Work

AI‐Powered TAT‐Net Hyperspectral Imaging for Rapid and Accurate Differentiation of Sinonasal Inverted Papilloma and Malignancy

Year: 2026

Type: article

Abstract: Background: Sinonasal inverted papilloma (IP) and malignancies share overlapping clinical and endoscopic features, and conventional histopathology frequently requires time-consuming adjunct immunohistochemistry. Hyperspectral imaging (HSI) can capture subtle spectral changes associated with malignant transformation that are not apparent on routine hematoxylin-eosin (H&E) staining. Methods: In this retrospective single-center study, 176 H&E-stained surgical specimens from 176 patients (89 with IP and 87 with sinonasal malignancies) were allocated at the patient/specimen level to training, validation, and test sets in a 6:2:2 ratio before any hyperspectral data processing. Hyperspectral calibration, selection of regions of interest (ROIs), spectral preprocessing, feature extraction, and quality control yielded 9,296 eligible ROI-level hyperspectral samples: 5,578 in the training set, 1,859 in the validation set, and 1,859 in the test set. Joint spectral-texture features were evaluated using several reference deep-learning models and a Transformer-Attention-based Network (TAT-Net). The primary outcome was the area under the receiver operating characteristic curve (AUC) in the test set. Results: HSI revealed distinct spectral signatures between IP and sinonasal malignancies. All deep learning models demonstrated good discriminative performance (AUCs ≥ 0.85), with TAT-Net achieving the best overall performance. On the independent test set, TAT-Net achieved an AUC of 0.9656 (95% CI: 0.9571-0.9741) for distinguishing between IP and sinonasal malignancies. Conclusion: AI-powered TAT-Net analysis of HSI enables rapid and accurate differentiation between IP and sinonasal malignancies on routine H&E sections. This approach has the potential to shorten diagnostic workflows, support early decision-making, and help standardize the evaluation of sinonasal tumors.

Source: World Journal of Otorhinolaryngology - Head and Neck Surgery

DOI: 10.1002/wjo2.70170

Authors: Hao‐Miao Zhao, Jia‐Chang Kong, Yun‐Ze Li, Wei Li, Xin Feng

Institutions: Shandong University, Qilu Hospital of Shandong University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Shandong University, National Natural Science Foundation of China