Ignite Creation Date:
2024-05-06 @ 8:23 PM
Last Modification Date:
2024-10-26 @ 3:26 PM
Study NCT ID:
NCT06366906
Status:
COMPLETED
Last Update Posted:
2024-04-16
First Post:
2024-03-19
Brief Title:
10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma
Sponsor:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Organization:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study Overview
Official Title:
Clinicopathological and Prognostic Analysis of Oral and Maxillofacial Squamous Cell Carcinoma a Single-center 10-year Retrospective Study
Status:
COMPLETED
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Introduction The incidence of occult cervical lymph node metastases OCLNM is reported to be 20-30 in early-stage oral cancer and oropharyngeal cancer There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions
Aim To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma OC and OP SCC based on deep learning features DLFs and radiomics features
Methods A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training test and external validation sets Traditional radiomics features and DLFs were extracted from their MRI images The least absolute shrinkage and selection operator LASSO analysis was employed to identify the most valuable features Prediction models for OCLNM were developed using radiomics features and DLFs The effectiveness of the models and their clinical applicability were evaluated using the area under the curve AUC decision curve analysis DCA and survival analysis
Aim To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma OC and OP SCC based on deep learning features DLFs and radiomics features
Methods A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training test and external validation sets Traditional radiomics features and DLFs were extracted from their MRI images The least absolute shrinkage and selection operator LASSO analysis was employed to identify the most valuable features Prediction models for OCLNM were developed using radiomics features and DLFs The effectiveness of the models and their clinical applicability were evaluated using the area under the curve AUC decision curve analysis DCA and survival analysis
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None