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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.
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页数:15
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