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
  • [21] CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors
    Zheng, Yunlin
    Zhou, Di
    Liu, Huan
    Wen, Ming
    EUROPEAN RADIOLOGY, 2022, 32 (10) : 6953 - 6964
  • [22] CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors
    Yunlin Zheng
    Di Zhou
    Huan Liu
    Ming Wen
    European Radiology, 2022, 32 : 6953 - 6964
  • [23] Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach
    Xia, Jianfu
    Chen, Huiling
    Li, Qiang
    Zhou, Minda
    Chen, Limin
    Cai, Zhennao
    Fang, Yang
    Zhou, Hong
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2017, 147 : 37 - 49
  • [24] CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development and Validation Study (vol 223, 10.2214/AJR.24.31077, 2024)
    Lin, Shaofan
    Gao, Ming
    Yang, Zehong
    Yu, Ruihuan
    Dai, Zhuozhi
    Jiang, Chuling
    Yao, Yubin
    Xu, Tingting
    Chen, Jiali
    Huang, Kainan
    Lin, Daiying
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2024, 223 (05)
  • [25] Fracture Conductivity Prediction Based on Machine Learning (vol 9, pg 13469, 2024)
    Wang, Xiaopeng
    Zhang, Binqi
    Du, Jianbo
    Liu, Dongdong
    Zhang, Qilong
    Liu, Xiaoqiang
    ACS OMEGA, 2024, 9 (36): : 38314 - 38314
  • [26] CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors
    Chen, Jia
    Liu, Lei
    He, Ziying
    Su, Danke
    Liu, Chanzhen
    JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 2024, 37 (01): : 180 - 195
  • [27] Incremental value of machine learning-based radiomics for preoperative discrimination between malignant and benign small renal masses
    Wang, Z. J.
    EUROPEAN UROLOGY, 2024, 85 : S1328 - S1328
  • [28] The effectiveness of deep learning model in differentiating benign and malignant pulmonary nodules on spiral CT (vol 32, pg 5129, 2024)
    Liu, Dongquan
    Zhao, Yonggang
    Liu, Bangquan
    TECHNOLOGY AND HEALTH CARE, 2024, 33 (01) : 695 - 695
  • [29] Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics
    Feng, Yanhui
    Feng, Zhaoyan
    Wang, Liang
    Lv, Wenzhi
    Liu, Zhiyong
    Min, Xiangde
    Li, Jin
    Zhang, Jiaxuan
    FRONTIERS IN MEDICINE, 2023, 10
  • [30] Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images
    Du, Dongyang
    Feng, Hui
    Lv, Wenbing
    Ashrafinia, Saeed
    Yuan, Qingyu
    Wang, Quanshi
    Yang, Wei
    Feng, Qianjin
    Chen, Wufan
    Rahmim, Arman
    Lu, Lijun
    MOLECULAR IMAGING AND BIOLOGY, 2020, 22 (03) : 730 - 738
12345