Abstract
Objectives
To explore the added value of arterial enhancement fraction (AEF) derived from dual-energy computed tomography CT (DECT) to conventional image features for diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC).
Methods
A total of 273 cervical LNs (153 non-metastatic and 120 metastatic) were recruited from 92 patients with PTC. Qualitative image features of LNs were assessed. Both single-energy CT (SECT)–derived AEF (AEFS) and DECT-derived AEF (AEFD) were calculated. Correlation between AEFD and AEFS was determined using Pearson’s correlation coefficient. Multivariate logistic regression analysis with the forward variable selection method was used to build three models (conventional features, conventional features + AEFS, and conventional features + AEFD). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses.
Results
Abnormal enhancement, calcification, and cystic change were chosen to build model 1 and the model provided moderate diagnostic performance with an area under the ROC curve (AUC) of 0.675. Metastatic LNs demonstrated both significantly higher AEFD (1.14 vs 0.48; p < 0.001) and AEFS (1.08 vs 0.38; p < 0.001) than non-metastatic LNs. AEFD correlated well with AEFS (r = 0.802; p < 0.001), and exhibited comparable performance with AEFS (AUC, 0.867 vs 0.852; p = 0.628). Combining CT image features with AEFS (model 2) and AEFD (model 3) could significantly improve diagnostic performances (AUC, 0.865 vs 0.675; AUC, 0.883 vs 0.675; both p < 0.001).
Conclusions
AEFD correlated well with AEFS, and exhibited comparable performance with AEFS. Integrating qualitative CT image features with both AEFS and AEFD could further improve the ability in diagnosing cervical LN metastasis in PTC.
Clinical relevance statement
Arterial enhancement fraction (AEF) values, especially AEF derived from dual-energy computed tomography, can help to diagnose cervical lymph node metastasis in patients with papillary thyroid cancer, and complement conventional CT image features for improved clinical decision making.
Key Points
• Metastatic cervical lymph nodes (LNs) demonstrated significantly higher arterial enhancement fraction (AEF) derived from dual-energy computed tomography (DECT) and single-energy CT (SECT)–derived AEF (AEFS) than non-metastatic LNs in patients with papillary thyroid cancer.
• DECT-derived AEF (AEFD) correlated significantly with AEFS, and exhibited comparable performance with AEFS.
• Integrating qualitative CT images features with both AEFS and AEFD could further improve the differential ability.
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Abbreviations
- AEF:
-
Arterial enhancement fraction
- AEFD :
-
DECT-derived AEF
- AEFS :
-
SECT-derived AEF
- AUC:
-
Area under the curve
- CI:
-
Confidence interval
- CT:
-
Computed tomography
- CTDIvol:
-
CT dose index
- DECT:
-
Dual-energy CT
- DLP:
-
Dose-length product
- HU:
-
Hounsfield unit
- ICC:
-
Intraclass correlation coefficient
- ID:
-
Iodine density
- IQR:
-
Interquartile range
- LN:
-
Lymph node
- PTC:
-
Papillary thyroid cancer
- ROC:
-
Receiver operating characteristic
- ROI:
-
Region of interest
- SECT:
-
Single-energy CT
- US:
-
Ultrasonography
- VIF:
-
Variance inflation factor
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Acknowledgements
We thank LetPub (http://www.letpub.com) for its linguistic assistance.
Funding
This study received funding by the Natural Science Foundation of China (82171928), Natural Science Foundation of Jiangsu Province (BK20201494), and Basic (Natural) Science Foundation of Education Department of Jiangsu Province (22KJB320005).
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The scientific guarantor of this publication is Fei-Yun Wu.
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Xing-Biao Chen is an employee of Philips Healthcare. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
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Zhou, Y., Xu, YK., Geng, D. et al. Added value of arterial enhancement fraction derived from dual-energy computed tomography for preoperative diagnosis of cervical lymph node metastasis in papillary thyroid cancer: initial results. Eur Radiol 34, 1292–1301 (2024). https://doi.org/10.1007/s00330-023-10109-0
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DOI: https://doi.org/10.1007/s00330-023-10109-0