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

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

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.

Study Overview

• Status: Completed
• Conditions: MRI; HNSCC; AI; Radiomic
• Intervention / Treatment: Diagnostic test: The Resnet50 deep learning (DL) model

• Study Type: Observational
• Enrollment (Actual): 319

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Sun Yat-Sen Memorial Hospital
    • Guangzhou, Guangdong, China, 510000
      • Sun yat-sun memorial hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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）

Collaborators and Investigators

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

Study record dates

Study Major Dates

• Study Start (Actual): May 10, 2023
• Primary Completion (Actual): February 10, 2024
• Study Completion (Actual): February 10, 2024

Study Registration Dates

• First Submitted: March 19, 2024
• First Submitted That Met QC Criteria: April 15, 2024
• First Posted (Actual): April 16, 2024

Study Record Updates

• Last Update Posted (Actual): April 16, 2024
• Last Update Submitted That Met QC Criteria: April 15, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms, Squamous Cell; Carcinoma, Squamous Cell
• Other Study ID Numbers: SYSKY-2023-426-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No