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Diagnosis of Nasopharyngeal Carcinoma Using a Deep Learning Model Based on Non-Contrast MRI: A Contrast Agent–Free Alternative

Diagnosis of Nasopharyngeal Carcinoma Using a Deep Learning Model Based on Non-Contrast MRI: A Contrast Agent–Free Alternative

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500108798
Enrollment
Unknown
Registered
2025-09-05
Start date
2025-09-05
Completion date
Unknown
Last updated
2025-09-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Nasopharyngeal Carcinoma

Interventions

Training and Validation:Observational study with no interventions. For model development and training.
Standalone Testing:Observational study with no interventions. Also applicable to the multi-reader, multi-case study.
Clinical validation group:Conduct a multi-reader, multi-case (MRMC) clinical validation study, based on an independent test set of 200 cases, by inviting multiple radiologists to participate in a full

Sponsors

Eye and ENT Hospital of Fudan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1. Age >= 18 years; 2. Patients with suspected or newly diagnosed, untreated nasopharyngeal carcinoma (NPC) at the time of consultation, or control individuals diagnosed as non-NPC (including adenoidal hypertrophy, chronic inflammation/hyperplasia, etc.); 3. Availability of a complete head and neck MRI with contrast enhancement (T1WI, T2WI, T1c); 4. Availability of a reference standard: NPC-positive cases confirmed by histopathology, and NPC-negative cases confirmed by negative follow-up (>=6 months) or histopathology showing non-neoplastic lesions.

Exclusion criteria

Exclusion criteria: 1. History of head and neck malignancy, nasopharyngeal surgery, or radiotherapy/chemotherapy; 2. Severe anatomical variations or extensive postoperative changes that interfere with nasopharyngeal assessment; 3. Contraindications to MRI or incomplete key clinical information (e.g., missing age, sex, or diagnostic conclusion); 4. Incomplete MRI sequences, insufficient field of view, or poor image quality that impairs image interpretation; 5. Any other condition deemed unsuitable for inclusion by the investigators.

Design outcomes

Primary

MeasureTime frame
The model achieved the target sensitivity and specificity of 80% in internal testing;In clinical validation, the sensitivity and specificity of T1+T2 combined with AI assistance achieved non-inferiority (non-inferiority margin: –10%);In clinical validation, the AUC of T1+T2 combined with AI assistance achieved non-inferiority (non-inferiority margin: –5%);

Secondary

MeasureTime frame
Derived metrics in internal testing of the model, including AUC, positive predictive value (PPV), and negative predictive value (NPV);Sensitivity, specificity, and AUC of the model under different comparative tests;Sensitivity, specificity, and AUC in clinical validation across different reader types and T-stage case subgroup analyses;

Countries

China

Contacts

Public ContactYu Hongmeng

Eye and ENT Hospital of Fudan University

hongmengyush@163.com+86 21 6437 7134

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026