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Validation of a Multitask Deep Learning System at Spine Metastasis CT

Conditions
To Evaluate Performance of the DLS
Interventions
Diagnostic Test: Deep Learning System
Registration Number
NCT05156567
Lead Sponsor
Shanghai 6th People's Hospital
Brief Summary

The multitask deep learning system (DLS) with five algorithms detecting five quantitative factors of Spinal Instability Neoplastic Score (SINS) was developed. Radiologists and oncologists from multicenter will be recruited to read the CT scans in picture archiving and communication system (PACS) independently, comparing with the DLS. One month after reading the CT scans in PACS, the participants will also asked to perform a web-based test in the DLS website using the same CT scans. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the DLS were calculated with professional graders as the reference standard.

Detailed Description

Not available

Recruitment & Eligibility

Status
UNKNOWN
Sex
All
Target Recruitment
420
Inclusion Criteria
  1. pathology-proven diagnosis of solid tumor;
  2. spinal CT scan indicating spinal metastasis with at least one lesion;
  3. no previous surgery for spinal metastasis
Exclusion Criteria
  1. spinal CT scans with no sagittal reconstruction;
  2. the radiologist considered that the quality of CT image was unqualified.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
DLSDeep Learning System-
Primary Outcome Measures
NameTimeMethod
AUC1 months

Area under the receiver operating characteristic curve (AUC) of spinal instability detection

Secondary Outcome Measures
NameTimeMethod
sensitivity1 months

sensitivity of spinal instability detection

specificity1 months

specificity of spinal instability detection

Trial Locations

Locations (1)

Shanghai Sixth People's Hospital

🇨🇳

Shanghai, China

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