2019
DOI: 10.3748/wjg.v25.i16.1986
|View full text |Cite
|
Sign up to set email alerts
|

Dual energy computed tomography for detection of metastatic lymph nodes in patients with hepatocellular carcinoma

Abstract: BACKGROUND Regional lymph node metastasis in patients with hepatocellular carcinoma (HCC) is not uncommon, and is often under- or misdiagnosed. Regional lymph node metastasis is associated with a negative prognosis in patients with HCC, and surgical resection of lymph node metastasis is considered feasible and efficacious in improving the survival and prognosis. It is critical to characterize lymph node preoperatively. There is currently no consensus regarding the optimal method for the assessment… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
17
3

Year Published

2020
2020
2024
2024

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 21 publications
(21 citation statements)
references
References 27 publications
1
17
3
Order By: Relevance
“…Considering that only 15.2% of patients received LND, a great number of patients without LND had occult LNM, indicating that the existing evaluation methods based on radiology are insufficient. Although previous studies have shown some factors that were associated with the presence of LNM, such as PET-CT imaging, dual-energy CT imaging, lncRNAs, microRNAs, and some other hematological indicators, these models were costly and required some technology to generate scores (12,13,15,(37)(38)(39)(40). In the present study, with the help of the LASSO logistic regression algorithm, we incorporated five easily accessible factors, including race, tumor size, clinical T stage (cT stage), extrahepatic bile duct invasion, and tumor grade, to develop a model for predicting LNM in HCC patients.…”
Section: Development and Validation Of A Model To Predict Regional Ly...mentioning
confidence: 99%
“…Considering that only 15.2% of patients received LND, a great number of patients without LND had occult LNM, indicating that the existing evaluation methods based on radiology are insufficient. Although previous studies have shown some factors that were associated with the presence of LNM, such as PET-CT imaging, dual-energy CT imaging, lncRNAs, microRNAs, and some other hematological indicators, these models were costly and required some technology to generate scores (12,13,15,(37)(38)(39)(40). In the present study, with the help of the LASSO logistic regression algorithm, we incorporated five easily accessible factors, including race, tumor size, clinical T stage (cT stage), extrahepatic bile duct invasion, and tumor grade, to develop a model for predicting LNM in HCC patients.…”
Section: Development and Validation Of A Model To Predict Regional Ly...mentioning
confidence: 99%
“…For assessment of the lymph node stations, assessment of node size is not enough; it is crucial to assess texture, local perfusion, and the pattern of perfusion within the nodes. [134][135][136] By visualization and quantification of iodine, 137 spectral images help in the assessment the malignancy of lymph nodes in the abdomen, 138 HCC, 139 H&N, 140-142 gastric 143,59 , pulmonary, 168 colorectal, 144 and rectal cancer. 145,146 Within RT, PET, and MRI currently play an integral role in RT staging of many cancer types.…”
Section: Staging Of Lymph Nodes and Metastasismentioning
confidence: 99%
“…Regional lymph-node metastasis is an important predictor for tumor recurrence and survival in patients with aggressive cancers (Hermanek, 2000 ; Xu et al, 2018 ). The diagnosis of lymphatic metastasis in a certain cancer may be uncertain even after extensive clinical examinations, such as endosonography, magnetic resonance imaging, and computed tomography (Christensen et al, 2006 ; Obinu et al, 2018 ; Zeng et al, 2019 ). Cancer patients examined with ambiguous lymphatic metastasis usually suffer from uncontrolled disease progression and a short overall survival period (Biaoxue et al, 2011 ; Yang et al, 2019a ).…”
Section: Introductionmentioning
confidence: 99%