A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma.

Multimodal clinical-radiomic-deep learning model for preoperative classification of pediatric pineal region tumors.

CT-based deep learning for identifying sinonasal inverted papilloma: a single-center retrospective study.

Multi‐Sequence Fusion MRI Deep Learning Models for Discrimination of Recurrent Nasopharyngeal Carcinoma and Osteoradionecrosis

ABSTRACT Objectives To develop a multi‐sequence fusion model based on MRI images to differentiate between recurrent nasopharyngeal carcinoma (rNPC) and osteoradionecrosis (ORN) in patients with nasopharyngeal carcinoma (NPC) after radiotherapy, and to evaluate its diagnostic performance. Methods We retrospectively revi...

Prediction of gross extrathyroidal extension in papillary thyroid carcinoma using a deep learning radiomics model based on post-processing techniques for multi-phase contrast-enhanced MRI images

Gross extrathyroidal extension (ETE) affects surgical management and prognosis in papillary thyroid carcinoma (PTC), yet noninvasive assessment is difficult. In this retrospective exploratory feasibility study, 101 patients with pathologically confirmed PTC who underwent multi-phase contrast-enhanced MRI (MC...

Diagnostic performance of radiomic features from FS-T2WI for parotid tumor classification: A comparison of deep learning and machine learning models.

The FS-T2WI radiomics can reliably and non-invasively distinguish between benign and malignant parotid tumors, while the TabResNet framework offers a practical reference for building high-performance radiomics models in clinical practice.

A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.