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

Ma Zhe

Status: Completed

Conditions: Thyroid Nodule

Study type: Observational

Funder types: Other

Identifiers

NCT06258044

YXLL-KY-2023(133)

Details and patient eligibility

About

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.

Enrollment: 500 patients

Sex: All

Ages: 23 to 78 years old

Volunteers: No Healthy Volunteers

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.

Trial design

500 participants in 2 patient groups

maligant

Description:

Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of malignancy in the TI-RADS4 category

benign

Description:

Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of benign TI-RADS4 category