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Abstract #5101

Multimodal MRI deep learning for predicting central lymph node metastasis in papillary thyroid cancer

Xiuyu Wang1, Heng Zhang2, Shudong Hu2, and Pu-Yeh Wu3
1Affiliated Hospital of Jiangnan University, Nanjing, China, 2Affiliated hospital of Jiangnan University, Wuxi, China, 3GE Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software

Motivation: In papillary thyroid cancer (PTC), accurately predicting central lymph node metastasis (CLNM) remains challenging, often leading to unnecessary central lymph node dissection (CLND).

Goal(s): Our goal was to develop a deep learning (DL) model using multimodal MRI to predict CLNM in PTC patients and compare it with classical machine learning (ML) models.

Approach: MRI images from 105 PTC patients were obtained. The DL model was trained on 80% of the data and tested on the remainder.

Results: The DL-Fusion model achieved an AUC of 0.891, outperforming all other ML models.

Impact: The high predictive accuracy of the DL-Fusion model can enhance clinical decision-making, allowing for more precise assessment of CLNM and reducing the frequency of unnecessary CLND in PTC patients.

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