Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

被引:2
|
作者
Zheng, Bo [1 ]
Wu, Jiayi [2 ]
Zhao, Zixuan [2 ]
Ou, Xuejin [2 ]
Cao, Peng [3 ]
Ma, Xuelei [1 ]
机构
[1] Sichuan Univ, West China Hosp, Dept Biotherapy, Dept Oncol, Chengdu 610041, Peoples R China
[2] Sichuan Univ, West China Sch Med, Chengdu 610041, Peoples R China
[3] Sichuan Univ, West China Hosp, Dept Oncol, Chengdu 610041, Peoples R China
关键词
FINE-NEEDLE-ASPIRATION; CELL LUNG-CANCER; TEXTURE ANALYSIS; HODGKIN-DISEASE; PROSTATE-CANCER; FDG-PET; CT; HETEROGENEITY; METASTASIS; IMAGES;
D O I
10.1155/2020/3959236
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
Background. The National Comprehensive Cancer Network guidelines recommend excisional biopsies for the diagnosis of lymphomas. However, resection biopsies in all patients who are suspected of having malignant lymph nodes may cause unnecessary injury and increase medical costs. We investigated the usefulness of 18F-fluorodeoxyglucose positron emission/computed tomography- (18F-FDG-PET/CT-) based radiomics analysis for differentiating between lymphomatous lymph nodes (LLNs) and cancerous lymph nodes (CLNs). Methods. Using texture analysis, radiomic parameters from the 18F-FDG-PET/CT images of 492 lymph nodes (373 lymphomatous lymph nodes and 119 cancerous lymph nodes) were extracted with the LIFEx package. Predictive models were generated from the six parameters with the largest area under the receiver operating characteristics curve (AUC) in PET or CT images in the training set (70% of the data), using binary logistic regression. These models were applied to the test set to calculate predictive variables, including the combination of PET and CT predictive variables (PREcombination). The AUC, sensitivity, specificity, and accuracy were used to compare the differentiating ability of the predictive variables. Results. Compared with the pathological diagnosis of the patient's primary tumor, the AUC, sensitivity, specificity, and accuracy of PREcombination in differentiating between LLNs and CLNs were 0.95, 91.67%, 94.29%, and 92.96%, respectively. Moreover, PREcombination could effectively distinguish LLNs caused by various lymphoma subtypes (Hodgkin's lymphoma and non-Hodgkin's lymphoma) from CLNs, with the AUC, sensitivity, specificity, and accuracy being 0.85 and 0.90, 77.78% and 77.14%, 97.22% and 88.89%, and 90.74% and 83.10%, respectively. Conclusions. Radiomics analysis of 18F-FDG-PET/CT images may provide a noninvasive, effective method to distinguish LLN and CLN and inform the choice between fine-needle aspiration and excision biopsy for sampling suspected lymphomatous lymph nodes.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] A systematic review and meta-analysis of 18F-fluorodeoxyglucose positron emission tomography or positron emission tomography/computed tomography for detection of infected prosthetic vascular grafts
    Kim, Seong-Jang
    Lee, Sang-Woo
    Jeong, Shin Young
    Pak, Kyoungjune
    Kim, Keunyoung
    JOURNAL OF VASCULAR SURGERY, 2019, 70 (01) : 307 - 313
  • [32] Effects of Different Liver Diseases on Metabolic Reference in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography
    Ng, K. S.
    Ng, K. K.
    Chu, K. S.
    Kung, B. T.
    Yong, T. K. Au
    HONG KONG JOURNAL OF RADIOLOGY, 2023, 26 (04): : 240 - 247
  • [33] Pacemaker-related infection detected by 18F-fluorodeoxyglucose positron emission tomography-computed tomography
    Peixoto, Giselle de Lima
    Siciliano, Rinaldo Focaccia
    Camargo, Raphael Abegao
    Bueno, Fabiana Lucas
    Soares Junior, Jose
    Costa, Roberto
    Mara, Tania
    Strabelli, Varejao
    Martinelli Filho, Martino
    INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, 2014, 19 : 87 - 90
  • [34] 18F-fluorodeoxyglucose positron emission tomography/computed tomography in diagnosis of post-transplant lymphoproliferative disorder
    Panagiotidis, Emmanouil
    Quigley, Ann-Marie
    Pencharz, Deborah
    Ardeshna, Kirit
    Syed, Rizwan
    Sajjan, Rakesh
    Bomanji, Jamshed
    LEUKEMIA & LYMPHOMA, 2014, 55 (03) : 515 - 519
  • [35] 18F-fluorodeoxyglucose Positron Emisson Tomography/Computed Tomography Guided Conformal Brachytherapy for Cervical Cancer
    Nam, Heerim
    Huh, Seung Jae
    Ju, Sang Gyu
    Park, Won
    Lee, Jeong Eun
    Choi, Joon Young
    Kim, Byung-Tae
    Kim, Chan Kyo
    Park, Byung Kwan
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2012, 84 (01): : E29 - E34
  • [36] The Role of 18F-Fluorodeoxyglucose Positron Emission Tomography in Thyroid Neoplasms
    Lang, Brian Hung-Hin
    Law, Tsz Ting
    ONCOLOGIST, 2011, 16 (04) : 458 - 466
  • [37] Gallbladder lymphoma detected by 18F-fluorodeoxyglucose positron emission tomography
    So, Alvin
    Sheldon, James
    Kua, Hock
    WORLD JOURNAL OF NUCLEAR MEDICINE, 2020, 19 (04) : 428 - 431
  • [38] Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes
    Amrane, Karim
    Le Meur, Coline
    Thuillier, Philippe
    Berthou, Christian
    Uguen, Arnaud
    Deandreis, Desiree
    Bourhis, David
    Bourbonne, Vincent
    Abgral, Ronan
    CANCER IMAGING, 2024, 24 (01)
  • [39] Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment
    Kim, Chu Hyun
    Park, Hyunjin
    Lee, Ho Yun
    Ahn, Joong Hyun
    Lee, Seung Hak
    Sohn, Insuk
    Choi, Joon Young
    Kim, Hong Kwan
    CANCERS, 2020, 12 (12) : 1 - 13
  • [40] Diagnostic value of 18F-fluorodeoxyglucose positron-emission tomography/computed tomography for preoperative lymph node metastasis of esophageal cancer A meta-analysis
    Hu, Jingfeng
    Zhu, Dengyan
    Yang, Yang
    MEDICINE, 2018, 97 (50)