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

doi:10.1016/j.cmpb.2025.108608

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

Comput Methods Programs Biomed. 2025 Jan 16:261:108608. doi: 10.1016/j.cmpb.2025.108608. Online ahead of print.

Authors

Shidi Miao¹, Qifan Xuan¹, Wenjuan Huang², Yuyang Jiang¹, Mengzhuo Sun¹, Hongzhuo Qi¹, Ao Li¹, Zengyao Liu³, Jing Li⁴, Xuemei Ding⁵, Ruitao Wang⁶

Affiliations

¹ School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
² Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, NO.150 Haping ST, Nangang District, Harbin 150081, China.
³ Department of Interventional Medicine, The First Affiliated Hospital, Harbin Medical University, Harbin, China.
⁴ Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.
⁵ School of Computing, Engineering & Intelligent Systems, Ulster University, Northern Ireland, BT48 7JL, United Kingdom.
⁶ Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, NO.150 Haping ST, Nangang District, Harbin 150081, China. Electronic address: ruitaowang@126.com.

PMID: 39827707
DOI: 10.1016/j.cmpb.2025.108608

Abstract

Background and objective: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Keywords: Central lymph node metastasis; Deep learning; Multi-region; Multimodal; Nomogram; Papillary thyroid carcinoma; Radiomics.