Jawbone Cysitc LesionNCT06579768
Radiomics for Preoperative Jaw Cyst Differentiation
An observational study of Jawbone Cysitc Lesion, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Jawbone Cysitc Lesion
- What is being tested?
- different types of computed tomography (CT) scans
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 5 Sep 2024to 1 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Sun Yat-sen Memorial Hospital,Sun Yat-sen University
Guangzhou, Guangdong, 510120
Zhiquan Huang, 13826142898, hzhquan@mail.sysu.edu.cn
About this study
Official title: Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study
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.
Read the detailed description
Jawbone cystic lesions include odontogenic tumors and non-tumorous cystic lesions occurring within the jawbone, with ameloblastoma being the most common among the former, and odontogenic and non-odontogenic cysts among the latter. Currently, the treatment focus varies for different types of jawbone cystic lesions. Ameloblastomas, which may recur and metastasize, are primarily treated with surgical excision, while cystic lesions are more broadly treated with procedures like curettage and marsupialization. Therefore, accurate preoperative differential diagnosis of various jawbone lesions and the subsequent selection of appropriate treatment plans are crucial for achieving optimal patient outcomes. Inappropriate treatment choices may delay the condition or lead to overtreatment, affecting the patient's quality of life. At present, there is still a lack of an objective and accurate standard and differential diagnosis protocol for the treatment of jawbone cystic lesions, making the establishment of an objective and scientific preoperative diagnostic prediction model of significant clinical importance. In previous research, investigators successfully developed an effective predictive diagnostic model by integrating machine learning techniques with computed tomography (CT) radiomics, achieving a maximum AUC ( area under curve ) value >0.8, indicating good predictive performance and clinical reference value. In the current study, investigators aim to conduct a multicenter, prospective machine learning study to further enhance the model's predictive diagnostic performance and assist clinical diagnosis and treatment.
This study is designed as a multicenter, prospective machine learning study, involving 300 patients with jawbone cystic lesions across 12 centers, as detailed in the list of collaborating institutions. Based on research group's previous investigation of the actual diagnostic and treatment conditions at each research center, investigators plan to utilize different types of imaging data for grouping according to the imaging examinations conducted, and to standardize the processing of imaging data from different units and types for subsequent work. Sun Yat-sen Memorial Hospital of Sun Yat-sen University will serve as the main center, with other institutions as sub-centers. The specific grouping is as follows: the spiral CT group includes six general hospitals; the cone beam CT (CBCT) group includes one general hospital and five specialized dental hospitals.
During the study, after enrolling participants who meet the inclusion criteria, investigators will collect maxillofacial CT imaging data, import them into the software (LIFEx version 6.30), and delineate the region of interest (ROI). Radiomic features within the ROI will be extracted using Pyradiomics software, selected, and used for preoperative diagnostic predictions with the existing model. After surgical treatment, the pathological results of the lesions will be tracked and recorded. If conditions permit, the model's predictive performance can be further optimized in phases during the study, or methodological adjustments and reconstructions of the predictive model can be attempted using all available data to achieve a more ideal preoperative diagnostic prediction.
- Conditions
- Jawbone Cysitc Lesion
- Being tested
- different types of computed tomography (CT) scans
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
different types of computed tomography (CT) scansDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| spiral CT | Not described Receives: Diagnostic Test: different types of computed tomography (CT) scans | |
| cone beam CT | Not described Receives: Diagnostic Test: different types of computed tomography (CT) scans |
What is being measured
The primary outcome is the main question the study is designed to answer.
Statistical Analysis Metrics for Machine Learning Model Predictions
Area Under the ROC Curve，Accuracy，Sentivity，Specificity...
Measured: 2025.06-2026.01
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Aug 2024
- Study start5 Sep 2024
- Primary completion30 Jun 2025 (estimated)
- Study completion1 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Jawbone Cysitc LesionStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Hospital of Stomatology, Wuhan University, Southern Medical University, China, Second Affiliated Hospital, Zhejiang University, School of Medicine, Xiangya Hospital of Central South University, Air Force Military Medical University, China, The People's Hospital Of QianNan, Central South University, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University, Hospital of Stomatology, Sun Yat-Sen University, First Affiliated Hospital of Xinjiang Medical University, Guangxi Medical University College of Stomatology
- Responsible party
- Principal investigator, huangzhiquan, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06579768
- Sponsor's protocol ID
- SYSKY-2024-432-02
- Status last verified
- Sep 2024
- First submitted
- 28 Aug 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Baumhoer D, Holler S. [Cystic lesions of the jaws]. Pathologe. 2018 Feb;39(1):71-84. doi: 10.1007/s00292-017-0402-x. German.
PubMed 29322252Effiom 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.
PubMed 28142213Al-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.
PubMed 36150944Yoshiura 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.
PubMed 9195629Neagu 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.
PubMed 30697397Kreppel M, Zoller J. Ameloblastoma-Clinical, radiological, and therapeutic findings. Oral Dis. 2018 Mar;24(1-2):63-66. doi: 10.1111/odi.12702.
PubMed 29480593Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016 Jul 7;61(13):R150-66. doi: 10.1088/0031-9155/61/13/R150. Epub 2016 Jun 8.
PubMed 27269645Mayerhoefer 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.
PubMed 32060219Avanzo 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.
PubMed 32418336Binczyk 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.
PubMed 33718055Alves 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.
PubMed 29791200Meng 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.
