Clinical trial NCT06463392: Deep Learning-based sbORN Diagnostic Model
Official title: Development of Deep-Learning-Based Multimodal Post Radiotherapy Skull-Base Osteonecrosis and Recurrence of Nasopharyngeal Carcinoma Differential Diagnostic Model
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Nasopharyngeal Carcinoma
Herpesvirus 4, Human
Keywords: Nasopharyngeal Carcinoma
Skull-base Osteonecrosis
Radiotherapy
Epstein-Barr Virus
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
Collaborators: name: Sun Yat-sen University
name: Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
First posted: 2024-06-17
Enrollment: 312
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All populations that meet the eligibility criteria.

Brief summary: 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
timeFrame: Baseline
measure: Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
timeFrame: Baseline
Secondary outcomes:
measure: Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
timeFrame: Baseline
measure: Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
timeFrame: Baseline
measure: F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
timeFrame: Baseline
measure: Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
timeFrame: Baseline
measure: Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
timeFrame: Baseline
measure: Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
timeFrame: Baseline
measure: Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
timeFrame: Baseline
measure: F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
timeFrame: Baseline
measure: Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
timeFrame: Baseline
measure: Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
timeFrame: Baseline
measure: Dice similarity coefficient of the MRI contouring between the deep-learning-based multimodal model and the radiologists.
timeFrame: Baseline
measure: Average surface distance of the MRI contouring between the deep-learning-based multimodal model and the radiologists.
timeFrame: Baseline
Other outcomes:
measure: The number of white blood cells in the peripheral blood.
timeFrame: Baseline
measure: The number of neutrophils in the peripheral blood.
timeFrame: Baseline
measure: The number of basophils in the peripheral blood.
timeFrame: Baseline
measure: The number of eosinophils in the peripheral blood.
timeFrame: Baseline
measure: The number of red blood cells in the peripheral blood.
timeFrame: Baseline
measure: The concentration of albumin in the peripheral blood.
timeFrame: Baseline
measure: The concentration of total protein in the peripheral blood.
timeFrame: Baseline
measure: The history of diabetes mellitus.
timeFrame: Baseline
measure: The history of hypertension.
timeFrame: Baseline
measure: The copy number of Epstein-Barr Virus (EBV) DNA.
timeFrame: Baseline
measure: The titer of EBV VCA IgA.
timeFrame: Baseline
measure: The titer of EBV EBNA1 IgA.
timeFrame: Baseline
measure: The titer of EBV EA IgA.
timeFrame: Baseline
Locations:
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
state: Guangdong
Study officials:
References: