Quantitative Histogram Analysis of T2-Weighted and Diffusion-Weighted Magnetic Resonance Images for Prediction of Malignant Thymic Epithelial Tumors

被引:4
|
作者
Morikawa, Kazuhiko [1 ]
Igarashi, Takao [1 ]
Shiraishi, Megumi [1 ]
Kano, Rui [1 ]
Misumi, Shigeki [1 ]
Ojiri, Hiroya [1 ]
Asano, Hisatoshi [2 ]
机构
[1] Jikei Univ, Dept Radiol, Sch Med, Tokyo, Japan
[2] Jikei Univ, Dept Surg, Sch Med, Tokyo, Japan
关键词
thymic epithelial tumors; T2-weighted magnetic resonance images; apparent diffusion coefficient; quantitative histogram analysis; WORLD-HEALTH-ORGANIZATION; HISTOLOGIC CLASSIFICATION; TEXTURE ANALYSIS; STAGING SYSTEM; THYMOMAS; CT; FEATURES; SUBTYPES;
D O I
10.1097/RCT.0000000000001197
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
Purpose To assess the value of histogram analysis for differentiating a high-risk thymic epithelial tumor (TET) from a low-risk TET using T2-weighted images and the apparent diffusion coefficient (ADC). Methods Forty-nine patients with histopathologically proven TET after thymectomy were enrolled in this study and retrospectively classified as having low-risk TET (low-risk thymoma) or high-risk TET (high-risk thymoma or thymic carcinoma). Twelve parameters were obtained from the quantitative histogram analysis. The histogram parameters were compared using the Mann-Whitney U test. Diagnostic efficacy was estimated by receiver-operating characteristic curve analysis. Results Twenty-five patients were classified as having low-risk TET and 24 as having high-risk TET. The mean ADC value showed diagnostic efficacy for differentiating high-risk TET from low-risk TET, with an area under the curve of 0.7, and was better than when using conventional methods alone. Conclusion The ADC-based histogram analysis could help to differentiate between high-risk and low-risk TETs.
引用
收藏
页码:795 / 801
页数:7
相关论文
共 50 条
  • [1] Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images
    Mao, Yanyan
    Chen, Chao
    Wang, Zhenjie
    Cheng, Dapeng
    You, Panlu
    Huang, Xingdan
    Zhang, Baosheng
    Zhao, Feng
    FRONTIERS IN NEUROSCIENCE, 2022, 16
  • [2] Hepatocellular Carcinoma Histological Grade Prediction: A Quantitative Comparison of Diffusion-Weighted, T2-Weighted, and Hepatobiliary-Phase Magnetic Resonance Imaging
    Iwasa, Yoshihiro
    Kitazume, Yoshio
    Tateishi, Ukihide
    Saida, Yukihisa
    Ban, Daisuke
    Tanabe, Minoru
    Takemoto, Akira
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2016, 40 (03) : 463 - 470
  • [3] T2-weighted combined with diffusion-weighted images for evaluating prostatic transition zone tumors at 3 Tesla
    Ren, Jing
    Yang, Yong
    Zhang, Jinsong
    Xu, Jian
    Liu, Ying
    Wei, Mengqi
    Ge, Yali
    Huan, Yi
    Larson, Andrew C.
    Zhang, Zhuoli
    FUTURE ONCOLOGY, 2013, 9 (04) : 585 - 593
  • [4] Hyperintense Uterine Myometrial Masses on T2-Weighted Magnetic Resonance Imaging: Differentiation With Diffusion-Weighted Magnetic Resonance Imaging
    Takeuchi, Mayumi
    Matsuzaki, Kenji
    Nishitani, Hiromu
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2009, 33 (06) : 834 - 837
  • [5] Infrarenal high intra-abdominal testis: fusion of T2-weighted and diffusion-weighted magnetic resonance images and pathological findings
    Hoshi, Seiji
    Sato, Yuichi
    Hata, Junya
    Akaihata, Hidenori
    Ogawa, Soichiro
    Haga, Nobuhiro
    Kojima, Yoshiyuki
    BMC UROLOGY, 2017, 17
  • [6] Infrarenal high intra-abdominal testis: fusion of T2-weighted and diffusion-weighted magnetic resonance images and pathological findings
    Seiji Hoshi
    Yuichi Sato
    Junya Hata
    Hidenori Akaihata
    Soichiro Ogawa
    Nobuhiro Haga
    Yoshiyuki Kojima
    BMC Urology, 17
  • [7] Analysis of Diffusion-Weighted and T2-Weighted Imaging in the Prediction of Distinct Granulation Patterns of Somatotroph Adenomas
    Tang, Yifan
    Xie, Tao
    Guo, Yinglong
    Liu, Shuang
    Li, Chen
    Liu, Tengfei
    Zhao, Puyuan
    Yang, Liangliang
    Li, Zeyang
    Yang, Hantao
    Zhang, Xiaobiao
    WORLD NEUROSURGERY, 2024, 182 : E334 - E343
  • [8] An automatic tumor segmentation framework of cervical cancer in T2-weighted and diffusion weighted magnetic resonance images
    Kao, Yueying
    Li, Wu
    Xue, Huadan
    Ren, Cui
    Tian, Jie
    MEDICAL IMAGING 2013: IMAGE PROCESSING, 2013, 8669
  • [9] Radiomics-Based Prediction of Malignant Glioma Grades Using T2-Weighted Magnetic Resonance Images
    Nakamoto, T.
    Takahashi, W.
    Haga, A.
    Takahashi, S.
    Nawa, K.
    Ohta, T.
    Ozaki, S.
    Tanaka, S.
    Mukasa, A.
    Nakagawa, K.
    MEDICAL PHYSICS, 2018, 45 (06) : E183 - E184
  • [10] T2-WEIGHTED AND DIFFUSION-WEIGHTED MAGNETIC-RESONANCE-IMAGING OF A FOCAL ISCHEMIC LESION IN RAT-BRAIN
    VANBRUGGEN, N
    CULLEN, BM
    KING, MD
    DORAN, M
    WILLIAMS, SR
    GADIAN, DG
    CREMER, JE
    STROKE, 1992, 23 (04) : 576 - 582