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

NCT ID: NCT05179850

Last Updated: 2022-01-20

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 400 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-01-01
• Study Completion Date: 2023-12-31

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.

Conditions

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Study Groups

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.

Radiomic Algorithm

Intervention Type: DIAGNOSTIC_TEST

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.

Radiomic Algorithm

Intervention Type: DIAGNOSTIC_TEST

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

Interventions

Radiomic Algorithm

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 0 Years
• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

West China Hospital

OTHER

Sponsor Role: lead

Responsible Party

Yuhan Yang

Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

West China Hospital, Sichuan University

Chengdu, Sichuan, China

Site Status: RECRUITING

Countries

China

Central Contacts

Yuhan Yang, MD

Role: CONTACT

yyh_1023@163.com

8613258389785

Facility Contacts

Yuhan Yang, MD

Role: primary

yyh_1023@163.com

8613258389785

Other Identifiers

HX-2021477

Identifier Type: -

Identifier Source: org_study_id