Thyroid NoduleNCT06258044
The Application Value of Deep Learning-Based Nomograms in Benign-Malignant Discrimination of TI-RADS Category 4 Thyroid Nodules
An observational study of Thyroid Nodule, run by Ma Zhe at 1 site in China.
Trial summary
This retrospective study focuses on benign and malignant classification of thyroid nodules using deep learning techniques and evaluates the value of deep learning based nomograms in the classification of TI-RADS category 4 thyroid nodules to improve the accuracy of benign and malignant identification of TI-RADS… Read more about this study
- Who is it for?
- Ages 23 to 78 years
- Conditions the study is for
- Thyroid Nodule
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2022to 30 Nov 2023
- 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 23 to 78 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study collected data on a total of 500 TI-RADS category 4 thyroid nodules from 500 patients who attended the First Affiliated Hospital of Shandong First Medical University from April 2022 to November 2023.
Who may be able to join
- Ultrasound-confirmed diagnosis of thyroid nodules that are classified as TI-RADS category 4.
- Availability of pathological results.
Who cannot join
- Lack of pathological diagnosis.
- History of thyroid surgery or other treatments.
- Poor quality of ultrasound images of thyroid nodules.
- Incomplete clinical and imaging data of the patient.
Trial locations and contacts
1 site in 1 country.
About this study
This retrospective study focuses on benign and malignant classification of thyroid nodules using deep learning techniques and evaluates the value of deep learning based nomograms in the classification of TI-RADS category 4 thyroid nodules to improve the accuracy of benign and malignant identification of TI-RADS category 4 thyroid nodules.
Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.
- Conditions
- Thyroid Nodule
- Keywords
- TI-RADS category 4 thyroid nodules, YOLOv3 model, nomograms
- Medical subject headings
- Thyroid Nodule
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| maligant | Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of malignancy in the TI-RADS4 category | |
| benign | Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of benign TI-RADS4 category |
What is being measured
The primary outcome is the main question the study is designed to answer.
deep learning prediction model（YOLOv3） and the model evaluation
Based on the characteristics of benign and malignant thyroid nodules, the dataset was divided into a training set and a test set using the cross-validation method, and the YOLOv3 model was trained using data from the training set, and the performance of the model was evaluated using data from the test set.The model is evaluated using a number of metrics such as: precision-recall curve, effective classification precision, confusion matrix and area under the curve.
Measured: Immediately evaluated after the prediction model was built
nomogram prediction and assessment
Factoring clinical features, ultrasound grading and model predictions to map nomograms using R language.Evaluation of the nomogram using various metrics, including subject operating characteristic curves, calibration curves and decision curve analysis
Measured: Immediately evaluated after the nomogram was built
Selection of clinical features and assessment
The researchers selected patients with TI-RADS category 4 thyroid nodules within 1 year to comprise the dataset. The researchers analyzed the clinical factors in the dataset and analyzed the significance of these clinical factors on the statistical results and clinical characteristics using the Wilcoxon two-sample rank sum test or chi-square test.
Measured: After the dataset is collected and pathology results are obtained, the statistical results obtained are analyzed for clinical factors, averaging about 1 year.
Impact and assessment of ultrasound grading
The researchers selected patients with TI-RADS category 4 thyroid nodules within 1 year to comprise the dataset. The researchers analyzed the results of grading TI-RADS category 4 nodules in this dataset and determined the significance of ultrasound grading on the statistical results using the chi-square test.
Measured: The graded results of the ultrasound examination were analyzed after the data set collection was completed, the ultrasound examination was completed and the final pathology results were obtained, on average about 1 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Feb 2024
- Study start1 Apr 2022
- Primary completion30 Nov 2023
- Study completion30 Nov 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Thyroid NoduleStudies of the same condition, open and under way.
