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MRI-based Habitat Radiomics for Differentiating T1-Stage Nasopharyngeal Carcinoma From Benign Hyperplasia: A
Hong Zhou1, Lin-Jie Zhou2, Tian-Cheng Lin2
1Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China (H.Z.).
Academic Radiology
|August 6, 2026
Summary
A new habitat-based MRI radiomics framework accurately differentiates early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH). AI-assisted analysis significantly improved diagnostic accuracy and radiologist agreement in this challenging task.
Area of Science:
- Radiology
- Artificial Intelligence
- Oncology
Background:
- Distinguishing T1-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) using MRI is difficult.
- Radiomics offers potential for improved diagnostic accuracy in oncology.
Purpose of the Study:
- To develop and validate a habitat-based MRI radiomics framework for differentiating T1-stage NPC from BH.
- To assess the clinical utility of this framework through a multi-observer study.
Main Methods:
- A retrospective dual-center study included 194 patients (159 internal, 35 external).
- nnU-Net segmented non-contrast T1/T2 MRI; K-means clustered intratumoral habitats.
- Support vector machine classifiers (whole-tumor, habitat, fusion radiomics) were trained and compared.
Main Results:
- Fusion radiomics (FR) achieved the highest AUC (0.939 training, 0.862 internal, 0.863 external).
- Habitat radiomics (HR) outperformed whole-tumor radiomics (WTR).
- AI assistance improved radiologist accuracy (58.5-74.4% to 74.4-84.1%) and inter-observer agreement (κ from 0.083 to 0.329).
Conclusions:
- Habitat-based MRI radiomics offers incremental diagnostic value for T1-stage NPC vs. BH.
- AI-assisted reports enhance diagnostic accuracy and inter-observer agreement.
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