Background:This study aimed to evaluate the impact of nipple-sparing mastectomy(NSM)and modified radical mastectomy(MRM)on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy(NST)i...Background:This study aimed to evaluate the impact of nipple-sparing mastectomy(NSM)and modified radical mastectomy(MRM)on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy(NST)in reducing surgical intervention requirements.Methods:To develop treatment recommendations for breast cancer patients,five machine learning models were trained.To mitigate bias in treatment allocation,advanced statistical methods,including propensity score matching(PSM)and inverse probability treatment weighting(IPTW),were applied.Results:NSM demonstrated either superior or noninferior survival outcomes compared with MRM across all breast cancer stages,irrespective of adjustments for IPTW and PSM.Among all models and National Comprehensive Cancer Network guidelines,the Balanced Individual and Mixture Effect(BIME)for survival regression model proposed in this study showed the strongest protective effects in treatment recommendations,as evidenced by an IPTW hazard ratio of 0.39(95%CI:0.26–0.59),an IPTW risk difference of 19.66%(95%CI:18.20–21.13),and an IPTW difference in restricted mean survival time of 17.77 months(95%CI:16.37–19.21).NST independently reduced the probability of surgical intervention by 1.4%(95%CI:0.9%–2.0%),with the greatest impact observed in patients with locally advanced breast cancer,in whom a 4.5%reduction(95%CI:3.8%–5.2%)in surgical selection was noted.展开更多
Background:The role of surgery in metastatic breast cancer(MBC)is currently controversial.Several novel statistical and deep learning(DL)methods promise to infer the suitability of surgery at the individual level.Obje...Background:The role of surgery in metastatic breast cancer(MBC)is currently controversial.Several novel statistical and deep learning(DL)methods promise to infer the suitability of surgery at the individual level.Objective:The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required.Methods:We introduced the deep survival regression with mixture effects(DSME),a semi-parametric DL model integrating three causal inference methods.Six models were trained to make individualized treatment recommendations.Patients who received treatments in line with the DL models'recommendations were compared with those who underwent treatments divergent from the recommendations.Inverse probability weighting(IPW)was used to minimize bias.The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference.Results:In total,5269 female patients with MBC were included.DSME was an independent protective factor,outperforming other models in recommending surgery(IPW-adjusted hazard ratio[HR]=0.39,95%confidence interval[CI]:0.19–0.78)and type of surgery(IPW-adjusted HR=0.66,95%CI:0.48–0.93).DSME was superior to other models and traditional guidelines,suggesting a higher proportion of patients benefiting from surgery,especially breast-conserving surgery.The debiased effect of patient characteristics,including age,tumor size,metastatic sites,lymph node status,and breast cancer subtypes,on surgery decision was also quantified.Conclusions:Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed.This method can facilitate the development of efficient,reliable treatment recommendation systems and provide quantifiable evidence for decision-making.展开更多
The scale-free ferroelectric polarization of fluorite MO_(2)(M=Hf,Zr)due to flat polar phonon bands are promising for nonvolatile memories.Defects are also widely introduced to improve the emergent ferroelectricity.Ho...The scale-free ferroelectric polarization of fluorite MO_(2)(M=Hf,Zr)due to flat polar phonon bands are promising for nonvolatile memories.Defects are also widely introduced to improve the emergent ferroelectricity.However,their roles are still not fully understood at the atomic-level.Here,we report a significant effect of point-defect-driven flattening of polar phonon bands with more polar modes and polarization contribution in doped MO_(2).The polar phonon bands in La-doped MO_(2)(M=Hf,Zr)can be significantly flattened,compared with pure ones.However,the lower energy barrier with larger polarization of VO-only doped MO_(2) compared with La-doped cases suggest that VO and local lattice distortion should be balanced for high-performance fluorite ferroelectricity.The work is believed to bridge the relation between point defects and the generally enhanced induced ferroelectricity in fluorite ferroelectrics at the atomic-level and inspire their further property optimization via defect-engineering.展开更多
基金supported by the Medical Discipline Construction Health Committee Project of Pudong,Shanghai(Grant No.PWYgV2021‐02).
