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

NCT06366906 | Completed

• 1 other identifier: SYSKY-2023-426-01
• Study Type: observational
• Target: 319
• Locations: 1 country
• Sites: 2

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.

Conditions

HNSCC; AI; Radiomic; MRI

Outcome Measures

Primary Outcomes (1)

• 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）

Study Arms (2)

Cohort A

Randomly (121 cases) divided as the training and test sets in a 7:3 ratio.

Diagnostic Test: The Resnet50 deep learning (DL) model

Cohort B

Segmented into two groups based on the batched collected, which were defined as external validation set1 (n = 68) and external validation set2 (n = 130)

Diagnostic Test: The Resnet50 deep learning (DL) model

Interventions

The Resnet50 deep learning (DL) model DIAGNOSTIC_TEST

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.

Cohort A; Cohort B

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The radiomics features that affects the prediction of OCLNM in OC and OP SCC. A total of 319 patients with early-stage OC or OP SCC from the hospitals

You may qualify if:

• Pathologically confirmed, previously untreated oral and oropharyngeal squamous cell carcinoma with radical resection;
• MRI examination was performed two weeks before surgery;
• All patients with neck dissection and the status of regional lymph nodes was confirmed via pathological examination;
• All patients had no clinical evidence of nodal involvement.

You may not qualify if:

• Other malignant tumor, such as adenoid cystic carcinoma;
• a lack of complete MRI imaging or poor MRI imaging quality;
• patients had undergone neck dissection or treated non-surgically;
• patients with metastatic disease.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (2)

Sun yat-sen memorial hospital

Guangzhou, Guangdong, 510000, China

Location

Sun yat-sun memorial hospital

Guangzhou, Guangdong, 510000, China

Location

MeSH Terms

Conditions

Squamous Cell Carcinoma of Head and Neck

Condition Hierarchy (Ancestors)

Carcinoma, Squamous Cell; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Head and Neck Neoplasms; Neoplasms by Site

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 19, 2024
• First Posted: April 16, 2024
• Study Start: May 10, 2023
• Primary Completion: February 10, 2024
• Study Completion: February 10, 2024
• Last Updated: April 16, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)