rate was observed among the three groups(P<0.05).Univariate analysis revealed that the volume and percentage of lung infection in deceased patients were significantly greater than those in surviving patients,while the lung volume in deceased patients was significantly smaller than that in surviving patients.The analysis showed left lung volume and the percentage of lesion components CT value≤-750 HU as risk factors for prognosis,and the combination of these two factors as the most effective predictive model.Conclusion The artificial intelligence analysis system for lung lesions provides a new systematic and quantitative method for the prognostic assessment of CTD-ILD patients,which can be used for the prognostic assessment and follow-up of CTD-ILD patients.

BACKGROUND:As a temporary matrix for new bone growth,the porous scaffold plays a key role in the process of bone repair.The structural design of porous scaffolds is a research priority in the process of bone repair.OBJECTIVE:To summarize traditional bone scaffolds(regular,uniform scaffolds)and bionic scaffolds(irregular,inhomogeneous scaffolds)in the field of bone tissue engineering research.METHODS:A computerized search was performed in the databases of CNKI,VIP,WanFang,Web of Science,Science Direct,PubMed,and EI.Literature published from January 2008 to March 2024 was selected.The search terms in Chinese included"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,triple-period minimal surfaces."The search terms in English were"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,TPMS."Finally,81 articles were included for review.RESULTS AND CONCLUSION:The structural design of bone scaffolds is the key to achieve bone repair and bone regeneration,and scaffold technology in bone tissue engineering has made remarkable progress.Traditional regular porous scaffolds are widely used due to their simple manufacturing process and good mechanical properties.However,these scaffolds often lack biological activity and are difficult to mimic the complex microenvironment of natural bone tissue,limiting their ability to promote cell proliferation and bone regeneration.On the contrary,bionic scaffolds provide a more suitable physiological microenvironment by mimicking the structural features of natural bone tissues,which promotes the proliferation and differentiation of osteoblasts,as well as the formation of new bone,and provides a new way of thinking for the effective treatment of bone defects.Despite the great potential of bionic scaffolds in theory,they still face many challenges in practical applications.Factors such as the scaffold's biocompatibility,bioactivity,and its long-term stability still need to be further verified through clinical trials.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.