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

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.

Study Overview

• Status: Recruiting
• Conditions: Sarcoma; Lymphoma; Neuroblastoma; Wilms' Tumor; Teratoma; Germ Cell Tumor
• Intervention / Treatment: Diagnostic test: Radiomic Algorithm

Detailed Description

The retroperitoneal space extends from the lumbar region to the pelvic region and houses vital structures such as the kidney, the ureter, the adrenal glands, the pancreas, the aorta and its branches, the inferior vena cava and its tributaries, lymph nodes, and loose connective tissue meshwork along with fat. This space thus allows the silent growth of primary and metastatic tumors, such that clinical features appear often too late. The therapeutic regimen differs on various types of retroperitoneal tumor in children. It is damaging for pediatric patients to acquire histological specimens through invasive procedures. Hence, an urgent evaluation is absolutely necessary for preoperative diagnosis in such cases via noninvasive approaches. 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 computed tomography images from June 2010 and December 2020. The investigators have constructed deep learning and machine learning radiomics diagnostic models on this retrospective cohort and validated it internally. A prospective cohort would recruit infantile patients diagnosed as retroperitoneal tumor since January 2021. The proposed deep learning model 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 retroperitoneal tumor using machine learning and deep learning techniques on computed tomography images in children.

• Study Type: Observational
• Enrollment (Anticipated): 400

Contacts and Locations

Study Locations

• China
  • Sichuan
    • Chengdu, Sichuan, China, 6100041
      • Recruiting
      • West China Hospital, Sichuan University
      • Principal Investigator: Yuhan Yang, MD

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 had retroperitoneal tumor and completed the abdominal computed tomography examination 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 computed tomography 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: Radiomic Algorithm; Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.
Prospective cohort; The same inclusion/exclusion criteria were applied for the same center prospectively. It is a external validation cohort.	Diagnostic test: Radiomic Algorithm; Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Pathological tumor diagnosis	The diagnosis is defined by histopathological specimens from surgery and/or biopsy.	Baseline

Collaborators and Investigators

• Sponsor: West China Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2021
• Primary Completion (Anticipated): December 31, 2023
• Study Completion (Anticipated): December 31, 2023

Study Registration Dates

• First Submitted: December 16, 2021
• First Submitted That Met QC Criteria: December 16, 2021
• First Posted (Actual): January 5, 2022

Study Record Updates

• Last Update Posted (Actual): January 20, 2022
• Last Update Submitted That Met QC Criteria: January 4, 2022
• Last Verified: January 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Kidney Diseases; Urologic Diseases; Neoplasms, Glandular and Epithelial; Genetic Diseases, Inborn; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Nerve Tissue; Kidney Neoplasms; Neoplastic Syndromes, Hereditary; Neoplasms, Complex and Mixed; Neuroectodermal Tumors, Primitive; Neuroectodermal Tumors, Primitive, Peripheral; Neoplasms; Neoplasms, Germ Cell and Embryonal; Neuroblastoma; Wilms Tumor; Teratoma
• Other Study ID Numbers: HX-2021477

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