Clinical trial NCT06579768: Radiomics for Preoperative Jaw Cyst Differentiation
Official title: Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Jawbone Cysitc Lesion
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
Collaborators: name: Hospital of Stomatology, Wuhan University
name: Southern Medical University, China
name: Second Affiliated Hospital, School of Medicine, Zhejiang University
name: Xiangya Hospital of Central South University
name: Air Force Military Medical University, China
name: The People's Hospital Of QianNan
name: Central South University
name: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
name: Hospital of Stomatology, Sun Yat-Sen University
name: First Affiliated Hospital of Xinjiang Medical University
name: Guangxi Medical University College of Stomatology
First posted: 2024-08-30
Enrollment: 300
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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.

Eligibility 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.

Primary outcomes:
measure: Statistical Analysis Metrics for Machine Learning Model Predictions
timeFrame: 2025.06-2026.01
Secondary outcomes:
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-sen Memorial Hospital,Sun Yat-sen University
state: Guangdong
Study officials:
References:
citation: Baumhoer D, Holler S. [Cystic lesions of the jaws]. Pathologe. 2018 Feb;39(1):71-84. doi: 10.1007/s00292-017-0402-x. German.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: Kreppel M, Zoller J. Ameloblastoma-Clinical, radiological, and therapeutic findings. Oral Dis. 2018 Mar;24(1-2):63-66. doi: 10.1111/odi.12702.
citation: Yip 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.