Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children

NCT05179850 | Unknown

• 1 other identifier: HX-2021477
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief Summary

The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for retroperitoneal tumor using machine learning and deep learning techniques on computed tomography images in children.

Conditions

Wilms' Tumor; Neuroblastoma; Teratoma; Lymphoma; Sarcoma; Germ Cell Tumor

Outcome Measures

Primary Outcomes (1)

• Pathological tumor diagnosis

  The diagnosis is defined by histopathological specimens from surgery and/or biopsy.

  Baseline

Study Arms (2)

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: Radiomic Algorithm

Prospective cohort

The same inclusion/exclusion criteria were applied for the same center prospectively. It is a external validation cohort.

Diagnostic Test: Radiomic Algorithm

Interventions

Radiomic Algorithm DIAGNOSTIC_TEST

Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

Prospective cohort; Retrospective cohort

Eligibility Criteria

• Age: 0 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Patients who had retroperitoneal tumor and completed the abdominal computed tomography examination before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.

You may qualify if:

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

You may not qualify if:

• Clinical data missing
• Unavailable computed tomography images
• Without written informed consent

Sponsors & Collaborators

• West China Hospital: lead

Study Sites (1)

West China Hospital, Sichuan University

Chengdu, Sichuan, 6100041, China

RECRUITING

MeSH Terms

Conditions

Wilms Tumor; Neuroblastoma; Teratoma; Lymphoma; Sarcoma; Neoplasms, Germ Cell and Embryonal

Condition Hierarchy (Ancestors)

Neoplasms, Complex and Mixed; Neoplasms by Histologic Type; Neoplasms; Kidney Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplastic Syndromes, Hereditary; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases; Genetic Diseases, Inborn; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Neuroectodermal Tumors, Primitive, Peripheral; Neuroectodermal Tumors, Primitive; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue; Lymphoproliferative Disorders; Lymphatic Diseases; Hemic and Lymphatic Diseases; Immunoproliferative Disorders; Immune System Diseases; Neoplasms, Connective and Soft Tissue

Central Study Contacts

Yuhan Yang, MD

CONTACT

8613258389785; yyh_1023@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: December 16, 2021
• First Posted: January 5, 2022
• Study Start: January 1, 2021
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: January 20, 2022

Record last verified: 2022-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)