Radiomics and machine learning model can improve the differentiation between ocular adnexal lymphoma and idiopathic orbital inflammation

被引：0

作者：

Wang Guorong ^{[1
]}

Qu Xiaoxia ^{[1
]}

Guo Jian ^{[1
]}

Luo Yongheng ^{[2
]}

Xian Junfang ^{[1
]}

机构：

[1] Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

[2] Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China

来源：

中华医学杂志英文版 | 2025年 / 138卷 / 02期

关键词：

D O I：

暂无

中图分类号：

R739.7 [眼肿瘤]; R777.5 [眼眶疾病];

学科分类号：

100214 ; 100212 ;

摘要：

<正>

引用

共 50 条

[21] Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm [J].

Deng, Yiwen ;

Zeng, Qiao ;

Zhao, Yu ;

Hu, Zhen ;

Zhan, Changmiao ;

Guo, Liangyun ;

Lai, Binghuang ;

Huang, Zhiping ;

Fu, Zhiyong ;

Zhang, Chunquan .

JOURNAL OF ULTRASOUND IN MEDICINE, 2025, 44 (03) :567-579

[22] Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques [J].

Wu, Zixuan ;

Song, Qiujie ;

Liu, Meiling ;

Hu, Yi ;

Peng, Xin ;

Zhang, Zheyuan ;

Yao, Xiaolei ;

Peng, Qinghua .

SCIENTIFIC REPORTS, 2024, 14 (01)

[23] Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography [J].

Xu, Hanyue ;

Zou, Xiuhe ;

Zhao, Yunuo ;

Zhang, Tao ;

Tang, Youyin ;

Zheng, Aiping ;

Zhou, Xianghong ;

Ma, Xuelei .

TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2021, 20

[24] Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach [J].

Yimit, Yasen ;

Yasin, Parhat ;

Tuersun, Abuduresuli ;

Abulizi, Abudoukeyoumujiang ;

Jia, Wenxiao ;

Wang, Yunling ;

Nijiati, Mayidili .

EUROPEAN JOURNAL OF MEDICAL RESEARCH, 2023, 28 (01)

[25] Multi-modality differentiation between myxofibrosarcoma and myxoma using radiomics and machine learning models [J].

Cui, Can ;

Johnson, Samuel R. ;

Moran, Cullen P. ;

Hajdu, Katherine S. ;

Shechtel, Joanna ;

Block, John J. ;

Bingham, Brian ;

Smith, David ;

Luo, Leo Y. ;

Kang, Hakmook ;

Halpern, Jennifer L. ;

Schwartz, Herbert S. ;

Holt, Ginger E. ;

Lawrenz, Joshua M. ;

Dawant, Benoit M. .

MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS, 2022, 12033

[26] Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach [J].

Yasen Yimit ;

Parhat Yasin ;

Abuduresuli Tuersun ;

Abudoukeyoumujiang Abulizi ;

Wenxiao Jia ;

Yunling Wang ;

Mayidili Nijiati .

European Journal of Medical Research, 28

[27] Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses [J].

Liu, Lu ;

Cai, Wenjun ;

Zheng, Feibo ;

Tian, Hongyan ;

Li, Yanping ;

Wang, Ting ;

Chen, Xiaonan ;

Zhu, Wenjing .

INSIGHTS INTO IMAGING, 2025, 16 (01)

[28] Machine Learning Model with Texture Analysis for Automatic Classification of Histopathological Images of Ocular Adnexal Mucosa-associated Lymphoid Tissue Lymphoma of Two Different Origins [J].

Tagami, Mizuki ;

Nishio, Mizuho ;

Katsuyama-Yoshikawa, Atsuko ;

Misawa, Norihiko ;

Sakai, Atsushi ;

Haruna, Yusuke ;

Azumi, Atsushi ;

Honda, Shigeru .

CURRENT EYE RESEARCH, 2023, 48 (12) :1195-1202

[29] Radiomics in the differentiation between upper tract urothelial carcinoma and renal cell carcinoma - can we overcome the performance of conventional diagnostics by machine learning? [J].

Rodler, S. ;

Rzany, M. ;

Buchner, A. ;

Stief, C. ;

Fabritius, M. ;

Winkelmann, M. ;

Kazmierczak, P. ;

Marcon, J. .

EUROPEAN UROLOGY, 2023, 83

[30] RADIOMICS-BASED MACHINE LEARNING APPROACH IN DIFFERENTIATION BETWEEN VESTIBULAR SCHWANNOMA AND MENINGIOMA IN THE CEREBELLOPONTINE ANGLE [J].

Zhang, Y. ;

Chen, C. ;

Xu, J. .

NEURO-ONCOLOGY, 2021, 23 :26-26

← 1 2 3 4 5 →