Multi-region nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma using multimodal imaging: A multicenter study

Shidi Miao, Qifan Xuan, Wenjuan Huang, Yuyang Jiang, Mengzhuo Sun, Hongzhuo Qi, Ao Li, Zengyao Liu, Jing Li, Xuemei Ding, Ruitao Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Central lymph node metastasis (CLNM) is associated with high recurrence rate and low survival in patients with papillary thyroid carcinoma (PTC). However, there is no satisfactory model to predict CLNM in PTC. This study aimed to integrate PTC deep learning feature based on ultrasound (US) images, fat radiomics features based on computed tomography (CT) images and clinical characteristics to construct a multimodal and multi-region nomogram (MMRN) for predicting the CLNM in PTC. We enrolled 661 patients diagnosed with PTC by thyroidectomy from two independent centers. Patients were divided into the primary cohort, internal test cohort (ITC), and external test cohort (ETC), and collected their US images and CT images. Resnet50 was employed to predict the CLNM status of PTC based on US images. Using radiomics feature extraction methods to extract fat radiomics features from CT images. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression. The predictive performance of the MMRN was evaluated using five-fold cross-validation. We comprehensively evaluated the DLRCN and compared it with five radiologists. In the ITC and ETC, the area under the curves (AUCs) of MMRN were 0.829 (95 % CI: 0.822, 0.835) and 0.818 (95 % CI: 0.808, 0.828). The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (P > 0.05). Decision curve analysis showed that the MMRN was clinically useful. Under equal specificity or sensitivity, the performance of MMRN increased by 6.5 % or 2.9 % compared to radiologist assessments. The incorporation of fat radiomics features led to significant net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (NRI=0.174, P < 0.05, IDI=0.035, P < 0.05). The MMRN demonstrated good performance in predicting the CLNM status of PTC, which was comparable to radiologist assessments. The fat radiomics features exhibited supplementary value for predicting CLNM in PTC. [Abstract copyright: Copyright © 2025. Published by Elsevier B.V.]
Original languageEnglish
Article number108608
Pages (from-to)1-12
Number of pages12
JournalComputer Methods and Programs in Biomedicine
Volume261
Early online date16 Jan 2025
DOIs
Publication statusPublished online - 16 Jan 2025

Bibliographical note

Publisher Copyright:
© 2025

Keywords

  • Central lymph node metastasis
  • Deep learning
  • Multi-region
  • Multimodal
  • Nomogram
  • Papillary thyroid carcinoma
  • Radiomics

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