BACKGROUND: This study aimed to investigate programmed death-ligand 1 tumor proportion score in predicting the safety and efficacy of PD-1/PD-L1 antibody-based therapy in treating patients with advanced non-small cell lung cancer (NSCLC) in a real-world setting. METHODS: This retrospective, multicenter, observational study enrolled adult patients who received PD-1/PD-L1 antibody-based therapy in China and met the following criteria: (1) had pathologically confirmed, unresectable stage III-IV NSCLC; (2) had a baseline PD-L1 tumor proportion score (TPS); and (3) had confirmed efficacy evaluation results after PD-1/PD-L1 treatment. Logistic regression, Kaplan-Meier analysis, and Cox regression were used to assess the progression-free survival (PFS), overall survival (OS), and immune-related adverse events (irAEs) as appropriate. RESULTS: A total of 409 patients, 65.0% ( n = 266) with a positive PD-L1 TPS (≥1%) and 32.8% ( n = 134) with PD-L1 TPS ≥50%, were included in this study. Cox regression confirmed that patients with a PD-L1 TPS ≥1% had significantly improved PFS (hazard ratio [HR] 0.747, 95% confidence interval [CI] 0.573-0.975, P = 0.032). A total of 160 (39.1%) patients experienced 206 irAEs, and 27 (6.6%) patients experienced 31 grade 3-5 irAEs. The organs most frequently associated with irAEs were the skin (52/409, 12.7%), thyroid (40/409, 9.8%), and lung (34/409, 8.3%). Multivariate logistic regression revealed that a PD-L1 TPS ≥1% (odds ratio [OR] 1.713, 95% CI 1.054-2.784, P = 0.030) was an independent risk factor for irAEs. Other risk factors for irAEs included pretreatment absolute lymphocyte count >2.5 × 10 9 /L (OR 3.772, 95% CI 1.377-10.329, P = 0.010) and pretreatment absolute eosinophil count >0.2 × 10 9 /L (OR 2.006, 95% CI 1.219-3.302, P = 0.006). Moreover, patients who developed irAEs demonstrated improved PFS (13.7 months vs. 8.4 months, P <0.001) and OS (28.0 months vs. 18.0 months, P = 0.007) compared with patients without irAEs. CONCLUSIONS A positive PD-L1 TPS (≥1%) was associated with improved PFS and an increased risk of irAEs in a real-world setting. The onset of irAEs was associated with improved PFS and OS in patients with advanced NSCLC receiving PD-1/PD-L1-based therapy.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Lung Neoplasms/metabolism* ; Aged ; B7-H1 Antigen/metabolism* ; Programmed Cell Death 1 Receptor/metabolism* ; Adult ; Aged, 80 and over ; Immune Checkpoint Inhibitors/therapeutic use*

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective To investigate the effects of Aurora kinase A(AURKA)on the tumor microenvironment of colorectal cancer(CRC)and to predict the active compounds in Chinese herbs that can target AURKA.Methods Based on the transcriptomic data and clinical information from 380 CRC tissues and 51 paracancerous tissues in The Cancer Genome Atlas(TCGA)database,the infiltration of different cells in the tumor tissues was analyzed using xCell and the binding of active compounds of Chinese herbs with AURKA was predicted through molecular docking.Results The expression of AURKA was significantly upregulated in CRC tissues compared with that in paracancerous tissues(P＜0.05),and CRC patients with high AURKA expression had shorter overall survival.Compared with the AURKA low-expression group,the abundance of macrophages,monocytes,and effector memory CD4+and CD8+T cells was significantly downregulated in the AURKA high-expression group(P＜0.05).In addition,the cytotoxicity of T cells was significantly reduced(P＜0.05).Further analysis revealed that AURKA expression was positively correlated with the abundance of myeloid-derived suppressor cells(MDSCs)and the expression levels of their chemokines CXCL2 and CXCL5(P＜0.05).Genes that were differentially expressed between the AURKA high-and low-expression groups were mainly enriched in monocyte migration,chemokine-induced cellular responses,and other related processes.Chinese herbal compounds,including hesperidin,aristololactam A Ⅱ a,anacardic acid,coumestrol,and 17β-estradiol,all showed binding energies to AURKA lower than-1.2 kcal/mol,indicating a certain level of binding stability.Among these Chinese herbal compounds,17β-estradiol exhibited the best binding stability to AURKA-3UOL.Conclusion The high expression of AURKA in CRC tissues suggests a poor clinical prognosis.AURKA can promote the development of a suppressive immune microenvironment in CRC,and 17β-estradiol,an active compound from Chinese herbs,is a potential therapeutic agent targeting AURKA.