文摘Background:This study aimed to evaluate the impact of nipple-sparing mastectomy(NSM)and modified radical mastectomy(MRM)on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy(NST)in reducing surgical intervention requirements.Methods:To develop treatment recommendations for breast cancer patients,five machine learning models were trained.To mitigate bias in treatment allocation,advanced statistical methods,including propensity score matching(PSM)and inverse probability treatment weighting(IPTW),were applied.Results:NSM demonstrated either superior or noninferior survival outcomes compared with MRM across all breast cancer stages,irrespective of adjustments for IPTW and PSM.Among all models and National Comprehensive Cancer Network guidelines,the Balanced Individual and Mixture Effect(BIME)for survival regression model proposed in this study showed the strongest protective effects in treatment recommendations,as evidenced by an IPTW hazard ratio of 0.39(95%CI:0.26–0.59),an IPTW risk difference of 19.66%(95%CI:18.20–21.13),and an IPTW difference in restricted mean survival time of 17.77 months(95%CI:16.37–19.21).NST independently reduced the probability of surgical intervention by 1.4%(95%CI:0.9%–2.0%),with the greatest impact observed in patients with locally advanced breast cancer,in whom a 4.5%reduction(95%CI:3.8%–5.2%)in surgical selection was noted.
基金Medical Discipline Construction Health Committee of Project of Pudong Shanghai,Grant/Award Number:PWYgV2021-02。
文摘Background:The role of surgery in metastatic breast cancer(MBC)is currently controversial.Several novel statistical and deep learning(DL)methods promise to infer the suitability of surgery at the individual level.Objective:The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required.Methods:We introduced the deep survival regression with mixture effects(DSME),a semi-parametric DL model integrating three causal inference methods.Six models were trained to make individualized treatment recommendations.Patients who received treatments in line with the DL models'recommendations were compared with those who underwent treatments divergent from the recommendations.Inverse probability weighting(IPW)was used to minimize bias.The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference.Results:In total,5269 female patients with MBC were included.DSME was an independent protective factor,outperforming other models in recommending surgery(IPW-adjusted hazard ratio[HR]=0.39,95%confidence interval[CI]:0.19–0.78)and type of surgery(IPW-adjusted HR=0.66,95%CI:0.48–0.93).DSME was superior to other models and traditional guidelines,suggesting a higher proportion of patients benefiting from surgery,especially breast-conserving surgery.The debiased effect of patient characteristics,including age,tumor size,metastatic sites,lymph node status,and breast cancer subtypes,on surgery decision was also quantified.Conclusions:Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed.This method can facilitate the development of efficient,reliable treatment recommendation systems and provide quantifiable evidence for decision-making.
基金W.D.acknowledges the National Key Research and Development Plan(2021YFA1202100)the Nature Science Foundation of Hubei province(20223564/2022CFB595)and China(52202134)+3 种基金2021 Independent Innovation Fund-New Teacher Research Starting Fund of Huazhong University of Science and Technology(5003182109)the Innovation Fund of WNLO,2022 Shenzhen Central Leading Local Science and Technology Development Special Funding Program Virtual University Park Laboratory ProjectQ.F.acknowledges the Natural Science Founda-tion of China(61971459)Shenzhen Technology Plan(JCYJ20190809095009521).
文摘The scale-free ferroelectric polarization of fluorite MO_(2)(M=Hf,Zr)due to flat polar phonon bands are promising for nonvolatile memories.Defects are also widely introduced to improve the emergent ferroelectricity.However,their roles are still not fully understood at the atomic-level.Here,we report a significant effect of point-defect-driven flattening of polar phonon bands with more polar modes and polarization contribution in doped MO_(2).The polar phonon bands in La-doped MO_(2)(M=Hf,Zr)can be significantly flattened,compared with pure ones.However,the lower energy barrier with larger polarization of VO-only doped MO_(2) compared with La-doped cases suggest that VO and local lattice distortion should be balanced for high-performance fluorite ferroelectricity.The work is believed to bridge the relation between point defects and the generally enhanced induced ferroelectricity in fluorite ferroelectrics at the atomic-level and inspire their further property optimization via defect-engineering.
