A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

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Construction of a Nomogram Model for Predicting Pathologic Complete Response in Breast Cancer Neoadjuvant Chemotherapy Based on the Pan-Immune Inflammation Value by Zhuowan Tian, Yiqing Xi, Mengting Chen, Meishun Hu, Fangfang Chen, Lei Wei, Jingwei Zhang

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Predicting ipsilateral supraclavicular lymph node pathological complete response: nomogram based on the inflammatory markers by Chen Zhou, Chen Zhou, Chen Zhou, Xian Wu, Rongruo Lin, Rongruo Lin, Li Xu, Li Xu, Tao He, Tao He, Jinzhi Yi, Jinzhi Yi, Qing Lv, Qing Lv

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Clinical study of 99mTc-Rituximab combined with dyes double tracing for axillary sentinel lymph node biopsy after neoadjuvant chemotherapy for breast cancer by Minxue Zhuang, Minxue Zhuang, Yidan Lin, HongBin Qiu, HongBin Qiu, Wei Chen, Wei Chen, Ruo Wang, Ruo Wang, HuanHong Zeng, HuanHong Zeng, Mengbo Lin, Mengbo Lin, Hui Zhang, Hui Zhang

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Exploring the role of tumor to background parenchymal ratio of the [18F]FLT PET/CT measures in determining response to neoadjuvant chemotherapy in breast cancer: a multicenter stud... by Alisa Mohebbi, Fatemeh Asli, Saeed Mohammadzadeh, Ali Abbasian Ardakani, Seyed Mohammad Tavangar, Afshin Mohammadi

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