Purpose To explore the value of amide proton transfer(APT)imaging combined with diffusion kurtosis imaging(DKI)in the differential diagnosis of prostate carcinoma and benign prostatic hyperplasia.Materials and Methods A retrospective analysis was conducted on 120 patients who underwent multi-parameter prostate MRI and pathological biopsy at the First Affiliated Hospital of Xinxiang Medical University from January 2020 to August 2021,including 66 cases of benign prostatic hyperplasia and 54 cases of prostate carcinoma.The parameters of APT imaging and DKI,including magnetization transfer ratio asymmetry(MTRasym),mean kurtosis(MK),mean diffusion coefficient(MD)and fraction anisotropy were measured,and the parameters between the two groups were compared and analyzed.The receiver operating characteristic curve and Delong test were used to analyze the efficacy of each parameter,APT or DKI sequence alone and their combined diagnosis,and the correlation among the parameters was analyzed.Results Compared with the benign prostatic hyperplasia group,the MTRasym,MK and fraction anisotropy of the prostate carcinoma group were significantly higher,while the MD was significantly lower,with statistical significance(t=8.23,12.53,2.20,-11.12,all P<0.05).The areas under the curve for the above parameters were 0.852,0.933,0.615 and 0.910,respectively.The diagnostic efficacy of APT combined with DKI for differentiating prostate carcinoma from benign prostatic hyperplasia is numerically higher than that of APT alone or DKI alone,with the areas under the curve being 0.994,0.988 and 0.852,respectively,as well as a sensitivity of 96.30%and a specificity of 98.48%.There was a statistically significant difference in efficacy between the APT+DKI combined approach and APT alone(Z=4.387,P<0.05),while no statistically significant difference exists between the combined approach and DKI alone(Z=1.375,P>0.05).MTRasym was positively correlated with MK(r=0.45,P<0.001).MD was negatively correlated with MTRasym and MK(r=-0.439,-0.500,both P<0.001).Conclusion The parameters MTRasym,MK and MD of APT and DKI have relatively high value in distinguishing prostate carcinoma from benign prostatic hyperplasia,and the combined diagnostic efficiency of the two sequences tends to increase,with some parameters showing correlation.

Purpose To explore the value of magnetic resonance amide proton transfer(APT)imaging combined with diffusion weighted imaging(DWI)in the differential diagnosis of prostate cancer and benign prostatic hyperplasia.Materials and Methods A retrospective analysis was made on 52 patients with prostate cancer and 60 patients with benign prostatic hyperplasia in the First Affiliated Hospital of Xinxiang Medical University from February 2020 to August 2021.The APT parameter values magnetization transfer ratio asymmetry(MTRasym)and DWI parameter values apparent diffusion coefficient(ADC)of the two groups were measured,respectively.The parameter differences between the two groups were compared and analyzed,the significant parameters and the efficacy of their joint diagnosis were evaluated.Then compared the performance of each parameter and joint diagnosis,and analyze the correlation between the two parameters.Results The MTRasym in the prostate cancer group[(3.70±0.94)%]was significantly higher than that in the prostate hyperplasia group[(2.35±0.60)%](t=8.89,P＜0.05);ADC value in prostate cancer group[(0.93±0.15)×10-3 mm2/s]was significantly lower than that in the prostate hyperplasia group[(1.32±0.22)×10-3 mm2/s](t=-11.01,P＜0.05).The areas under the curve for identifying prostate cancer and benign prostatic hyperplasia using MTRasym value,ADC value and their combination were 0.886,0.914 and 0.966,respectively.There was no statistically significant difference in the diagnostic efficacy between the MTRasym value and the ADC value(P＞0.05),and the combined diagnostic efficacy of the two was superior to the individual diagnostic efficacy of the MTRasym value and ADC value(Z=3.125,2.776,P＜0.05).The MTRasym was negatively correlated with the ADC value(r=-0.469,P＜0.001).Conclusion APT and DWI have high efficacy in distinguishing between prostate cancer and benign prostatic hyperplasia.The combination of the two sequences has higher diagnostic efficacy.

Purpose To explore the value of amide proton transfer(APT)imaging combined with diffusion kurtosis imaging(DKI)in the differential diagnosis of prostate carcinoma and benign prostatic hyperplasia.Materials and Methods A retrospective analysis was conducted on 120 patients who underwent multi-parameter prostate MRI and pathological biopsy at the First Affiliated Hospital of Xinxiang Medical University from January 2020 to August 2021,including 66 cases of benign prostatic hyperplasia and 54 cases of prostate carcinoma.The parameters of APT imaging and DKI,including magnetization transfer ratio asymmetry(MTRasym),mean kurtosis(MK),mean diffusion coefficient(MD)and fraction anisotropy were measured,and the parameters between the two groups were compared and analyzed.The receiver operating characteristic curve and Delong test were used to analyze the efficacy of each parameter,APT or DKI sequence alone and their combined diagnosis,and the correlation among the parameters was analyzed.Results Compared with the benign prostatic hyperplasia group,the MTRasym,MK and fraction anisotropy of the prostate carcinoma group were significantly higher,while the MD was significantly lower,with statistical significance(t=8.23,12.53,2.20,-11.12,all P<0.05).The areas under the curve for the above parameters were 0.852,0.933,0.615 and 0.910,respectively.The diagnostic efficacy of APT combined with DKI for differentiating prostate carcinoma from benign prostatic hyperplasia is numerically higher than that of APT alone or DKI alone,with the areas under the curve being 0.994,0.988 and 0.852,respectively,as well as a sensitivity of 96.30%and a specificity of 98.48%.There was a statistically significant difference in efficacy between the APT+DKI combined approach and APT alone(Z=4.387,P<0.05),while no statistically significant difference exists between the combined approach and DKI alone(Z=1.375,P>0.05).MTRasym was positively correlated with MK(r=0.45,P<0.001).MD was negatively correlated with MTRasym and MK(r=-0.439,-0.500,both P<0.001).Conclusion The parameters MTRasym,MK and MD of APT and DKI have relatively high value in distinguishing prostate carcinoma from benign prostatic hyperplasia,and the combined diagnostic efficiency of the two sequences tends to increase,with some parameters showing correlation.

Purpose To explore the value of magnetic resonance amide proton transfer(APT)imaging combined with diffusion weighted imaging(DWI)in the differential diagnosis of prostate cancer and benign prostatic hyperplasia.Materials and Methods A retrospective analysis was made on 52 patients with prostate cancer and 60 patients with benign prostatic hyperplasia in the First Affiliated Hospital of Xinxiang Medical University from February 2020 to August 2021.The APT parameter values magnetization transfer ratio asymmetry(MTRasym)and DWI parameter values apparent diffusion coefficient(ADC)of the two groups were measured,respectively.The parameter differences between the two groups were compared and analyzed,the significant parameters and the efficacy of their joint diagnosis were evaluated.Then compared the performance of each parameter and joint diagnosis,and analyze the correlation between the two parameters.Results The MTRasym in the prostate cancer group[(3.70±0.94)%]was significantly higher than that in the prostate hyperplasia group[(2.35±0.60)%](t=8.89,P＜0.05);ADC value in prostate cancer group[(0.93±0.15)×10-3 mm2/s]was significantly lower than that in the prostate hyperplasia group[(1.32±0.22)×10-3 mm2/s](t=-11.01,P＜0.05).The areas under the curve for identifying prostate cancer and benign prostatic hyperplasia using MTRasym value,ADC value and their combination were 0.886,0.914 and 0.966,respectively.There was no statistically significant difference in the diagnostic efficacy between the MTRasym value and the ADC value(P＞0.05),and the combined diagnostic efficacy of the two was superior to the individual diagnostic efficacy of the MTRasym value and ADC value(Z=3.125,2.776,P＜0.05).The MTRasym was negatively correlated with the ADC value(r=-0.469,P＜0.001).Conclusion APT and DWI have high efficacy in distinguishing between prostate cancer and benign prostatic hyperplasia.The combination of the two sequences has higher diagnostic efficacy.