For: | Li M, Jin YM, Zhang YC, Zhao YL, Huang CC, Liu SM, Song B. Radiomics for predicting perineural invasion status in rectal cancer. World J Gastroenterol 2021; 27(33): 5610-5621 [PMID: 34588755 DOI: 10.3748/wjg.v27.i33.5610] |
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URL: | https://www.wjgnet.com/1007-9327/full/v27/i33/5610.htm |
Number | Citing Articles |
1 |
Jin Wang, Xiang Zhu, Jian Zeng, Cheng Liu, Wei Shen, Xiaojiang Sun, Qingren Lin, Jun Fang, Qixun Chen, Yongling Ji. Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation. European Radiology 2023; 33(12): 8554 doi: 10.1007/s00330-023-09884-7
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2 |
Yajiao Gan, Qiping Hu, Qingling Shen, Peng Lin, Qingfu Qian, Minling Zhuo, Ensheng Xue, Zhikui Chen. Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging. Annals of Surgical Oncology 2024; doi: 10.1245/s10434-024-16697-5
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3 |
Wen-xi Liu, Hong Wu, Chi Cai, Qing-quan Lai, Yi Wang, Yuan-zhe Li. Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging. Journal of Orthopaedic Surgery and Research 2024; 19(1) doi: 10.1186/s13018-024-04569-3
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4 |
Yan Liu, Bai-Jin-Tao Sun, Chuan Zhang, Bing Li, Xiao-Xuan Yu, Yong Du. Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study. World Journal of Gastroenterology 2024; 30(16): 2233-2248 doi: 10.3748/wjg.v30.i16.2233
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Caixia Zhang, Jianyou Chen, Yifan Liu, Yinrui Yang, Yongzhou Xu, Ruimin You, Yanli Li, Lizhu Liu, Ling Yang, Huaxiu Li, Guanshun Wang, Wenliang Li, Zhenhui Li. Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study. European Radiology 2024; 35(2): 968 doi: 10.1007/s00330-024-11000-2
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6 |
Yumeng Zhang, Huaqing Tan, Bin Huang, Xinjian Guo, Yuntai Cao. Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. Abdominal Radiology 2024; doi: 10.1007/s00261-024-04556-6
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7 |
Zhi-Chun Zhao, Jia-Xuan Liu, Ling-Ling Sun. Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study. Artificial Intelligence in Medical Imaging 2024; 5(1): 93993 doi: 10.35711/aimi.v5.i1.93993
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8 |
Siyuan Qin, Siyi Lu, Ke Liu, Yan Zhou, Qizheng Wang, Yongye Chen, Enlong Zhang, Hao Wang, Ning Lang. Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy. Diagnostics 2023; 13(12): 1987 doi: 10.3390/diagnostics13121987
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9 |
Lei Gao, Xiuli Wen, Guanghui Yue, Hui Wang, Ziqing Lu, Beibei Wu, Zhihong Liu, Yuming Wu, Dongmei Lin, Shijian Yi, Wei Jiang, Yi Hao. The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma. Ultrasonic Imaging 2025; 47(2): 93 doi: 10.1177/01617346251313982
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10 |
Jacobo Porto-Álvarez, Gary T. Barnes, Alex Villanueva, Roberto García-Figueiras, Sandra Baleato-González, Emilio Huelga Zapico, Miguel Souto-Bayarri. Digital Medical X-ray Imaging, CAD in Lung Cancer and Radiomics in Colorectal Cancer: Past, Present and Future. Applied Sciences 2023; 13(4): 2218 doi: 10.3390/app13042218
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11 |
Wenzheng Lu, Xiaoying Tan, Yanqi Zhong, Peng Wang, Yuxi Ge, Heng Zhang, Shudong Hu. Spectral CT in the evaluation of perineural invasion status in rectal cancer. Japanese Journal of Radiology 2024; 42(9): 1012 doi: 10.1007/s11604-024-01575-7
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12 |
Jiaxuan Liu, Lingling Sun, Xiang Zhao, Xi Lu. Development and validation of a combined nomogram for predicting perineural invasion status in rectal cancer via computed tomography-based radiomics. Journal of Cancer Research and Therapeutics 2023; 19(6): 1552 doi: 10.4103/jcrt.jcrt_2633_22
