HNSCCNCT06366906
10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma
An observational study of HNSCC and AI, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- What is being tested?
- The Resnet50 deep learning (DL) model
- How many people
- 319 participants
- Where
- 2 sitesChina
- Study dates
- 10 May 2023to 10 Feb 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country.
China2 sites
Sun yat-sen memorial hospital
Guangzhou, Guangdong, 510000
Sun yat-sun memorial hospital
Guangzhou, Guangdong, 510000
About this study
Official title: 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.
- Being tested
- The Resnet50 deep learning (DL) model
- Medical subject headings
- Squamous Cell Carcinoma of Head and Neck
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The Resnet50 deep learning (DL) modelDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Cohort A | Randomly (121 cases) divided as the training and test sets in a 7:3 ratio. Receives: 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) Receives: Diagnostic Test: The Resnet50 deep learning (DL) model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 10 years（This is a retrospective research，we collect 10 years patients, but the project we implement data collection and analysis is 9 months）
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Apr 2024
- Study start10 May 2023
- Primary completion10 Feb 2024
- Study completion10 Feb 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HNSCCStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 319 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06366906
- Sponsor's protocol ID
- SYSKY-2023-426-01
- Status last verified
- Apr 2024
- First submitted
- 19 Mar 2024
- Last updated
- 16 Apr 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinicopathological and Prognostic Analysis of Oral and Maxillofacial Squamous Cell Carcinoma: a Single-center 10-year Retrospective Study
- ClinicalTrials.gov ID
- NCT06366906
- Acronym / sponsor ID
- SYSKY-2023-426-01
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 319 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 10 May 2023 (actual)
- Primary completion
- 10 Feb 2024 (actual)
- Study completion
- 10 Feb 2024 (actual)
- First posted
- 16 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Cohort A | – | Diagnostic Test: The Resnet50 deep learning (DL) modelRandomly (121 cases) divided as the training and test sets in a 7:3 ratio. |
| Cohort B | – | Diagnostic Test: The Resnet50 deep learning (DL) modelSegmented into two groups based on the batched collected, which were defined as external validation set1 (n = 68) and external validation set2 (n = 130) |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| The Resnet50 deep learning (DL) modelThe 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. | Diagnostic test | Cohort A, Cohort B |
Primary outcome
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)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Apr 2024.
- Individual participant dataSharing planned: No
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RecruitingPhase 2
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RecruitingPhase 1/2
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RecruitingPhase 2
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RecruitingPhase 3
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of HNSCC and AI, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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）). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Apr 2024.