Clinical trial NCT05179850: Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children
Official title: Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Wilms' Tumor
Neuroblastoma
Teratoma
Lymphoma
Sarcoma
Germ Cell Tumor
Lead sponsor: West China Hospital
Sponsor class: OTHER
First posted: 2022-01-05
Enrollment: 400
Sex: ALL
Minimum age (years): 0
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
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.

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.

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

Primary outcomes:
measure: Pathological tumor diagnosis
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Chengdu
country: China
facility: West China Hospital, Sichuan University
state: Sichuan
Study officials:
References: