Using machine learning to differentiate jawbone cystic lesions before surgery
Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study
This study is testing if using machine learning can help doctors better tell apart different types of jawbone cysts before surgery to improve treatment outcomes for patients.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 300 (estimated) |
| Sex | All |
| Sponsor | Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University Academic / other |
| Locations | 1 site (Guangzhou, Guangdong) |
| Trial ID | NCT06579768 on ClinicalTrials.gov |
What this trial studies
This observational study focuses on differentiating jawbone cystic lesions, including odontogenic tumors like ameloblastoma and various cysts, to improve preoperative diagnosis and treatment outcomes. It involves a multicenter, prospective approach with 300 patients across 12 centers, utilizing machine learning techniques integrated with CT radiomics to enhance a previously developed predictive model. Patients will be grouped based on imaging modalities, and data will be processed uniformly to improve diagnostic predictions, addressing the current lack of standard protocols for differential diagnosis. The goal is to establish an objective and scientific preoperative diagnostic prediction model that can significantly impact treatment decisions.
Who should consider this trial
Good fit: Ideal candidates include first-time visitors with jawbone cystic lesions who have complete preoperative medical records and imaging data.
Not a fit: Patients who have received prior treatment interventions or have incomplete medical records may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to more accurate preoperative diagnoses, resulting in better treatment choices and improved patient quality of life.
How similar studies have performed: Previous studies have shown success with similar machine learning approaches in medical imaging, indicating potential for this study's predictive model.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * first-time visitors who have not received other treatment interventions; * participants with complete preoperative medical records, imaging examinations, and imaging data; * participants who have undergone maxillofacial CT examination preoperatively, with complete CT data, no artifact interference in the lesion area, and a lesion size with the longest diameter of at least 2 cm; * participants who can tolerate surgical treatment, with specimens sent for routine pathological examination after surgery. Exclusion Criteria: * incomplete medical records, such as missing specialized examination and treatment operation records; * patients who received therapeutic operations at other hospitals at first diagnosis, not fully cured or with recurrence; * patients who did not undergo CT examination preoperatively, with incomplete CT data, severe artifact interference in the lesion area, or lesion size not meeting requirements; * lesions not submitted as specimens for examination during surgery, with no routine pathological examination; * unclear postoperative pathology reports, or pathological diagnoses other than odontogenic cysts or non-solid ameloblastoma.
Where this trial is running
Guangzhou, Guangdong
- Sun Yat-sen Memorial Hospital,Sun Yat-sen University — Guangzhou, Guangdong, China (Recruiting)
Study contacts
- Study coordinator: Zhiquan Huang
- Email: hzhquan@mail.sysu.edu.cn
- Phone: 13826142898
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.