Laryngeal CarcinomaNCT06463756
AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma
An observational study of Laryngeal Carcinoma and Thyroid Cartilage, run by First Affiliated Hospital of Chongqing Medical University at 1 site in China.
Trial summary
This retrospective study was to develop and verify CT-based AI model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. Read more about this study
- Who is it for?
- Ages 18 to 81 years
- Conditions the study is for
- Laryngeal Carcinoma, Thyroid Cartilage
- What is being tested?
- AI
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 13 Aug 2023to 13 Oct 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 18 to 81 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The investigators collected patients with laryngeal carcinoma from two centers.
Who may be able to join
- Availability of complete clinical data
- Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
- CT examination performed within 2 weeks before surgery
Who cannot join
- Patients who received preoperative chemotherapy or radiation therapy
- CT images with significant artifacts
- Patients with tumor recurrence
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Chongqing Medical University
Chongqing
Peng juan, +86 189 8328 0171, pengjuan1209@126.com
About this study
Official title: CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study
This retrospective study was to develop and verify CT-based AI model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. In addition, the researchers investigated the prediction of survival outcomes of patients by the above optimal models.
Read the detailed description
Laryngeal squamous cell carcinoma (LSCC), as one of the most common head and neck tumors, is the eighth leading cause of cancer-associated death worldwide. The treatment decisions has a profound impact on both tumor control and functional prognosis of LSCC patients. And these decisions are primarily based on tumor staging, with the invasion of the thyroid cartilage serving as a crucial determinant. Consequently, the presence of thyroid cartilage invasion indicates an advanced stage (T3 or T4) diagnosis for the LSCC patients. For patients without thyroid cartilage invasion, partial laryngectomy may be considered to preserve laryngeal function. However, for patients with advanced laryngeal carcinoma and thyroid cartilage invasion extending beyond the larynx, total laryngectomy is often necessary to completely remove the tumor and extend survival time. Therefore, accurate assessment of thyroid cartilage invasion is vital for treatment decision-making and prognosis evaluation for LSCC patients.
- Conditions
- Laryngeal Carcinoma, Thyroid Cartilage
- Being tested
- AI
- Keywords
- radiomics, deep learning
- Medical subject headings
- Laryngeal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIOther
Also called radiomics, deep learning
Radiomics extracts quantitative information from medical images to generate high-dimensional feature vectors for analysis. It aims to provide insights into disease processes and improve diagnosis.
Deep learning utilizes neural networks with multiple layers to learn complex patterns from data. In medical imaging, it enables accurate and efficient analysis for disease detection and diagnosis.
| Study group | Role | What they receive |
|---|---|---|
| training cohort | No interventions Receives: Other: AI | |
| internal validation cohort | No interventions Receives: Other: AI | |
| external validation cohort | No interventions Receives: Other: AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the curve, AUC
Area under the curve(AUC) is a metric widely used in machine learning and medical research to evaluate the performance of models in binary classification problems. It reflects the ability of a model to identify true positives (True Positives) while avoiding falsely classifying negative examples as positive (False Positives).
Measured: Through study completion, an average of 6 months
Secondary outcomes (1)
Disease-Free-Survival, DFS
Disease-Free Survival (DFS) refers to the time from the start of randomization (usually the starting point of a clinical trial) to the recurrence of the disease or death of the patient due to disease progression. DFS is an important clinical and statistical indicator used to evaluate the long-term effects of cancer treatment.
Measured: The date of surgery and the occurrence of events such as disease progression, the date of the last follow-up, or death from any cause, and the follow-up time was at least 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jun 2024
- Study start13 Aug 2023
- Primary completion13 Sep 2024 (estimated)
- Study completion13 Oct 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Laryngeal CarcinomaStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Chongqing Medical 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Nankai University
- Responsible party
- Principal investigator, xinwei Chen, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06463756
- Sponsor's protocol ID
- 2024-Chenx
- Status last verified
- Aug 2024
- First submitted
- 12 Jun 2024
- Last updated
- 22 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The clinical data are manually collected from the clinical case system; the CT image data are exported from the PACS system and anonymously stored on a separate data disk; and the image materials are collected and anonymously stored on a separate data disk.
