Deep Learning-based sbORN Diagnostic Model

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

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.

Conditions3

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