- 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
- 500 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ma Zhe
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Ma Zhe, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06258044
- Sponsor's protocol ID
- YXLL-KY-2023(133)
- Status last verified
- Feb 2024
- First submitted
- 4 Jan 2024
- Last updated
- 14 Feb 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 14 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Application Value of Deep Learning-Based Nomograms in Benign-Malignant Discrimination of TI-RADS Category 4 Thyroid Nodules
- ClinicalTrials.gov ID
- NCT06258044
- Acronym / sponsor ID
- YXLL-KY-2023(133)
- Conditions
- Thyroid Nodule
- Keywords
- TI-RADS category 4 thyroid nodules, YOLOv3 model, nomograms
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- Ma ZheUniversity, hospital or other
- Study start
- 1 Apr 2022 (actual)
- Primary completion
- 30 Nov 2023 (actual)
- Study completion
- 30 Nov 2023 (actual)
- First posted
- 14 Feb 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 focuses on benign and malignant classification of thyroid nodules using deep learning techniques and evaluates the value of deep learning based nomograms in the classification of TI-RADS category 4 thyroid nodules to improve the accuracy of benign and malignant identification of TI-RADS category 4 thyroid nodules.
Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| maligant | – | –Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of malignancy in the TI-RADS4 category |
| benign | – | –Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of benign TI-RADS4 category |
Who may qualify
- Ages
- 23 to 78 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study collected data on a total of 500 TI-RADS category 4 thyroid nodules from 500 patients who attended the First Affiliated Hospital of Shandong First Medical University from April 2022 to November 2023.
Key inclusion criteria (2 listed)
- Ultrasound-confirmed diagnosis of thyroid nodules that are classified as TI-RADS category 4.
- Availability of pathological results.
Key exclusion criteria (4 listed)
- Lack of pathological diagnosis.
- History of thyroid surgery or other treatments.
- Poor quality of ultrasound images of thyroid nodules.
- Incomplete clinical and imaging data of the patient.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
deep learning prediction model（YOLOv3） and the model evaluation
Time frame: Immediately evaluated after the prediction model was built
Based on the characteristics of benign and malignant thyroid nodules, the dataset was divided into a training set and a test set using the cross-validation method, and the YOLOv3 model was trained using data from the training set, and the performance of the model was evaluated using data from the test set.The model is evaluated using a number of metrics such as: precision-recall curve, effective classification precision, confusion matrix and area under the curve.
nomogram prediction and assessment
Time frame: Immediately evaluated after the nomogram was built
Factoring clinical features, ultrasound grading and model predictions to map nomograms using R language.Evaluation of the nomogram using various metrics, including subject operating characteristic curves, calibration curves and decision curve analysis
Selection of clinical features and assessment
Time frame: After the dataset is collected and pathology results are obtained, the statistical results obtained are analyzed for clinical factors, averaging about 1 year.
The researchers selected patients with TI-RADS category 4 thyroid nodules within 1 year to comprise the dataset. The researchers analyzed the clinical factors in the dataset and analyzed the significance of these clinical factors on the statistical results and clinical characteristics using the Wilcoxon two-sample rank sum test or chi-square test.
Impact and assessment of ultrasound grading
Time frame: The graded results of the ultrasound examination were analyzed after the data set collection was completed, the ultrasound examination was completed and the final pathology results were obtained, on average about 1 year.
The researchers selected patients with TI-RADS category 4 thyroid nodules within 1 year to comprise the dataset. The researchers analyzed the results of grading TI-RADS category 4 nodules in this dataset and determined the significance of ultrasound grading on the statistical results using the chi-square test.
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 14 Feb 2024.
- Individual participant dataSharing planned: No
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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.
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 Thyroid Nodule, run by Ma Zhe 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 2 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 14 Feb 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?
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?
deep learning prediction model（YOLOv3） and the model evaluation (time frame: Immediately evaluated after the prediction model was built). The study lists 4 primary outcomes in all. 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 14 Feb 2024; AzTrials last refreshed its copy on 28 Oct 2026.