CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

被引:0
|
作者
Liu, Liangsen [1 ,2 ]
Liao, Hai [1 ]
Zhao, Yang [1 ]
Yin, Jiayu [1 ,3 ]
Wang, Chen [1 ]
Duan, Lixia [1 ]
Xie, Peihan [1 ]
Wei, Wupeng [4 ]
Xu, Meihai [3 ]
Su, Danke [1 ]
机构
[1] Guangxi Med Univ, Canc Hosp, Dept Med Imaging Ctr, Nanning, Peoples R China
[2] Guangxi Med Univ, Dept Nucl Med, Affiliated Hosp 1, Nanning, Peoples R China
[3] Guangxi Med Univ, Dept Radiol, Affiliated Hosp 1, Nanning, Peoples R China
[4] Guangxi Med Univ, Affiliated Hosp 2, Nanning, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2024年 / 14卷
关键词
esophageal cancer; lymph node metastasis; computerized tomography; radiomics; diagnosis; meta-analysis; SQUAMOUS-CELL CARCINOMA; DIAGNOSTIC-TEST ACCURACY; COMPUTED-TOMOGRAPHY; SIZE;
D O I
10.3389/fonc.2024.1267596
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objective: We aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC). Methods: The present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman's correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS). Results: The meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 16 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability. Conclusion: The present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis
    Ma, Dong
    Zhou, Teli
    Chen, Jing
    Chen, Jun
    [J]. BMC MEDICAL IMAGING, 2024, 24 (01):
  • [2] Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer
    Jannatdoust, Payam
    Valizadeh, Parya
    Pahlevan-Fallahy, Mohammad-Taha
    Hassankhani, Amir
    Amoukhteh, Melika
    Behrouzieh, Sadra
    Ghadimi, Delaram J.
    Bilgin, Cem
    Gholamrezanezhad, Ali
    [J]. Clinical Imaging, 2024, 113
  • [3] Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis (vol 24, 144, 2024)
    Ma, Dong
    Zhou, Teli
    Chen, Jing
    Chen, Jun
    [J]. BMC MEDICAL IMAGING, 2024, 24 (01):
  • [4] Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis
    Gong, Xiuru
    Guo, Yaxin
    Zhu, Tingting
    Peng, Xiaolin
    Xing, Dongwei
    Zhang, Minguang
    [J]. FRONTIERS IN ONCOLOGY, 2022, 12
  • [5] Diagnostic performance of CT scan-based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis
    HajiEsmailpoor, Zanyar
    Tabnak, Peyman
    Baradaran, Behzad
    Pashazadeh, Fariba
    Aghebati-Maleki, Leili
    [J]. FRONTIERS IN ONCOLOGY, 2023, 13
  • [6] CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis
    Luo, Xinmin
    Zheng, Renying
    Zhang, Jiao
    He, Juan
    Luo, Wei
    Jiang, Zhi
    Li, Qiang
    [J]. FRONTIERS IN ONCOLOGY, 2024, 14
  • [7] Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis
    HajiEsmailPoor, Zanyar
    Kargar, Zana
    Tabnak, Peyman
    [J]. EUROPEAN JOURNAL OF RADIOLOGY, 2023, 168
  • [8] Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis
    Abbaspour, Elahe
    Karimzadhagh, Sahand
    Monsef, Abbas
    Joukar, Farahnaz
    Mansour-Ghanaei, Fariborz
    Hassanipour, Soheil
    [J]. INTERNATIONAL JOURNAL OF SURGERY, 2024, 110 (06) : 3795 - 3813
  • [9] Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review
    Micciche, Francesco
    Rizzo, Gianluca
    Casa, Calogero
    Leone, Mariavittoria
    Quero, Giuseppe
    Boldrini, Luca
    Bulajic, Milutin
    Corsi, Domenico Cristiano
    Tondolo, Vincenzo
    [J]. FRONTIERS IN MEDICINE, 2023, 10
  • [10] Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis
    Zhang, Sijie
    Liu, Ruijuan
    Wang, Yiyang
    Zhang, Yuewei
    Li, Mengpu
    Wang, Yang
    Wang, Siyu
    Ma, Na
    Ren, Junhong
    [J]. ACADEMIC RADIOLOGY, 2024, 31 (08) : 3118 - 3130