Purpose To investigate the value of intratumoral and peritumoral radiomics features based on multi-parametric MRI for preoperative prediction of lymphovascular invasion(LVI)in clinical lymph node-negative(cN0)breast cancer.Materials and Methods This retrospective study included 280 patients with pathologically confirmed breast cancer who underwent preoperative MRI at Henan Provincial People's Hospital from January 2017 to May 2021.Patients were randomly divided into a training cohort and a testing cohort.After Z-score normalization,feature selection was performed using Select K Best and least absolute shrinkage and selection operator regression.Random forest algorithms were used to construct intratumoral,peritumoral,and combined intratumoral-peritumoral radiomics models for LVI prediction.Model performance and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC),calibration curves and decision curve analysis.Results High Ki-67 expression(≥20%),axillary lymph node metastasis and positive diffusion weighted imaging(DWI)margin sign were more common in the LVI-positive group(χ2=5.959,18.316,20.554,all P<0.05).In the testing cohort,the AUC values of the dynamic contrast-enhanced(DCE)-Intra and DCE-Com models for predicting LVI status were higher than those of the DWI sequence,whereas the AUC value of the DWI-Peri model was higher than that of the DCE sequence.The DWI-DCE-Com model achieved AUCs of 0.836 and 0.818 in the training and testing cohorts,respectively,which surpassed the predictive performance of single-sequence intratumoral-peritumoral radiomics models(DWI-Com,DCE-Com).Decision curve analysis showed that the DWI-DCE-Com model provided greater net clinical benefit across a reasonable range of threshold probabilities.Conclusion Radiomics models based on multiparametric MRI features from intratumoral and peritumoral regions can effectively predict LVI status in cN0 breast cancer,offering valuable support for preoperative individualized treatment decision-making.

Lung cancer represents the leading cause of cancer-related mortality worldwide,with non-small cell lung cancer(NSCLC)constituting the predominant histological subtype.Molecular targeted therapy serves as first-line treatment for advanced NSCLC patients with driver gene mutations,yet therapeutic response and prognosis vary substantially across molecular phenotypes.Conventional CT evaluation relies primarily on unidimensional tumor measurements but provides limited morphological and functional information.CT radiomics enables extraction and comprehensive analysis of numerous quantitative features from medical images,potentially revealing tumor genotype,treatment response and prognostic information.This approach shows significant promise for assessing targeted therapy outcomes in NSCLC.This review summarizes current applications and research advances in CT radiomics for NSCLC targeted therapy evaluation,providing insights to guide personalized precision medicine.

Purpose To investigate the value of intratumoral and peritumoral radiomics features based on multi-parametric MRI for preoperative prediction of lymphovascular invasion(LVI)in clinical lymph node-negative(cN0)breast cancer.Materials and Methods This retrospective study included 280 patients with pathologically confirmed breast cancer who underwent preoperative MRI at Henan Provincial People's Hospital from January 2017 to May 2021.Patients were randomly divided into a training cohort and a testing cohort.After Z-score normalization,feature selection was performed using Select K Best and least absolute shrinkage and selection operator regression.Random forest algorithms were used to construct intratumoral,peritumoral,and combined intratumoral-peritumoral radiomics models for LVI prediction.Model performance and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC),calibration curves and decision curve analysis.Results High Ki-67 expression(≥20%),axillary lymph node metastasis and positive diffusion weighted imaging(DWI)margin sign were more common in the LVI-positive group(χ2=5.959,18.316,20.554,all P<0.05).In the testing cohort,the AUC values of the dynamic contrast-enhanced(DCE)-Intra and DCE-Com models for predicting LVI status were higher than those of the DWI sequence,whereas the AUC value of the DWI-Peri model was higher than that of the DCE sequence.The DWI-DCE-Com model achieved AUCs of 0.836 and 0.818 in the training and testing cohorts,respectively,which surpassed the predictive performance of single-sequence intratumoral-peritumoral radiomics models(DWI-Com,DCE-Com).Decision curve analysis showed that the DWI-DCE-Com model provided greater net clinical benefit across a reasonable range of threshold probabilities.Conclusion Radiomics models based on multiparametric MRI features from intratumoral and peritumoral regions can effectively predict LVI status in cN0 breast cancer,offering valuable support for preoperative individualized treatment decision-making.

Lung cancer represents the leading cause of cancer-related mortality worldwide,with non-small cell lung cancer(NSCLC)constituting the predominant histological subtype.Molecular targeted therapy serves as first-line treatment for advanced NSCLC patients with driver gene mutations,yet therapeutic response and prognosis vary substantially across molecular phenotypes.Conventional CT evaluation relies primarily on unidimensional tumor measurements but provides limited morphological and functional information.CT radiomics enables extraction and comprehensive analysis of numerous quantitative features from medical images,potentially revealing tumor genotype,treatment response and prognostic information.This approach shows significant promise for assessing targeted therapy outcomes in NSCLC.This review summarizes current applications and research advances in CT radiomics for NSCLC targeted therapy evaluation,providing insights to guide personalized precision medicine.

