Computer Aided Tool for Diagnosis of Neck Masses in Children

The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for neck masses using machine learning and deep learning techniques on clinical information and radiological images in children.

Study Overview

• Status: Recruiting
• Conditions: Infantile Hemangiomas; Neck Mass; Thyroglossal Duct Cysts; Branchial Cleft Anomalies; Dermoid and Epidermoid Cysts; Teratomas
• Intervention / Treatment: Diagnostic test: Artificial Intelligence Algorithm

Detailed Description

This study is a retrospective-prospective design by West China Hospital, Sichuan University, including clinical data and radiological images. A retrospective database was enrolled for patients with definite histological diagnosis and available radiological images from June 2010 and December 2020. The investigators have constructed deep learning and machine learning diagnostic models on this retrospective cohort and validated it internally. A prospective cohort would recruit patients found neck masses since January 2021. The proposed computer aided diagnostic models would also be validated in this prospective cohort externally. The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for neck masses using machine learning and deep learning techniques on clinical data and radiological images in children.

• Study Type: Observational
• Enrollment (Anticipated): 1500

Contacts and Locations

Study Locations

• China
  • Sichuan
    • Chengdu, Sichuan, China, 6100041
      • Recruiting
      • West China Hospital, Sichuan University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 1 second to 18 years (Child, Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients who were found neck masses, and had completed clinical information and radiological images before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.

Description

Inclusion Criteria:

• Age up to 18 years old
• Receiving no treatment before diagnosis
• With written informed consent

Exclusion Criteria:

• Clinical data missing
• Unavailable radiological images
• Without written informed consent

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Retrospective cohort; The internal cohort was retrospectively enrolled in West China Hospital, Sichuan University from June 2010 and December 2020. It is a training and internal validation cohort.	Diagnostic test: Artificial Intelligence Algorithm; Different machine learning and deep learning computer aided strategies for model construction and validation.
Prospective cohort; The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.	Diagnostic test: Artificial Intelligence Algorithm; Different machine learning and deep learning computer aided strategies for model construction and validation.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic accuracy of neck masses with AI-based screening tools in children	The diagnostic accuracy of neck masses with AI-based screening tools in children.	1 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic sensitivity of neck masses with AI-based screening tools in children	The diagnostic sensitivity of neck masses with AI-based screening tools in children.	1 month
The diagnostic specificity of neck masses with AI-based screening tools in children	The diagnostic specificity of neck masses with AI-based screening tools in children.	1 month
The diagnostic positive predictive value of neck masses with AI-based screening tools in children	The diagnostic positive predictive value of neck masses with AI-based screening tools in children.	1 month
The diagnostic negative predictive value of neck masses with AI-based screening tools in children	The diagnostic negative predictive value of neck masses with AI-based screening tools in children	1 month

Collaborators and Investigators

• Sponsor: West China Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2021
• Primary Completion (Anticipated): December 31, 2024
• Study Completion (Anticipated): December 31, 2024

Study Registration Dates

• First Submitted: December 24, 2021
• First Submitted That Met QC Criteria: December 24, 2021
• First Posted (Actual): January 12, 2022

Study Record Updates

• Last Update Posted (Actual): January 27, 2022
• Last Update Submitted That Met QC Criteria: January 11, 2022
• Last Verified: January 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms by Histologic Type; Neoplasms; Congenital Abnormalities; Neoplasms, Germ Cell and Embryonal; Pathological Conditions, Anatomical; Skin Abnormalities; Neoplasms, Vascular Tissue; Hemangioma, Capillary; Port-Wine Stain; Hemangioma; Teratoma; Cysts; Epidermal Cyst; Thyroglossal Cyst
• Other Study ID Numbers: HX-20211023

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