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Keywords Texture analysis of thymic epi...

Texture analysis of thymic epithelial tumor

https://read.qxmd.com/read/38010596/computed-tomography-radiomic-feature-analysis-of-thymic-epithelial-tumors-differentiation-of-thymic-epithelial-tumors-from-thymic-cysts-and-prediction-of-histological-subtypes
#1
JOURNAL ARTICLE
Wenya Zhao, Yoshiyuki Ozawa, Masaki Hara, Katsuhiro Okuda, Akio Hiwatashi
PURPOSE: To investigate the value of computed tomography (CT) radiomic feature analysis for the differential diagnosis between thymic epithelial tumors (TETs) and thymic cysts, and prediction of histological subtypes of TETs. MATERIALS AND METHODS: Twenty-four patients with TETs (13 low-risk and 9 high-risk thymomas, and 2 thymic carcinomas) and 12 with thymic cysts were included in this study. For each lesion, the radiomic features of a volume of interest covering the lesion were extracted from non-contrast enhanced CT images...
November 27, 2023: Japanese Journal of Radiology
https://read.qxmd.com/read/37685677/usefulness-of-three-dimensional-iodine-mapping-quantified-by-dual-energy-ct-for-differentiating-thymic-epithelial-tumors
#2
JOURNAL ARTICLE
Shuhei Doi, Masahiro Yanagawa, Takahiro Matsui, Akinori Hata, Noriko Kikuchi, Yuriko Yoshida, Kazuki Yamagata, Keisuke Ninomiya, Shoji Kido, Noriyuki Tomiyama
Background : Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT and to investigate the association of extracellular volume fraction (ECV) with the fibrosis of thymic carcinoma. Methods : 42 patients with low-risk thymoma ( n = 20), high-risk thymoma ( n = 16), and thymic carcinoma ( n = 6) were scanned by dual-energy CT. 3D iodine density histogram texture analysis was performed for each nodule on iodine density mapping: Seven texture features (max, min, median, average, standard deviation [SD], skewness, and kurtosis) were obtained...
August 28, 2023: Journal of Clinical Medicine
https://read.qxmd.com/read/36944121/predicting-the-risk-of-thymic-tumors-using-texture-analysis-of-contrast-enhanced-chest-computed-tomography
#3
JOURNAL ARTICLE
Wei Guo, Jianfang Liu, Xiaohua Wang, Huishu Yuan
OBJECTIVE: This study aimed to explore the value of contrast-enhanced computed tomography texture features for predicting the risk of malignant thymic epithelial tumor. METHODS: Data of 97 patients with pathologically confirmed thymic epithelial tumors treated at in our hospital from March 2015 to October 2021 were retrospectively analyzed. Based on the World Health Organization classification of thymic epithelial tumors, patients were divided into a high-risk group (types B2, B3, and C; n = 45) and a low-risk group (types A, AB, and B1; n = 52)...
March 9, 2023: Journal of Computer Assisted Tomography
https://read.qxmd.com/read/36877755/radiomics-analysis-of-multiphasic-computed-tomography-images-for-distinguishing-high-risk-thymic-epithelial-tumors-from-low-risk-thymic-epithelial-tumors
#4
JOURNAL ARTICLE
Yuling Liufu, Yanhua Wen, Wensheng Wu, Ruihua Su, Shuya Liu, Jingxu Li, Xiaohuan Pan, Kai Chen, Yubao Guan
OBJECTIVES: The objective of this study is to preoperatively investigate the value of multiphasic contrast-enhanced computed tomography (CT)-based radiomics signatures for distinguishing high-risk thymic epithelial tumors (HTET) from low-risk thymic epithelial tumors (LTET) compared with conventional CT signatures. MATERIALS AND METHODS: Pathologically confirmed 305 thymic epithelial tumors (TETs), including 147 LTET (Type A/AB/B1) and 158 HTET (Type B2/B3/C), were retrospectively analyzed, and were randomly divided into training (n = 214) and validation cohorts (n = 91)...
