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
- pathology-proven diagnosis of solid tumor;
- spinal CT scan indicating spinal metastasis with at least one lesion;
- no previous surgery for spinal metastasis
- spinal CT scans with no sagittal reconstruction;
- the radiologist considered that the quality of CT image was unqualified.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description DLS Deep Learning System -
- Primary Outcome Measures
Name Time Method AUC 1 months Area under the receiver operating characteristic curve (AUC) of spinal instability detection
- Secondary Outcome Measures
Name Time Method sensitivity 1 months sensitivity of spinal instability detection
specificity 1 months specificity of spinal instability detection
Trial Locations
- Locations (1)
Shanghai Sixth People's Hospital
🇨🇳Shanghai, China