Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological featur...Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.展开更多
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima...Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.展开更多
基金supported by grants from the Youth Cultivation Fund of Beijing Medical Ward Foundation(No.20180502)Beijing Medical Ward Foundation(No.YXJL-2020-0941-0736)。
文摘Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3002803)the National Key Research and Development Program of China(Grant No.2024YFF0808402)the National Natural Science Foundation of China(Grant No.42375169)。
文摘Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.