Objective To observe the value of intratumoral and peritumoral radiomics based on diffusion weighted imaging(DWI)for predicting histological grade of breast cancer.Methods Preoperative DWI data of 700 patients with single breast cancer diagnosed by pathology were retrospectively analyzed.The patients were divided into training set(n= 560,including 381 of grade Ⅰ+Ⅱ and 179 of grade Ⅲ)and test set(n=140,including 95 of grade Ⅰ+Ⅱ and 45 of grade Ⅲ)at the ratio of 8∶2.Intratumoral ROI(ROIintra)was manually delineated on DWI,which was automatically expanded by 3 mm and 5 mm to decline peritumoral ROI(ROIperi,including ROI3 mm and ROI5 mm),then intratumoral-peritumoral ROI(ROIintra+3 mm,ROIintra+5 mm)were obtained.The optimal radiomics features were extracted and screened,and the radiomics model(RM)for predicting the histological grade of breast cancer were constructed.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the predictive efficacy of each model.Calibration curve method was used to evaluate the calibration degree,while decision curve analysis(DCA)was performed to explore the clinical practicability of each model.Results AUC of RMintra,RM+3 mm,RM+5mm,RMintra+3 mm and RMintra+5 mm was 0.750,0.724,0.749,0.833 and 0.807 in training set,while was 0.723,0.718,0.736,0.759 and 0.782 in test set,respectively.In training set,significant differences of AUC was found(all P＜0.01),while in test set,no significant difference of AUC was found among models(all P＞0.05).The calibrations of models were all high.DCA showed that taken 0.02-0.88 as the threshold,the clinical net benefit of RMintra+per were greater in training set,while taken 0.40-0.72 as the threshold,the clinical net benefit of RMintra+per was greater in test set.Conclusion Both DWI intratumoral and peritumoral radiomics could effectively predict histological grade of breast cancer.Combination of intratumoral and peritumoral radiomics was more effective.

Tumor immune microenvironment is a local external tumor environment composed of tumor immune cells and the cytokines secreted by these cells， and it plays a regulatory role in the development and progression of tumors. In the treatment of hepatocellular carcinoma， amino acid metabolism and its reprogramming of proliferating cell metabolism have attracted more and more attention， showing potential in regulating the tumor immune microenvironment. Although amino acid metabolic reprogramming is regarded as a novel approach for tumor therapy， its specific mechanism remains unclear in the regulation of tumor immunity in hepatocellular carcinoma. This article discusses the mechanism of action of amino acid metabolism in the tumor immune microenvironment of hepatocellular carcinoma and its application prospect in clinical practice， in order to provide new ideas for immunotherapy for liver cancer.

Objective:To investigate the value of intratumoral and peritumoral radiomics features of multi-parameter MRI in evaluation of the status of human epithelial growth factor receptor 2 in breast cancer.Methods:The clinical, pathological and imaging data of 340 patients with pathologically confirmed breast cancer in Henan Provincial People′s Hospital from September 2019 to December 2020 were retrospectively collected. All patients were female, 48 (42, 55) years old. All patients underwent multi-parameter breast MRI before surgery, including dynamic contrast-enhanced T 1WI (DCE-T 1WI), fat-suppressed T 2WI (T 2WI) and diffusion-weighted imaging (DWI). The region of interest (ROI) for lesions were manually delineated and the segmented ROIs were zoomed in ring shape by 4 mm to acquire ROI intra and ROI prei, respectively. Then six sets of radiomics features were extracted from ROI intra and ROI prei of DCE-T 1WI, T 2WI and DWI. The cases were divided into a training set (272 cases) and a test set (68 cases) by stratified sampling at a ratio of 4∶1. The Mann-Whitney U test, Select K Best and minimum absolute contraction and selection operator were used for feature selection of the 6 sets of radiomics features. The feature subsets after reduction were used to construct independent and combined radiomics signatures with support vector machine algorithm to predict the HER2 status of breast cancer. Receiver operating characteristic curve was generated and area under curve (AUC) was calculated to compare the prediction performance of different models. Results:Of the 340 patients, 80 were HER2-positive and 260 were HER2-negative. Among the radiomics signatures based on single sequence, the DWI peri showed the best performance in predicting HER2 status of breast cancer, with an AUC of 0.678 for the test set. Among the combination of intratumoral and peritumoral radiomics signatures based on same sequence, the DWI intra+DWI peri had the highest prediction value, achieving an AUC of 0.774 for the testing set. Among the intratumoral or peritumoral radiomics signatures derived from two different sequences, the DCE-T 1WI intra+DWI intra and T 2WI peri+DWI peri showed the best predictive performance, yielding AUC of 0.766 and 0.769 in the testing set, respectively. Among the combination of intratumoral or peritumoral radiomics signatures derived from all 3 sequences or combinations of all features, the DCE-T 1WI intra+T 2WI intra+DWI intra+DCE-T 1WI peri+T 2WI peri+DWI peri obtained the highest prediction efficiency, with an AUC of 0.913 for the testing set. Conclusion:The radiomics features of intratumoral and peritumoral regions based on multi-parameter MRI have a certain value in non-invasive evaluation of HER2 status of breast cancer, which can help clinicians to provide scientific basis for decision-making of targeted therapy in patients with breast cancer.

