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Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal

Xinyi Li¹, Yun Zeng¹, Hao Wang¹

¹Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Shanghai, 201199, China.

Journal of Imaging Informatics in Medicine

|June 3, 2026

Summary

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A new multiparametric MRI model integrating radiomics and deep learning accurately predicts extrathyroidal extension in papillary thyroid cancer. This noninvasive tool aids clinical decisions for personalized treatment strategies.

Area of Science:

Radiology
Oncology
Artificial Intelligence

Background:

Papillary thyroid carcinoma (PTC) can extend beyond the thyroid capsule (extathyroidal extension - ETE).
Accurate preoperative prediction of ETE is crucial for surgical planning and patient management.

Purpose of the Study:

To develop and validate a multiparametric MRI (mpMRI)-based model for predicting ETE in PTC.
Integrate habitat-based radiomics, deep transfer learning (DTL), and quantitative parameters into a predictive model.

Main Methods:

Retrospective analysis of 140 PTC patients' mpMRI data.
Development of DTL models (ResNet152) and habitat-based deep learning radiomics (DLR) models.
Feature selection using LASSO and logistic regression to build a nomogram for ETE prediction.

Keywords:

Deep transfer learning Extrathyroidal extension Habitat imaging Papillary thyroid carcinoma Radiomics

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Main Results:

The developed Habitat-DTL nomogram achieved an AUC of 0.963 in the training cohort and 0.884 in the validation cohort.
Protrusion value and ADC-best ratio were identified as key predictors of ETE.
The model showed promising and consistent performance across training and validation cohorts.

Conclusions:

The integrated Habitat-DTL nomogram demonstrates high performance for preoperative ETE prediction in PTC.
This noninvasive tool can assist in clinical decision-making and guide personalized treatment approaches.
The model offers a potential noninvasive method for assessing ETE risk in PTC patients.