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective:To investigate the prognostic value of the Second Revision of the International Staging System (R2-ISS) in patients with newly diagnosed multiple myeloma (NDMM) .Methods:The retrospective study was performed in 326 NDMM patients with immunomodulatory drugs and/or proteasome inhibitors as the first-line treatment attending the Department of Hematology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China, from December 2012 to March 2022. The Kaplan-Meier method was used for the survival analysis, with the Log-rank test comparing the between-group differences and Cox proportional risk regression modeling A multifactorial analysis was performed.Results:①326 patients were included in the study, 190 of whom were males. The median age was 63 years, and the median followup time was 37 months. R2-ISS can effectively predict prognosis, particularly for R-ISS Ⅱ patients. The median progression-free survival (PFS) time of R2-ISS Ⅰ, R2-ISS Ⅱ, R2-ISS Ⅲ, and R2-ISS Ⅳ was 52, 29, 20, and 15 months ( P<0.001), while the median overall survival (OS) time was 91, 60, 44, and 36 months ( P<0.001). Multifactor analysis revealed that ISS Ⅱ, ISS Ⅲ, del (17p), t (4;14), 1q+, LDH increased, and age >65 years old were independent negative prognostic factors for OS. ISS Ⅱ, ISS Ⅲ, del (17p), t (4;14), 1q+, and LDH were independent negative prognostic factors for PFS. ②The C-index score of R2-ISS was 0.724, higher than that of R-ISS (0.678), indicating high prediction efficiency. ③The median PFS for 1q+-related double-hit in R2-ISS Ⅲ and Ⅳ were 20, 15 months ( P=0.084) and the median OS were 35, 36 months ( P=0.786), respectively. In R2-ISS Ⅲ, there were twenty-seven cases of 1q+-related double-hit, sixty-one cases of 1q+ single abnormality, and sixty-eight cases with no 1q+. The median PFS for the three groups were 20, 18, and 21 months ( P=0.974), while the median OS was 35, 47, and 56 months ( P=0.042), respectively. Adjusting the assignment of 1q+ to 1, the median PFS and OS of different R2-ISS stages differed significantly after regrouping ( P<0.001) . Conclusions:The prognostic stratification value of R2-ISS is higher than R-ISS, particularly in the highly heterogeneous R-ISS Ⅱ population. Adjusting the assignment of the 1q+-related double-hit can improve R2-ISS, which should be validated in future studies with multi-center and expanded cases.

Objective:To establish and validate a gallstones classification method based on deep learning.Methods:A total of 618 gallstones samples were collected from East Hospital Affiliated to Tongji University, and 1 023 high-definition cross-sectional gallstones profile images were captured to construct a cross-sectional gallstones profile image dataset. Based on the traditional eight-category gallstones classification method, a lightweight network model, MobileNet V3, was trained using deep learning and transfer learning methods. The classification performance of MobileNet was evaluated using a confusion matrix with metrics such as accuracy rate, precision rate, F1 score, and recall rate. The MobileNet V3 was improved and further validated using accuracy and loss values.Results:The accuracy rate (94.17%), precision rate (94.03%), F1 score (92.96%) and recall rate (92.99%) of the improved MobileNet V3 model were better than other networks. The improved MobileNet V3 model achieved the highest accuracy rate (94.17%) in gallstones profile classification and was validated by the test set. The confusion matrix showed a weighted average of accuracy rate (92.0%), precision rate (92.6%), and F1 score (92.2%) for each category of gallstones.Conclusions:Based on deep learning, a high-accuracy gallstones classification method is proposed, which provides a new idea for the intelligent identification of gallstones.