Radiomics for Preoperative Jaw Cyst Differentiation

NCT06579768 | Recruiting DMC

• 1 other identifier: SYSKY-2024-432-02
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

This study focuses on jawbone cystic lesions, including odontogenic tumors like ameloblastoma and various cysts. Treatment approaches differ; ameloblastomas often require surgical excision due to potential recurrence and metastasis, while cystic lesions may be treated with curettage and marsupialization. Accurate preoperative diagnosis is crucial for optimal treatment outcomes, as inappropriate choices can lead to delayed treatment or overtreatment, affecting patient quality of life. Currently, there is no standard protocol for differential diagnosis, highlighting the need for a predictive diagnostic model. The study will be a multicenter, prospective machine learning research involving 300 patients across 12 centers. It aims to enhance a previously developed predictive model that integrates machine learning with CT radiomics. Patients will be grouped based on imaging modalities, with data processed uniformly to improve diagnostic predictions. Inclusion criteria ensure comprehensive preoperative data, while exclusion criteria eliminate incomplete or previously treated cases. The study seeks to optimize the model's performance and provide valuable clinical insights.

Conditions

Jawbone Cysitc Lesion

Outcome Measures

Primary Outcomes (1)

• Statistical Analysis Metrics for Machine Learning Model Predictions

  Area Under the ROC Curve，Accuracy，Sentivity，Specificity...

  2025.06-2026.01

Study Arms (2)

spiral CT

Diagnostic Test: different types of computed tomography (CT) scans

cone beam CT

Diagnostic Test: different types of computed tomography (CT) scans

Interventions

different types of computed tomography (CT) scans DIAGNOSTIC_TEST

For enrolled patients with jaw cystic lesions, depending on their group, either a maxillofacial spiral CT scan or a cone beam CT scan is performed before surgical treatment.

cone beam CT; spiral CT

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients who visit various research centers, undergo preliminary examinations, and are clinically diagnosed with jawbone cystic lesions. If patients express a willingness to pursue further treatment, additional relevant examinations will be completed during subsequent diagnosis and treatment. Patients will be screened according to inclusion and exclusion criteria, informed about the details of the study, and asked if they are willing to participate. If patients agree to participate, they can be enrolled into the study population.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead
• Hospital of Stomatology, Wuhan University: collaborator
• Southern Medical University, China: collaborator
• Second Affiliated Hospital, School of Medicine, Zhejiang University: collaborator
• Xiangya Hospital of Central South University: collaborator
• Air Force Military Medical University, China: collaborator
• The People's Hospital Of QianNan: collaborator
• Central South University: collaborator
• Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University: collaborator
• Hospital of Stomatology, Sun Yat-Sen University: collaborator
• First Affiliated Hospital of Xinjiang Medical University: collaborator
• Guangxi Medical University College of Stomatology: collaborator

Study Sites (1)

Sun Yat-sen Memorial Hospital,Sun Yat-sen University

Guangzhou, Guangdong, 510120, China

RECRUITING

Related Publications (16)

• Baumhoer D, Holler S. [Cystic lesions of the jaws]. Pathologe. 2018 Feb;39(1):71-84. doi: 10.1007/s00292-017-0402-x. German.

  PMID: 29322252 RESULT

• Effiom OA, Ogundana OM, Akinshipo AO, Akintoye SO. Ameloblastoma: current etiopathological concepts and management. Oral Dis. 2018 Apr;24(3):307-316. doi: 10.1111/odi.12646. Epub 2017 Mar 9.

  PMID: 28142213 RESULT

• Al-Moraissi EA, Kaur A, Gomez RS, Ellis E 3rd. Effectiveness of different treatments for odontogenic keratocyst: a network meta-analysis. Int J Oral Maxillofac Surg. 2023 Jan;52(1):32-43. doi: 10.1016/j.ijom.2022.09.004. Epub 2022 Sep 21.

  PMID: 36150944 RESULT

• Yoshiura K, Higuchi Y, Araki K, Shinohara M, Kawazu T, Yuasa K, Tabata O, Kanda S. Morphologic analysis of odontogenic cysts with computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 1997 Jun;83(6):712-8. doi: 10.1016/s1079-2104(97)90325-5.