Objective:To explore the effect of stepwise nursing in postoperative rehabilitation of patients with spinal cord injury.Methods:From January 2020 to June 2021, convenience sampling was used to select 91 patients with spinal cord injury admitted to the Henan Third Provincial People's Hospital as the research object. The patients were divided into the observation group ( n=46) and the control group ( n=45) by random number table method. The control group received routine nursing, while the observation group received stepwise nursing on the basis of the control group. The postoperative functional independence and quality of life were compared between the two groups. Results:After six months of intervention, the scores of the Spinal Cord Independence Measure Ⅲ and the scores of each dimension of the Generic Quality of Life Inventory 74 in the observation group were higher than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Stepwise nursing can promote postoperative functional recovery and improve the quality of life of patients with spinal cord injury.

Objective:To explore the value of mammography-based radiomics for preoperative prediction of axillary lymph node metastasis in breast carcinoma.Methods:The clinical and X-ray data of female patients with pathologically confirmed breast cancer in Henan People′s Hospital from June 2013 to July 2017 were analyzed retrospectively. A total of 214 patients, aged 30-85 (53±11) years, were randomly divided into training set ( n=153) and verification set ( n=61) according to the ratio of 3∶1. According to pathological findings of the axillary lymph node metastasis, 99 cases were divided into positive group and 115 cases into negative group. The lesions were segmented and extracted in X-ray images of mediolateral oblique (MLO) and cranial caudal (CC). Three, nine and seven axillary lymph node metastasis related histologic features were selected from the high dimensional features of CC, MLO and CC combined MLO images by lasso regression model. According to the characteristics of imaging and clinical characteristics, the prediction model was constructed. The prediction ability of the model was verified by 10% cross validation. Results:The lymph node in positive group was larger than negative groups, the difference was statistically significant ( t=2.611, P<0.05). In the validation set, the area under curve (AUC) values of CC, MLO, CC combined with MLO images, clinical features and clinical features combined with CC and MLO images were 0.680, 0.723, 0.740, 0.558 and 0.714, respectively. Among them, CC combined with MLO images had the highest prediction efficiency, and AUC values were higher than CC alone, MLO images and CC combined with MLO images. Conclusions:Quantitative radiomics features of breast tumor extracted from digital mammograms are helpful for preoperatively predicting axillary lymph node metastasis. Future larger studies are needed to further evaluate these findings.

Objective:To explore the value of radiomics features extracted from multi-parameter MRI (mp-MRI) in preoperative prediction of triple negative breast cancer (TNBC) and non triple negative breast cancer (NTNBC).Methods:The clinical and preoperative-MRI data of 371 patients with breast cancer confirmed by surgical pathology from January 2017 to July 2019 in Henan Provincial People′s Hospital were retrospectively analyzed. Based on the results of immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) from postoperative pathological specimens, the cancer was classified as TNBC and NTNBC. Patients were randomly assigned to a training set ( n=250) and a validation set ( n=121). Quantitative radiomics features were extracted from three-dimensional lesions based on dynamic contrast enhanced-T 1WI (DCE-T 1WI) and fat-suppressed T 2WI sequences, and 32 quantitative radiomics features were selected by Mann-Whitney U test, elastic network, and support vector machine recursive feature elimination. Three radiomics signatures were constructed by using the algorithm of support vector machine based on the quantitative radiomics features extracted from fat-suppressed T 2WI, DCE-T 1WI and the mp-MRI of their combination. The prediction performances were evaluated by receiver operating characteristic (ROC) curve and the area under the ROC curve, accuracy, sensitivity, and specificity were calculated. Results:There were 61 patients with TNBC and 310 patients with NTNBC. The clinicopathological characteristics between NTNBC and TNBC were statistically different in the pathological grade (χ2=24.544, P<0.001). Other clinicopathological characteristics (age, maximum diameter of mass, vascular tumor thrombus, axillary lymph nodes) were not statistically differences between NTNBC and TNBC ( P>0.05). The radiomics signature presenting the best performance for predictive TNBC and NTNBC were based on mp-MRI radiomics features. The area under the ROC curve, accuracy, sensitivity, and specificity were 0.91[95% confidence interval (CI) 0.881-0.932], 86.0%, 84.4% and 86.3% in training set, and 0.84 (95%CI 0.807-0.868), 75.2%, 68.7% and 76.1%, in validation set, respectively. Conclusion:Radiomics based on mp-MRI features can be a effectively potential tool for predictive TNBC and NTNBC breast cancer and provide scientific basis for clinicians to make treatment decisions.