Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening (vol 42, pg 872, 2024)

被引：0
|
作者：
Bulbul, Hande Melike [1 ]
Burakgazi, Gulen [1 ]
Kesimal, Ugur [2 ]
Kaba, Esat [1 ]
机构：
[1] Recep Tayyip Erdogan Univ Training & Res Hosp, Minist Hlth, Dept Radiol, Rize, Turkiye
[2] Ankara Res & Training Hosp, Dept Radiol, Minist Hlth, Ankara, Turkiye
来源：
JAPANESE JOURNAL OF RADIOLOGY | 2025年 / 43卷 / 02期
关键词：
D O I：
10.1007/s11604-024-01715-z
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
引用
收藏
页码：330 / 330
页数：1
相关论文
共 50 条
  • [11] Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors
    Claudio E. von Schacky
    Nikolas J. Wilhelm
    Valerie S. Schäfer
    Yannik Leonhardt
    Matthias Jung
    Pia M. Jungmann
    Maximilian F. Russe
    Sarah C. Foreman
    Felix G. Gassert
    Florian T. Gassert
    Benedikt J. Schwaiger
    Carolin Mogler
    Carolin Knebel
    Ruediger von Eisenhart-Rothe
    Marcus R. Makowski
    Klaus Woertler
    Rainer Burgkart
    Alexandra S. Gersing
    European Radiology, 2022, 32 : 6247 - 6257
  • [12] Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors
    Yin, Ping
    Mao, Ning
    Chen, Hao
    Sun, Chao
    Wang, Sicong
    Liu, Xia
    Hong, Nan
    FRONTIERS IN ONCOLOGY, 2020, 10
  • [13] A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules
    Guerrisi, Antonino
    Seri, Elena
    Dolcetti, Vincenzo
    Miseo, Ludovica
    Elia, Fulvia
    Lo Conte, Gianmarco
    Del Gaudio, Giovanni
    Pacini, Patrizia
    Barbato, Angelo
    David, Emanuele
    Cantisani, Vito
    CANCERS, 2024, 16 (22)
  • [14] Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques
    Chen, Boran
    Chen, Chaoyue
    Wang, Jian
    Teng, Yuen
    Ma, Xuelei
    Xu, Jianguo
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [15] MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors
    Gu, Jinming
    Yu, Qiang
    Li, Quanjiang
    Peng, Juan
    Lv, Fajin
    Gong, Beibei
    Zhang, Xiaodi
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [16] Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation
    Wentland, Andrew L.
    Yamashita, Rikiya
    Kino, Aya
    Pandit, Prachi
    Shen, Luyao
    Jeffrey, R. Brooke
    Rubin, Daniel
    Kamaya, Aya
    ABDOMINAL RADIOLOGY, 2023, 48 (02) : 642 - 648
  • [17] Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation
    Andrew L. Wentland
    Rikiya Yamashita
    Aya Kino
    Prachi Pandit
    Luyao Shen
    R. Brooke Jeffrey
    Daniel Rubin
    Aya Kamaya
    Abdominal Radiology, 2023, 48 : 642 - 648
  • [18] MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions
    Zhao, Yanjie
    Chen, Rong
    Zhang, Ting
    Chen, Chaoyue
    Muhelisa, Muhetaer
    Huang, Jingting
    Xu, Yan
    Ma, Xuelei
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [19] Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors
    Wan, Yuan
    Miao, Lei
    Zhang, HuanHuan
    Wang, YanMei
    Li, Xiao
    Li, Meng
    Zhang, Li
    ACTA RADIOLOGICA, 2024, 65 (11) : 1359 - 1367
  • [20] Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images
    Wang, Yi
    Gao, Jiening
    Yin, Zhaolin
    Wen, Yue
    Sun, Meng
    Han, Ruoling
    FRONTIERS IN ONCOLOGY, 2024, 14
12345