Deep Learning-based sbORN Diagnostic Model

September 27, 2024 updated by: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

312

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510000
        • Recruiting
        • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

All populations that meet the eligibility criteria.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Case
Histologically confirmed sbORN that meets the eligibility criteria.
Other: No Intervention: Observational Cohort
No intervention is scheduled for this observational study.
Control
Histologically confirmed NPC recurrence that meets the eligibility criteria.
Other: No Intervention: Observational Cohort
No intervention is scheduled for this observational study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time Frame: Baseline
Baseline
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time Frame: Baseline
Baseline

Secondary Outcome Measures

Outcome Measure
Time Frame
Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time Frame: Baseline
Baseline
Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time Frame: Baseline
Baseline
F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time Frame: Baseline
Baseline
Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time Frame: Baseline
Baseline
Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time Frame: Baseline
Baseline
Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time Frame: Baseline
Baseline
Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time Frame: Baseline
Baseline
F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time Frame: Baseline
Baseline
Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time Frame: Baseline
Baseline
Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time Frame: Baseline
Baseline
Dice similarity coefficient of the MRI contouring between the deep-learning-based multimodal model and the radiologists.
Time Frame: Baseline
Baseline
Average surface distance of the MRI contouring between the deep-learning-based multimodal model and the radiologists.
Time Frame: Baseline
Baseline

Other Outcome Measures

Outcome Measure
Time Frame
The number of white blood cells in the peripheral blood.
Time Frame: Baseline
Baseline
The number of neutrophils in the peripheral blood.
Time Frame: Baseline
Baseline
The number of basophils in the peripheral blood.
Time Frame: Baseline
Baseline
The number of eosinophils in the peripheral blood.
Time Frame: Baseline
Baseline
The number of red blood cells in the peripheral blood.
Time Frame: Baseline
Baseline
The concentration of albumin in the peripheral blood.
Time Frame: Baseline
Baseline
The concentration of total protein in the peripheral blood.
Time Frame: Baseline
Baseline
The history of diabetes mellitus.
Time Frame: Baseline
Baseline
The history of hypertension.
Time Frame: Baseline
Baseline
The copy number of Epstein-Barr Virus (EBV) DNA.
Time Frame: Baseline
Baseline
The titer of EBV VCA IgA.
Time Frame: Baseline
Baseline
The titer of EBV EBNA1 IgA.
Time Frame: Baseline
Baseline
The titer of EBV EA IgA.
Time Frame: Baseline
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

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

Collaborators

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

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 1, 2024

Primary Completion (Estimated)

December 31, 2029

Study Completion (Estimated)

December 31, 2030

Study Registration Dates

First Submitted

May 22, 2024

First Submitted That Met QC Criteria

June 12, 2024

First Posted (Actual)

June 17, 2024

Study Record Updates

Last Update Posted (Actual)

October 1, 2024

Last Update Submitted That Met QC Criteria

September 27, 2024

Last Verified

September 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SYSKY-2024-395-03

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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