Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning

被引：0
作者：
Xia Zhong
Jiaxuan Peng
Zhenyu Shu
Qiaowei Song
Dongxue Li
机构：
[1] The First Clinical Medical College,Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital
[2] Zhejiang Chinese Medical University,undefined
[3] Jinzhou Medical University,undefined
[4] Affiliated People’s Hospital,undefined
[5] Hangzhou Medical College,undefined
来源：
Cancer Imaging | / 23卷
关键词：
Nomogram; Rectal cancer; Machine learning; p53 gene; Magnetic resonance imaging;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [41] Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer
    Huang, Zi Huai
    Chen, Lianghong
    Sun, Yan
    Liu, Qian
    Hu, Pingzhao
    JOURNAL OF TRANSLATIONAL MEDICINE, 2024, 22 (01)
  • [42] Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer
    Zi Huai Huang
    Lianghong Chen
    Yan Sun
    Qian Liu
    Pingzhao Hu
    Journal of Translational Medicine, 22
  • [43] Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study: Imaging Part
    Wang, Chengyan
    Li, Yan
    Lv, Jun
    Jin, Jianhua
    Hu, Xumei
    Kuang, Xutong
    Chen, Weibo
    Wang, He
    PHENOMICS, 2021, 1 (04): : 151 - 170
  • [44] Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms
    Nie, Tingting
    Yuan, Zilong
    He, Yaoyao
    Xu, Haibo
    Guo, Xiaofang
    Liu, Yulin
    TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2024, 23
  • [45] Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer
    Kim, Ka Eun
    Kim, Chan Kyo
    ABDOMINAL RADIOLOGY, 2022, 47 (01) : 352 - 361
  • [46] A magnetic resonance imaging (MRI)-based nomogram for predicting lymph node metastasis in rectal cancer: a node-for-node comparative study of MRI and histopathology
    Liu, Yuan
    Wan, Lijuan
    Peng, Wenjing
    Zou, Shuangmei
    Zheng, Zhaoxu
    Ye, Feng
    Jiang, Jun
    Han, Ouyang
    Zhao, Xinming
    Zhang, Hongmei
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2021, 11 (06) : 2586 - U56
  • [47] Clinical variables and magnetic resonance imaging-based radiomics predict human papillomavirus status of oropharyngeal cancer
    Bos, Paula
    van den Brekel, Michiel W. M.
    Gouw, Zeno A. R.
    Al-Mamgani, Abrahim
    Waktola, Selam
    Aerts, Hugo J. W. L.
    Beets-Tan, Regina G. H.
    Castelijns, Jonas A.
    Jasperse, Bas
    HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK, 2021, 43 (02): : 485 - 495
  • [48] Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study: Imaging Part
    Chengyan Wang
    Yan Li
    Jun Lv
    Jianhua Jin
    Xumei Hu
    Xutong Kuang
    Weibo Chen
    He Wang
    Phenomics, 2021, 1 : 151 - 170
  • [49] Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging
    Han, Tao
    Liu, Xianwang
    Long, Changyou
    Xu, Zhendong
    Geng, Yayuan
    Zhang, Bin
    Deng, Liangna
    Jing, Mengyuan
    Zhou, Junlin
    MAGNETIC RESONANCE IMAGING, 2023, 104 : 16 - 22
  • [50] Machine learning for rectal cancer prediction based on metabolic changes on amino acids
    Tavares, Jose
    Brandao, Pedro
    Barros, Ivo
    Goncalves, Jose
    Praca, Isabel
    Lacerda, Lucia
    Santos, Marisa
    2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS, 2023, : 214 - 217
12345