10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma

MRI Clinical Trial

Official title:

Clinicopathological and Prognostic Analysis of Oral and Maxillofacial Squamous Cell Carcinoma: a Single-center 10-year Retrospective Study

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06366906
• Other study ID #: SYSKY-2023-426-01
• Status: Completed
• Start date: May 10, 2023
• Est. completion date: February 10, 2024

Study information

• Verified date: April 2024
• Source: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 319
• Est. completion date: February 10, 2024
• Est. primary completion date: February 10, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

1. Pathologically confirmed, previously untreated oral and oropharyngeal squamous cell carcinoma with radical resection;

2. MRI examination was performed two weeks before surgery;

3. All patients with neck dissection and the status of regional lymph nodes was confirmed via pathological examination;

4. All patients had no clinical evidence of nodal involvement.

Exclusion Criteria:

1. Other malignant tumor, such as adenoid cystic carcinoma;

2. a lack of complete MRI imaging or poor MRI imaging quality;

3. patients had undergone neck dissection or treated non-surgically;

4. patients with metastatic disease.

Related Conditions & MeSH terms

• Carcinoma, Squamous Cell
• HNSCC
• MRI

Intervention

Diagnostic Test:
• The Resnet50 deep learning (DL) model
The predictive capability of the above Resnet50 deep learning (DL) model was validated in the test set. Based on the AUC and ACC, the best prediction model was identified. To explore the robust of the selected model, ROC analysis was performed the in the external validation set. Moreover, the Log-rank test was applied to evaluate the prognostic value of the model.

Locations

Country	Name	City	State
China	Sun yat-sen memorial hospital	Guangzhou	Guangdong
China	Sun yat-sun memorial hospital	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUC(the area under the curve) values of the model	The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC)	10 years(This is a retrospective research,we collect 10 years patients, but the project we implement data collection and analysis is 9 months)