The Application Value of Deep Learning-Based Nomograms in Benign-Malignant Discrimination of TI-RADS Category 4 Thyroid Nodules

NCT06258044 | Completed

• 1 other identifier: YXLL-KY-2023(133)
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief 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 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

Outcome Measures

Primary Outcomes (4)

• 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.

  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

  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.

  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.

  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.

Study Arms (2)

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

Eligibility Criteria

• Age: 23 Years - 78 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• Ultrasound-confirmed diagnosis of thyroid nodules that are classified as TI-RADS category 4.
• Availability of pathological results.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Ma Zhe: lead

Study Sites (1)

QianfoshanH

Jinan, Shandong, China

Location

MeSH Terms

Conditions

Thyroid Nodule

Condition Hierarchy (Ancestors)

Thyroid Neoplasms; Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Head and Neck Neoplasms; Endocrine System Diseases; Thyroid Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Director of Ultrasound

Study Record Dates

• First Submitted: January 4, 2024
• First Posted: February 14, 2024
• Study Start: April 1, 2022
• Primary Completion: November 30, 2023
• Study Completion: November 30, 2023
• Last Updated: February 14, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)