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NCT Number: NCT06463392

Deep Learning-based sbORN Diagnostic Model

Skull-base osteonecrosis (sbORN) is a severe long-term complication of nasopharyngeal carcinoma (NPC) post radiotherapy, which significantly diminish the quality of life, increase the risk of internal carotid artery rupture, and is frequently misdiagnosed as NPC recurrence. Novel diagnostic tools are therefore clinically significant. In this study, the investigators seek to ask if a deep-learning-based model shows a significantly higher sensitivity than radiologists. With a cross-sectional design, the investigators aim to recruit 312 participants in Sun Yat-sen Memorial Hospital, Guangzhou, China that meet the eligibility criteria.

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Key information

Conditions

Nasopharyngeal Carcinoma Carcinoma DNA Virus Infections Epstein-Barr Virus Infections Head and Neck Neoplasms Herpesviridae Infections Herpesvirus 4, Human Infections Nasopharyngeal Diseases Nasopharyngeal Neoplasms Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Otorhinolaryngologic Diseases Otorhinolaryngologic Neoplasms Pharyngeal Diseases Pharyngeal Neoplasms Stomatognathic Diseases Tumor Virus Infections Virus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Guangzhou, Guangdong, 510000, China

Location status: Recruiting

Location contact

Xiang-Wei Kong, Ph.D.

CONTACT

[email protected]

0086-020-34071439

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Equal to or older than 18 years old.
  • A history of histologically confirmed nonkeratinizing undifferentiated nasopharyngeal carcinoma.
  • A history of radical radiotherapy at nasopharynx.
  • Complete remission six months post radical radiotherapy according to RECIST 1.1.
  • No evidence of distant metastasis upon recruitment.
  • Diagnosis of sbORN given by senior radiologist with 2-4 Likert scores.
  • Consent to biopsy awake or under general anesthesia.
  • Consent to perform blood tests, EBV DNA, EBV IgAs, and MRI inspection of nasopharynx and neck.
  • With a written consent.

Exclusion criteria

  • MRI artifacts or other factors that interfere radiological diagnosis and region of interest contouring.
  • Suspected lesion is not confined to nasopharynx and skull-base.

Treatment and study plan

No Intervention: Observational Cohort

Other

No intervention is scheduled for this observational study.

Primary outcomes

  1. Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.

    Time frame: Baseline

  2. Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.

    Time frame: Baseline

Secondary outcomes

  1. Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.

    Time frame: Baseline

  2. Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.

    Time frame: Baseline

  3. F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.

    Time frame: Baseline

  4. Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.

    Time frame: Baseline

  5. Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.

    Time frame: Baseline

  6. Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.

    Time frame: Baseline

  7. Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.

    Time frame: Baseline

  8. F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.

    Time frame: Baseline

  9. Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.

    Time frame: Baseline

  10. Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.

    Time frame: Baseline

  11. Dice similarity coefficient of the MRI contouring between the deep-learning-based multimodal model and the radiologists.

    Time frame: Baseline

  12. Average surface distance of the MRI contouring between the deep-learning-based multimodal model and the radiologists.

    Time frame: Baseline

Other outcomes

  1. The number of white blood cells in the peripheral blood.

    Time frame: Baseline

  2. The number of neutrophils in the peripheral blood.

    Time frame: Baseline

  3. The number of basophils in the peripheral blood.

    Time frame: Baseline

  4. The number of eosinophils in the peripheral blood.

    Time frame: Baseline

  5. The number of red blood cells in the peripheral blood.

    Time frame: Baseline

  6. The concentration of albumin in the peripheral blood.

    Time frame: Baseline

  7. The concentration of total protein in the peripheral blood.

    Time frame: Baseline

  8. The history of diabetes mellitus.

    Time frame: Baseline

  9. The history of hypertension.

    Time frame: Baseline

  10. The copy number of Epstein-Barr Virus (EBV) DNA.

    Time frame: Baseline

  11. The titer of EBV VCA IgA.

    Time frame: Baseline

  12. The titer of EBV EBNA1 IgA.

    Time frame: Baseline

  13. The titer of EBV EA IgA.

    Time frame: Baseline

Study contacts

Contact information is provided by the study sponsor or research team.

Xiang-Wei Kong, Ph.D.

CONTACT

[email protected]

0086-020-34071439

Sponsors and collaborators

Lead sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Other

Collaborators

  • Sun Yat-sen University
  • Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China

Registry information

Official study title

Development of Deep-Learning-Based Multimodal Post Radiotherapy Skull-Base Osteonecrosis and Recurrence of Nasopharyngeal Carcinoma Differential Diagnostic Model

Important dates

Study start
2024
Primary completion
2029
Study completion
2030
First posted
Jun 17, 2024
Registry last updated
Oct 1, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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