PubMed 30392221Valdivia 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.
PubMed 35866678Huang 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.
PubMed 36229793Zhu 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.
PubMed 34717582
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study
- ClinicalTrials.gov ID
- NCT06579768
- Acronym / sponsor ID
- SYSKY-2024-432-02
- Conditions
- Jawbone Cysitc Lesion
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Hospital of Stomatology, Wuhan University, Southern Medical University, China, Second Affiliated Hospital, Zhejiang University, School of Medicine, Xiangya Hospital of Central South University, Air Force Military Medical University, China, The People's Hospital Of QianNan, Central South University, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University, Hospital of Stomatology, Sun Yat-Sen University, First Affiliated Hospital of Xinjiang Medical University, Guangxi Medical University College of Stomatology
- Study start
- 5 Sep 2024 (actual)
- Primary completion
- 30 Jun 2025 (estimated)
- Study completion
- 1 Jan 2026 (estimated)
- First posted
- 30 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Jawbone cystic lesions include odontogenic tumors and non-tumorous cystic lesions occurring within the jawbone, with ameloblastoma being the most common among the former, and odontogenic and non-odontogenic cysts among the latter. Currently, the treatment focus varies for different types of jawbone cystic lesions. Ameloblastomas, which may recur and metastasize, are primarily treated with surgical excision, while cystic lesions are more broadly treated with procedures like curettage and marsupialization. Therefore, accurate preoperative differential diagnosis of various jawbone lesions and the subsequent selection of appropriate treatment plans are crucial for achieving optimal patient outcomes. Inappropriate treatment choices may delay the condition or lead to overtreatment, affecting the patient's quality of life. At present, there is still a lack of an objective and accurate standard and differential diagnosis protocol for the treatment of jawbone cystic lesions, making the establishment of an objective and scientific preoperative diagnostic prediction model of significant clinical importance. In previous research, investigators successfully developed an effective predictive diagnostic model by integrating machine learning techniques with computed tomography (CT) radiomics, achieving a maximum AUC ( area under curve ) value >0.8, indicating good predictive performance and clinical reference value. In the current study, investigators aim to conduct a multicenter, prospective machine learning study to further enhance the model's predictive diagnostic performance and assist clinical diagnosis and treatment.
This study is designed as a multicenter, prospective machine learning study, involving 300 patients with jawbone cystic lesions across 12 centers, as detailed in the list of collaborating institutions. Based on research group's previous investigation of the actual diagnostic and treatment conditions at each research center, investigators plan to utilize different types of imaging data for grouping according to the imaging examinations conducted, and to standardize the processing of imaging data from different units and types for subsequent work. Sun Yat-sen Memorial Hospital of Sun Yat-sen University will serve as the main center, with other institutions as sub-centers. The specific grouping is as follows: the spiral CT group includes six general hospitals; the cone beam CT (CBCT) group includes one general hospital and five specialized dental hospitals.
During the study, after enrolling participants who meet the inclusion criteria, investigators will collect maxillofacial CT imaging data, import them into the software (LIFEx version 6.30), and delineate the region of interest (ROI). Radiomic features within the ROI will be extracted using Pyradiomics software, selected, and used for preoperative diagnostic predictions with the existing model. After surgical treatment, the pathological results of the lesions will be tracked and recorded. If conditions permit, the model's predictive performance can be further optimized in phases during the study, or methodological adjustments and reconstructions of the predictive model can be attempted using all available data to achieve a more ideal preoperative diagnostic prediction.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| spiral CT | – | Diagnostic Test: different types of computed tomography (CT) scans |
| cone beam CT | – | Diagnostic Test: different types of computed tomography (CT) scans |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| different types of computed tomography (CT) scansFor 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. | Diagnostic test | cone beam CT, spiral CT |
Primary outcome
Statistical Analysis Metrics for Machine Learning Model Predictions
Time frame: 2025.06-2026.01
Area Under the ROC Curve，Accuracy，Sentivity，Specificity...
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
- Results publication: PubMed 29322252Baumhoer D, Holler S. [Cystic lesions of the jaws]. Pathologe. 2018 Feb;39(1):71-84. doi: 10.1007/s00292-017-0402-x. German.
- Results publication: PubMed 28142213Effiom 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.
- Results publication: PubMed 36150944Al-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.
- Results publication: PubMed 9195629Yoshiura 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.
- Results publication: PubMed 30697397Neagu 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.
- Results publication: PubMed 29480593Kreppel M, Zoller J. Ameloblastoma-Clinical, radiological, and therapeutic findings. Oral Dis. 2018 Mar;24(1-2):63-66. doi: 10.1111/odi.12702.
- Results publication: PubMed 27269645Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016 Jul 7;61(13):R150-66. doi: 10.1088/0031-9155/61/13/R150. Epub 2016 Jun 8.
- Results publication: PubMed 32060219Mayerhoefer 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.
- Results publication: PubMed 32418336Avanzo 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.
- Results publication: PubMed 33718055Binczyk 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.
- Results publication: PubMed 29791200Alves 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.
- Results publication: PubMed 30392221Meng 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.
- Individual participant dataSharing planned: No
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Jawbone Cysitc Lesion, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zhiquan Huang (13826142898, hzhquan@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Statistical Analysis Metrics for Machine Learning Model Predictions (time frame: 2025.06-2026.01). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06579768, which also lists 16 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Sep 2024.