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

• Conditions: Neuroblastoma; Wilms' Tumor; Lymphoma; Germ Cell Tumor; Teratoma; Sarcoma
• Interventions: Diagnostic Test: Radiomic Algorithm

• Registration Number: NCT05179850
• Lead Sponsor: West China Hospital

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.

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 Timeline

• Study Start: 2021-01-01
• Study First Submitted: 2021-12-16
• Study First Submitted QC: 2021-12-16
• Status Verified: 2022-01
• Last Update Submitted: 2022-01-04
• Study First Posted: 2022-01-05
• Last Update Posted: 2022-01-20
• Primary Completion: 2023-12-31
• Study Completion: 2023-12-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 400

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Prospective cohort	Radiomic Algorithm	The same inclusion/exclusion criteria were applied for the same center prospectively. It is a external validation cohort.
Retrospective cohort	Radiomic Algorithm	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.

Primary Outcome Measures

Name	Time	Method
Pathological tumor diagnosis	Baseline	The diagnosis is defined by histopathological specimens from surgery and/or biopsy.

Trial Locations

Locations (1)

West China Hospital, Sichuan University; 🇨🇳 Chengdu, Sichuan, China