March 7, 2023: Journal of Computer Assisted Tomography
https://read.qxmd.com/read/35693628/ct-based-radiomics-analysis-for-differentiation-between-thymoma-and-thymic-carcinoma
#5
JOURNAL ARTICLE
Ryosuke Ohira, Masahiro Yanagawa, Yuki Suzuki, Akinori Hata, Tomo Miyata, Noriko Kikuchi, Yuriko Yoshida, Kazuki Yamagata, Shuhei Doi, Keisuke Ninomiya, Noriyuki Tomiyama
Background: The purpose of our study was to differentiate between thymoma and thymic carcinoma using a radiomics analysis based on the computed tomography (CT) image features. Methods: The CT images of 61 patients with thymic epithelial tumors (TETs) who underwent contrast-enhanced CT with slice thickness <1 mm were analyzed. Pathological examination of the surgical specimens revealed thymoma in 45 and thymic carcinoma in 16. Tumor volume and the ratio of major axis to minor axis were calculated using a computer-aided diagnostic software...
May 2022: Journal of Thoracic Disease
https://read.qxmd.com/read/33308325/development-and-validation-of-a-ct-texture-analysis-nomogram-for-preoperatively-differentiating-thymic-epithelial-tumor-histologic-subtypes
#6
JOURNAL ARTICLE
Caiyue Ren, Mingli Li, Yunyan Zhang, Shengjian Zhang
BACKGROUND: Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. METHODS: Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts...
December 11, 2020: Cancer Imaging: the Official Publication of the International Cancer Imaging Society
https://read.qxmd.com/read/30877464/predicting-pathological-subtypes-and-stages-of-thymic-epithelial-tumors-using-dwi-value-of-combining-adc-and-texture-parameters
#7
JOURNAL ARTICLE
Bo Li, Yong-Kang Xin, Gang Xiao, Gang-Feng Li, Shi-Jun Duan, Yu Han, Xiu-Long Feng, Wei-Qiang Yan, Wei-Cheng Rong, Shu-Mei Wang, Yu-Chuan Hu, Guang-Bin Cui
OBJECTIVES: To explore the value of combining apparent diffusion coefficients (ADC) and texture parameters from diffusion-weighted imaging (DWI) in predicting the pathological subtypes and stages of thymic epithelial tumors (TETs). METHODS: Fifty-seven patients with TETs confirmed by pathological analysis were retrospectively enrolled. ADC values and optimal texture feature parameters were compared for differences among low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC) by one-way ANOVA, and between early and advanced stages of TETs were tested using the independent samples t test...
October 2019: European Radiology
https://read.qxmd.com/read/28624025/quantitative-computed-tomography-texture-analysis-for-estimating-histological-subtypes-of-thymic-epithelial-tumors
#8
JOURNAL ARTICLE
Koichiro Yasaka, Hiroyuki Akai, Masanori Nojima, Aya Shinozaki-Ushiku, Masashi Fukayama, Jun Nakajima, Kuni Ohtomo, Shigeru Kiryu
OBJECTIVES: To investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis. MATERIALS AND METHODS: The data of 39 patients (mean age, 58.6±14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter...
July 2017: European Journal of Radiology
https://read.qxmd.com/read/26868139/differentiating-the-grades-of-thymic-epithelial-tumor-malignancy-using-textural-features-of-intratumoral-heterogeneity-via-18-f-fdg-pet-ct
#9
JOURNAL ARTICLE
Hyo Sang Lee, Jungsu S Oh, Young Soo Park, Se Jin Jang, Ik Soo Choi, Jin-Sook Ryu
OBJECTIVE: We aimed to explore the ability of textural heterogeneity indices determined by (18)F-FDG PET/CT for grading the malignancy of thymic epithelial tumors (TETs). METHODS: We retrospectively enrolled 47 patients with pathologically proven TETs who underwent pre-treatment (18)F-FDG PET/CT. TETs were classified by pathological results into three subgroups with increasing grades of malignancy: low-risk thymoma (LRT; WHO classification A, AB and B1), high-risk thymoma (B2 and B3), and thymic carcinoma (TC)...
May 2016: Annals of Nuclear Medicine
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