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

NCT06258044 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2024-02-14

No results posted yet for this study

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

Sponsors & Collaborators

  • Ma Zhe

    lead OTHER

Eligibility

Min Age
23 Years
Max Age
78 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-04-01
Primary Completion
2023-11-30
Completion
2023-11-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06258044 on ClinicalTrials.gov