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13 |
Peng-Chao Zhan, Pei-jie Lyu, Zhen Li, Xing Liu, Hui-Xia Wang, Na-Na Liu, Yuyuan Zhang, Wenpeng Huang, Yan Chen, Jian-bo Gao. CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.900478
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14 |
Margherita Mottola, Rita Golfieri, Alessandro Bevilacqua. The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging. Sensors 2024; 24(4): 1156 doi: 10.3390/s24041156
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15 |
Wenzheng Lu, Yanqi Zhong, Xifeng Yang, Yuxi Ge, Heng Zhang, Xingbiao Chen, Shudong Hu. Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast-Enhanced CT Imaging. Journal of Imaging Informatics in Medicine 2024; doi: 10.1007/s10278-024-01325-1
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16 |
Mo Zheng, Shouliang Miao, Dan Chen, Fei Yao, Qinqin Xiao, Guanxia Zhu, Chenqiang Pan, Tao Lei, Chenhao Ye, Yunjun Yang, Lusi Ye. Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis?. Clinical Rheumatology 2023; 42(6): 1675 doi: 10.1007/s10067-023-06543-6
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17 |
Joao Miranda, Natally Horvat, Jose A. B. Araujo-Filho, Kamila S. Albuquerque, Charlotte Charbel, Bruno M. C. Trindade, Daniel L. Cardoso, Lucas de Padua Gomes de Farias, Jayasree Chakraborty, Cesar Higa Nomura. The Role of Radiomics in Rectal Cancer. Journal of Gastrointestinal Cancer 2023; 54(4): 1158 doi: 10.1007/s12029-022-00909-w
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18 |
Ning Tang, Shicen Pan, Qirong Zhang, Jian Zhou, Zhiwei Zuo, Rui Jiang, Jinping Sheng. Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis. Abdominal Radiology 2025; doi: 10.1007/s00261-024-04713-x
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19 |
Azadeh Tabari, Shin Mei Chan, Omar Mustafa Fathy Omar, Shams I. Iqbal, Michael S. Gee, Dania Daye. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers 2022; 15(1): 63 doi: 10.3390/cancers15010063
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20 |
Wei-Qin Huang, Ruo-Xuan Lin, Xiao-Hui Ke, Xiao-Hong Deng, Shi-Xiong Ni, Lina Tang. Radiomics in rectal cancer: current status of use and advances in research. Frontiers in Oncology 2025; 14 doi: 10.3389/fonc.2024.1470824
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21 |
Qiaoling Chen, Yanfen Cui, Ting Xue, Hui Peng, Manman Li, Xinghua Zhu, Shaofeng Duan, Hongmei Gu, Feng Feng. Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study. Abdominal Radiology 2022; 47(9): 3251 doi: 10.1007/s00261-022-03620-3
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22 |
Bo Deng, Qian Wang, Yuanqing Liu, Yanwei Yang, Xiaolong Gao, Hui Dai. A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer. Abdominal Radiology 2024; 49(6): 1850 doi: 10.1007/s00261-023-04164-w
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23 |
Lifang Fan, Huazhang Wu, Yimin Wu, Shujian Wu, Jinsong Zhao, Xiangming Zhu. Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3. BMC Gastroenterology 2024; 24(1) doi: 10.1186/s12876-024-03316-6
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24 |
Xiu-chun Ren, Pan Liang. Analysis of influencing factors of nerve invasion in locally advanced gastric cancer. Abdominal Radiology 2023; 48(9): 3005 doi: 10.1007/s00261-023-03970-6
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25 |
Nian-jun Liu, Mao-sen Liu, Wei Tian, Ya-nan Zhai, Wei-long Lv, Tong Wang, Shun-Lin Guo. The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study. Insights into Imaging 2024; 15(1) doi: 10.1186/s13244-024-01664-1
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26 |
Xuewu Liu, Feng Lin, Danni Li, Nan Lei. The accuracy of radiomics in diagnosing tumor deposits and perineural invasion in rectal cancer: a systematic review and meta-analysis. Frontiers in Oncology 2025; 14 doi: 10.3389/fonc.2024.1425665
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