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

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.

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Key information

Conditions

HNSCC AI Carcinoma Carcinoma, Squamous Cell Head and Neck Neoplasms MRI Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Radiomic Squamous Cell Carcinoma of Head and Neck

Sex eligibility

All sexes

Study type

Observational

Primary location

Sun yat-sen memorial hospital, Guangzhou, Guangdong, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

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.

Treatment and study plan

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.

Primary outcomes

AUC(the area under the curve) values of the model

Time frame: 10 years（This is a retrospective research，we collect 10 years patients, but the project we implement data collection and analysis is 9 months）

The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC)

Sponsors and collaborators

Lead sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Other

Registry information

Official study title

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

Important dates

Study start: 2023
Primary completion: 2024
Study completion: 2024
First posted: Apr 16, 2024
Registry last updated: Apr 16, 2024

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed