AI, HNSCC, MRI, Radiomic
Conditions
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.
Interventions
DIAGNOSTIC_TESTThe 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.
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC(the area under the curve) values of the model | 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) |
Countries
China
Outcome results
None listed