Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models. METHODS A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies. RESULTS There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good. CONCLUSIONS The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective To explore the applicative value of spectral CT in increasing positive rates of lung cancer puncture and reducing complications during CT guided percutaneous transthoracic needle biopsy(PTNB).Methods The pathological results and complica-tion incidences of 260 PTNB patients were analyzed retrospectively.All patients were divided into three groups:group A(conventional CT group,103 cases)used a scheme based on conventional enhanced CT;group B(PET/CT group,84 cases)used a scheme combining the maximum standardized uptake value(SUVmax)with conventional enhanced CT;group C(spectral CT group,73 cases)used a scheme of quantitative spectral CT parameters and images.Results Group A included 103 cases in total,of which 87 were positive(84.47％),41 pneumothorax(39.81％),and 31 hemorrhage(30.10％).Group B totaled 84 cases,including 82 positive cases(97.62％),19 cases of pneumothorax(22.62％),and 11 cases of hemorrhage(13.10％).Group C was of 73 cases,including 70 positive cases(95.89％),16 cases of pneumothorax(21.92％),and 10 cases of hemorrhage(13.70％).There were statistically significant differ-ences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences among groups A,B,and C(P＜0.05).There were also statistically significant differences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences between groups A and B or groups A and C(P＜0.016 7),respectively.However,no statistically significant differences were found between groups B and C in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences(P＞0.016 7).Conclusion Spectral CT can improve the positive rate of lung cancer and reduce the risk of pneumothorax and hemorrhage with PTNB.

Chimeric antigen receptor natural killer(CAR-NK)cell therapy,as an emerging cellular immunotherapy strategy,has demonstrated a broader clinical application potential compared to CAR-T cell therapy due to its high safety profile and the unique advantages of'off-the-shelf'preparation.This article thoroughly discusses the antitumor mechanisms of CAR-NK cells,elucidating their targeted recognition mechanism,inherent cytotoxic activity,and the latest advancements in optimizing specific receptors to enhance their adaptability within the tumor microenvironment.Additionally,it provides an in-depth analysis of the advantages and challenges of various sources for CAR-NK cells,including peripheral blood,umbilical cord blood,induced pluripotent stem cells(iPSCs),and NK-92 cells,while summarizing their major challenges in the tumor immune microenvironment,such as insufficient persistence,immune suppression,and antigen heterogeneity.Finally,this article presents the therapeutic potential,limitations,and future perspectives of CAR-NK therapy in treating solid tumors,with a focus on its ongoing development and clinical translation.

In 2024,significant progress has been made in the field of immunology and inflammation,contributing to a series of important research achievements in immunology theories and translational applications.These latest immunological research achieve-ments have deepened our understanding of the fundamental principles of immunity and inflammation,laying a fundamental basis for re-vealing the mechanisms of autoimmune diseases,inflammatory diseases,and tumors,and developing new and effective treatment strategies.In this article,we summarized some of the representative advances in immunology research at home and abroad in 2024,and discussed the future challenges and directions.

OBJECTIVE To construct a prediction model for the efficacy of serotonin-norepinephrine reuptake inhibitor(SNRI)in inpatients with moderate and severe depression by using a machine learning method.METHODS The case records of inpatients with moderate and severe depression treated with SNRI antidepressants were collected from a third-grade class-A hospital in Xinjiang from January 2022 to October 2024;those patients were divided into effective group and ineffective group based on the Hamilton depression scale-24 score reduction rate.After screening the characteristic variables related to the therapeutic efficacy of SNRI drugs through LASSO regression,five prediction models including support vector machine,k-nearest neighbor,random forest,lightweight gradient boosting machine and extreme gradient boosting were constructed using the training set.Bayesian optimization was used to adjust the hyperparameters of these models.The performance of the models was evaluated in the validation set to select the optimal model.The Shapley additive explanations method was used to perform explainable analysis on the best model.RESULTS The medical records from 355 hospitalized patients with moderate and severe depression were collected,comprising 285 cases in the effective group and 70 cases in the ineffective group,resulting in an overall therapeutic response rate of 80.28%.After feature variable screening,five characteristic variables for therapeutic efficacy were obtained,including Hamilton anxiety scale,blood urea nitrogen,combination of anti-anxiety drugs,drinking history,and first onset of the disease.Compared with other models,the random forest model performed the best.The area under the receiver operating characteristic curve was 0.85,the area under the precision-recall curve was 0.87,the accuracy was 0.74,and the recall rate value was 0.75.CONCLUSIONS The random forest model constructed based on five characteristic variables demonstrates potential for predicting the therapeutic efficacy of SNRI antidepressants in hospitalized patients with moderate and severe depression.

Objective To explore the applicative value of spectral CT in increasing positive rates of lung cancer puncture and reducing complications during CT guided percutaneous transthoracic needle biopsy(PTNB).Methods The pathological results and complica-tion incidences of 260 PTNB patients were analyzed retrospectively.All patients were divided into three groups:group A(conventional CT group,103 cases)used a scheme based on conventional enhanced CT;group B(PET/CT group,84 cases)used a scheme combining the maximum standardized uptake value(SUVmax)with conventional enhanced CT;group C(spectral CT group,73 cases)used a scheme of quantitative spectral CT parameters and images.Results Group A included 103 cases in total,of which 87 were positive(84.47％),41 pneumothorax(39.81％),and 31 hemorrhage(30.10％).Group B totaled 84 cases,including 82 positive cases(97.62％),19 cases of pneumothorax(22.62％),and 11 cases of hemorrhage(13.10％).Group C was of 73 cases,including 70 positive cases(95.89％),16 cases of pneumothorax(21.92％),and 10 cases of hemorrhage(13.70％).There were statistically significant differ-ences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences among groups A,B,and C(P＜0.05).There were also statistically significant differences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences between groups A and B or groups A and C(P＜0.016 7),respectively.However,no statistically significant differences were found between groups B and C in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences(P＞0.016 7).Conclusion Spectral CT can improve the positive rate of lung cancer and reduce the risk of pneumothorax and hemorrhage with PTNB.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

In 2024,significant progress has been made in the field of immunology and inflammation,contributing to a series of important research achievements in immunology theories and translational applications.These latest immunological research achieve-ments have deepened our understanding of the fundamental principles of immunity and inflammation,laying a fundamental basis for re-vealing the mechanisms of autoimmune diseases,inflammatory diseases,and tumors,and developing new and effective treatment strategies.In this article,we summarized some of the representative advances in immunology research at home and abroad in 2024,and discussed the future challenges and directions.