Rationale and Objectives
This study is based on multicenter cohorts and aims to utilize computed tomography
(CT) images to construct a radiomics nomogram for predicting the lateral neck lymph
node (LNLN) metastasis in the papillary thyroid carcinoma (PTC) and further explore
the biological basis under its prediction.
Materials and Methods
In the multicenter study, 1213 lymph nodes from 409 patients with PTC who underwent
CT examinations and received open surgery and lateral neck dissection were included.
A prospective test cohort was used in validating the model. Radiomics features were
extracted from the CT images of each patient’s LNLNs. Selectkbest, maximum relevance
and minimum redundancy and the least absolute shrinkage and selection operator (LASSO)
algorithm were used in reducing the dimensionality of radiomics features in the training
cohort. Then, a radiomics signature (Rad-score) was calculated as the sum of each
feature multiplied by the nonzero coefficient from LASSO. A nomogram was generated
using the clinical risk factors of the patients and Rad-score. The nomograms’ performance
was analyzed in terms of accuracy, sensitivity, specificity, confusion matrix, receiver
operating characteristic curves, and areas under the receiver operating characteristic
curve (AUCs). The clinical usefulness of the nomogram was evaluated by decision curve
analysis. Moreover, three radiologists with different working experiences and nomogram
were compared to one another. Whole transcriptomics sequencing was performed in 14
tumor samples; the correlation of biological functions and high and low LNLN samples
predicted by the nomogram was further investigated.
Results
A total of 29 radiomics features were used in constructing the Rad-score. Rad-score
and clinical risk factors (age, tumor diameter, location and number of suspected tumors)
compose the nomogram. The nomogram exhibited good discrimination performance of the
nomogram for predicting LNLN metastasis in the training cohort (AUC, 0.866), internal
test cohort (0.845), external test cohort (0.725), and prospective test cohort (0.808)
and showed diagnostic capability comparable to senior radiologists, significantly
outperforming junior radiologists (p < 0.05). Functional enrichment analysis suggested that the nomogram can reflect the
ribosome-related structures of cytoplasmic translation in patients with PTC.
Conclusion
Our radiomics nomogram provides a noninvasive method that incorporates radiomics features
and clinical risk factors for predicting LNLN metastasis in patients with PTC.
Keywords
Abbreviations:
CT (computed tomography), LNLN (lateral neck lymph node), PTC (papillary thyroid carcinoma), LASSO (least absolute shrinkage and selection operator), AUC (areas under the receiver operating characteristic curve), US (ultrasound), ATA (American Thyroid Association), FNAB (fine-needle aspiration biopsy), ROI (region of interest), TSH (thyroid stimulating hormone), VOI (volumes of interest), ICC (intraclass correlation coefficient), MRMR (minimum redundancy maximum relevance), VIF (variance inflation factor), DCA (decision curve analysis), GO (gene ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), CI (confidence interval)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: May 18, 2023
Accepted:
March 27,
2023
Received in revised form:
March 26,
2023
Received:
January 16,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
