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

  • STATUS
    Recruiting
  • End date
    Dec 31, 2023
  • participants needed
    400
  • sponsor
    West China Hospital
Updated on 21 March 2022

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.

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.

Details
Condition Wilms' Tumor, Neuroblastoma, Teratoma, Lymphoma, Sarcoma, Germ Cell Tumor
Treatment Radiomic Algorithm
Clinical Study IdentifierNCT05179850
SponsorWest China Hospital
Last Modified on21 March 2022

Eligibility

Yes No Not Sure

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
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