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

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 Overview

• Status: Completed
• Conditions: Thyroid Nodule

• Study Type: Observational
• Enrollment (Actual): 500

Contacts and Locations

Study Locations

• China
  • Shandong
    • Jinan, Shandong, China
      • QianfoshanH

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

1. Lack of pathological diagnosis.
2. History of thyroid surgery or other treatments.
3. Poor quality of ultrasound images of thyroid nodules.
4. Incomplete clinical and imaging data of the patient.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.

Collaborators and Investigators

• Sponsor: Ma Zhe

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2022
• Primary Completion (Actual): November 30, 2023
• Study Completion (Actual): November 30, 2023

Study Registration Dates

• First Submitted: January 4, 2024
• First Submitted That Met QC Criteria: February 5, 2024
• First Posted (Actual): February 14, 2024

Study Record Updates

• Last Update Posted (Actual): February 14, 2024
• Last Update Submitted That Met QC Criteria: February 5, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Keywords: nomograms; TI-RADS category 4 thyroid nodules; YOLOv3 model
• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Site; Endocrine System Diseases; Endocrine Gland Neoplasms; Head and Neck Neoplasms; Thyroid Neoplasms; Thyroid Diseases; Thyroid Nodule
• Other Study ID Numbers: YXLL-KY-2023(133)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No