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收藏分享 收藏 分享 收藏 分享 收藏 MRI-based multimodal AI model enables prediction of recurrence risk and adjuvant therapy in breast cancer基于MRI的多模态AI模型能够预测乳腺癌的复发风险和辅助治疗效果。
Yu, Yunfang; Ren, Wei; Mao, Luhui; Ouyang, Wenhao; Hu, Qiugen; Yao, Qinyue; Tan, Yujie; He, Zifan; Ban, Xiaohua; Hu, Huijun; Lin, Ruichong; Wang, Zehua; Chen, Yongjian; Wu, Zhuo; Chen, Kai; Ouyang, Jie; Li, Tang; Zhang, Zebang; Liu, Guoying; Chen, Xiuxing; Li, Zhuo; Wang, Jin; Yao, Herui 分享 收藏 分享 收藏 分享 收藏 Gd(III)-Catalyzed Regio-, Diastereo-, and Enantioselective [4+2] Photocycloaddition of Naphthalene DerivativesLi, Muzi; Huang, Xu-Lun; Zhang, Zuo-Yu; Wang, Zhiping; Wu, Zhuo; Yang, Hui; Shen, Wen-Jie; Cheng, Yuan-Zheng; You, Shu-Li 分享 收藏 分享 收藏 分享 收藏 Development of a radiomics model to discriminate ammonium urate stones from uric acid stones in vivo: A remedy for the diagnostic pitfall of dual-energy computed tomographyZheng, Junjiong; Zhang, Jie; Cai, Jinhua; Yao, Yuhui; Lu, Sihong; Wu, Zhuo; Cai, Zhaoxi; Tuerxun, Aierken; Batur, Jesur; Huang, Jian; Kong, Jianqiu; Lin, Tianxin 分享 收藏 Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort studyYu, Yunfang; Ren, Wei; He, Zifan; Chen, Yongjian; Tan, Yujie; Mao, Luhui; Ouyang, Wenhao; Lu, Nian; Ouyang, Jie; Chen, Kai; Li, Chenchen; Zhang, Rong; Wu, Zhuo; Su, Fengxi; Wang, Zehua; Hu, Qiugen; Xie, Chuanmiao; Yao, Herui 分享 收藏 Clinical guidelines for indications, techniques, and complications of autogenous bone graftingZhang, Jianzheng; Li, Shaoguang; He, Hongying; Han, Li; Zhang, Simeng; Yang, Lin; Han, Wenxing; Wang, Xiaowei; Gao, Jie; Zhao, Jianwen; Shi, Weidong; Wu, Zhuo; Wang, Hao; Zhang, Zhicheng; Zhang, Licheng; Chen, Wei; Zhu, Qingtang; Sun, Tiansheng; Tang, Peifu; Zhang, Yingze 分享 收藏 Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancerLei, Ruilin; Yu, Yunfang; Li, Qingjian; Yao, Qinyue; Wang, Jin; Gao, Ming; Wu, Zhuo; Ren, Wei; Tan, Yujie; Zhang, Bingzhong; Chen, Liliang; Lin, Zhongqiu; Yao, Herui 分享 收藏 分享 收藏 ASO Visual Abstract: A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast CancerLi, Chenchen; Lu, Nian; He, Zifan; Tan, Yujie; Liu, Yajing; Chen, Yongjian; Wu, Zhuo; Liu, Jingwen; Ren, Wei; Mao, Luhui; Yu, Yunfang; Xie, Chuanmiao; Yao, Herui 分享 收藏 分享 收藏 A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast CancerLi, Chenchen; Lu, Nian; He, Zifan; Tan, Yujie; Liu, Yajing; Chen, Yongjian; Wu, Zhuo; Liu, Jingwen; Ren, Wei; Mao, Luhui; Yu, Yunfang; Xie, Chuanmiao; Yao, Herui 分享 收藏 Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer基于dce-mri的深度学习在预测乳腺癌新辅助化疗病理完全缓解方面优于放射组学分析Peng, Yunsong; Cheng, Ziliang; Gong, Chang; Zheng, Chushan; Zhang, Xiang; Wu, Zhuo; Yang, Yaping; Yang, Xiaodong; Zheng, Jian; Shen, Jun 分享 收藏 Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validationKong, Jianqiu; Zheng, Junjiong; Wu, Jieying; Wu, Shaoxu; Cai, Jinhua; Diao, Xiayao; Xie, Weibin; Chen, Xiong; Yu, Hao; Huang, Lifang; Fang, Hongpeng; Fan, Xinxiang; Qin, Haide; Li, Yong; Wu, Zhuo; Huang, Jian; Lin, Tianxin 分享 收藏 
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