Study at a glance
From the registry record, last updated by the sponsor on 22 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study
- ClinicalTrials.gov ID
- NCT06463756
- Acronym / sponsor ID
- 2024-Chenx
- Conditions
- Laryngeal Carcinoma, Thyroid Cartilage
- Keywords
- radiomics, deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical UniversityUniversity, hospital or other
- Collaborators
- Nankai University
- Study start
- 13 Aug 2023 (actual)
- Primary completion
- 13 Sep 2024 (estimated)
- Study completion
- 13 Oct 2024 (estimated)
- First posted
- 18 Jun 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.
This retrospective study was to develop and verify CT-based AI model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. In addition, the researchers investigated the prediction of survival outcomes of patients by the above optimal models.
Detailed description
Laryngeal squamous cell carcinoma (LSCC), as one of the most common head and neck tumors, is the eighth leading cause of cancer-associated death worldwide. The treatment decisions has a profound impact on both tumor control and functional prognosis of LSCC patients. And these decisions are primarily based on tumor staging, with the invasion of the thyroid cartilage serving as a crucial determinant. Consequently, the presence of thyroid cartilage invasion indicates an advanced stage (T3 or T4) diagnosis for the LSCC patients. For patients without thyroid cartilage invasion, partial laryngectomy may be considered to preserve laryngeal function. However, for patients with advanced laryngeal carcinoma and thyroid cartilage invasion extending beyond the larynx, total laryngectomy is often necessary to completely remove the tumor and extend survival time. Therefore, accurate assessment of thyroid cartilage invasion is vital for treatment decision-making and prognosis evaluation for LSCC patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| training cohort | – | Other: AINo interventions |
| internal validation cohort | – | Other: AINo interventions |
| external validation cohort | – | Other: AINo interventions |
Who may qualify
- Ages
- 18 to 81 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The investigators collected patients with laryngeal carcinoma from two centers.
Key inclusion criteria (3 listed)
- Availability of complete clinical data
- Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
- CT examination performed within 2 weeks before surgery
Key exclusion criteria (3 listed)
- Patients who received preoperative chemotherapy or radiation therapy
- CT images with significant artifacts
- Patients with tumor recurrence
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 |
|---|---|---|
| AIAlso: radiomics, deep learningRadiomics extracts quantitative information from medical images to generate high-dimensional feature vectors for analysis. It aims to provide insights into disease processes and improve diagnosis. Deep learning utilizes neural networks with multiple layers to learn complex patterns from data. In medical imaging, it enables accurate and efficient analysis for disease detection and diagnosis. | Other | external validation cohort, internal validation cohort, training cohort |
Primary outcome
Area under the curve, AUC
Time frame: Through study completion, an average of 6 months
Area under the curve(AUC) is a metric widely used in machine learning and medical research to evaluate the performance of models in binary classification problems. It reflects the ability of a model to identify true positives (True Positives) while avoiding falsely classifying negative examples as positive (False Positives).
1 secondary outcome
Disease-Free-Survival, DFS
Time frame: The date of surgery and the occurrence of events such as disease progression, the date of the last follow-up, or death from any cause, and the follow-up time was at least 3 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Aug 2024.
- Individual participant dataSharing planned: No. The clinical data are manually collected from the clinical case system; the CT image data are exported from the PACS system and anonymously stored on a separate data disk; and the image materials are collected and anonymously stored on a separate data disk.
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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 First Affiliated Hospital of Chongqing Medical University
Chongqing, China
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Laryngeal Carcinoma and Thyroid Cartilage, run by First Affiliated Hospital of Chongqing Medical University at 1 site 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 3 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 22 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the curve, AUC (time frame: Through study completion, an average of 6 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 22 Aug 2024.