  PMID: 9195629 RESULT

• Neagu D, Escuder-de la Torre O, Vazquez-Mahia I, Carral-Roura N, Rubin-Roger G, Penedo-Vazquez A, Luaces-Rey R, Lopez-Cedrun JL. Surgical management of ameloblastoma. Review of literature. J Clin Exp Dent. 2019 Jan 1;11(1):e70-e75. doi: 10.4317/jced.55452. eCollection 2019 Jan.

  PMID: 30697397 RESULT

• Kreppel M, Zoller J. Ameloblastoma-Clinical, radiological, and therapeutic findings. Oral Dis. 2018 Mar;24(1-2):63-66. doi: 10.1111/odi.12702.

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• Mayerhoefer ME, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P, Cook G. Introduction to Radiomics. J Nucl Med. 2020 Apr;61(4):488-495. doi: 10.2967/jnumed.118.222893. Epub 2020 Feb 14.

  PMID: 32060219 RESULT

• Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys. 2020 Jun;47(5):e185-e202. doi: 10.1002/mp.13678.

  PMID: 32418336 RESULT

• Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res. 2021 Feb;10(2):1186-1199. doi: 10.21037/tlcr-20-708.

  PMID: 33718055 RESULT

• Alves DBM, Tuji FM, Alves FA, Rocha AC, Santos-Silva ARD, Vargas PA, Lopes MA. Evaluation of mandibular odontogenic keratocyst and ameloblastoma by panoramic radiograph and computed tomography. Dentomaxillofac Radiol. 2018 Oct;47(7):20170288. doi: 10.1259/dmfr.20170288. Epub 2018 Jun 5.

  PMID: 29791200 RESULT

• Meng Y, Zhang YQ, Ye X, Zhao YN, Chen Y, Liu DG. [Imaging analysis of ameloblastoma, odontogenic keratocyst and dentigerous cyst in the maxilla using spiral CT and cone beam CT]. Zhonghua Kou Qiang Yi Xue Za Zhi. 2018 Oct 9;53(10):659-664. doi: 10.3760/cma.j.issn.1002-0098.2018.10.003. Chinese.

  PMID: 30392221 RESULT

• Valdivia ADCM, Ramos-Ibarra ML, Franco-Barrera MJ, Arias-Ruiz LF, Garcia-Cruz JM, Torres-Bugarin O. What is Currently Known about Odontogenic Keratocysts? Oral Health Prev Dent. 2022 Jul 22;20:321-330. doi: 10.3290/j.ohpd.b3240829.

  PMID: 35866678 RESULT

• Huang CB, Hu JS, Tan K, Zhang W, Xu TH, Yang L. Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study. BMC Geriatr. 2022 Oct 13;22(1):796. doi: 10.1186/s12877-022-03502-9.

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• Zhu Y, Yao W, Xu BC, Lei YY, Guo QK, Liu LZ, Li HJ, Xu M, Yan J, Chang DD, Feng ST, Zhu ZH. Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers. BMC Cancer. 2021 Oct 30;21(1):1167. doi: 10.1186/s12885-021-08899-x.

  PMID: 34717582 RESULT

• Fang S, Wang Y, He Y, Yu T, Xie Y, Cai Y, Li W, Wang Y, Huang Z. Machine Learning Model Based on Radiomics for Preoperative Differentiation of Jaw Cystic Lesions. Otolaryngol Head Neck Surg. 2024 Jun;170(6):1561-1569. doi: 10.1002/ohn.744. Epub 2024 Apr 1.

  PMID: 38557958 RESULT

MeSH Terms

Interventions

Radionuclide Imaging

Intervention Hierarchy (Ancestors)

Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Techniques, Radioisotope

Central Study Contacts

Zhiquan Huang

CONTACT

13826142898; hzhquan@mail.sysu.edu.cn

Songling Fang

CONTACT

15878920032; fangsling@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: August 28, 2024
• First Posted: August 30, 2024
• Study Start: September 5, 2024
• Primary Completion: June 30, 2025
• Study Completion: January 1, 2026
• Last Updated: September 19, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)