10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma
NCT ID: NCT06366906
Last Updated: 2024-04-16
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
319 participants
OBSERVATIONAL
2023-05-10
2024-02-10
Brief Summary
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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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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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Cohort A
Randomly (121 cases) divided as the training and test sets in a 7:3 ratio.
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)
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
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
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.
ALL
No
Sponsors
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Locations
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Sun yat-sen memorial hospital
Guangzhou, Guangdong, China
Sun yat-sun memorial hospital
Guangzhou, Guangdong, China
Countries
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Other Identifiers
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SYSKY-2023-426-01
Identifier Type: -
Identifier Source: org_study_id
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