Ignite Creation Date:
2024-10-25 @ 8:02 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06579768
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-28
Brief Title:
Radiomics for Preoperative Jaw Cyst Differentiation
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images A Multicenter Prospective Machine Learning Study
Status:
RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 models performance and provide valuable clinical insights
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 models 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 patients 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 08 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 models 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 groups 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 630 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 models 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
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 groups 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 630 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 models 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None