Metastatic Bone Tumor
Conditions
Brief summary
Bone scintigraphy scans are two dimensional medical images that are used heavily in nuclear medicine. The scans detect changes in bone metabolism with high sensitivity, yet it lacks the specificity to underlying causes. Therefore, further imaging would be required to confirm the underlying cause. The aim of this study is to investigate whether deep learning can improve clinical decision based on bone scintigraphy scans.
Interventions
OTHERDeep learning based detection of metastatic bone disease on bone scintigraphy scans.
The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.
Sponsors
Aalborg University Hospital
Centre Hospitalier Universitaire de Liege
University Hospital, Aachen
University of Namur
Maastricht University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Patients who underwent a bone scintigraphy scan that is available with the radiologic report between 2010-2018
Exclusion criteria
* The lack of a bone scan, or corresponding radiologic report
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The classification performance of DL algorithm compared to the ground truth | June 2021 | Reporting the performance measures (Area under the curve, accuracy, specificity..etc) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Comparing the classification performance of the DL algorithm to that of physicians | June 2021 | Correctness of the diagnosis of Dr versus AI (dichotomous variable: correct versus not correct) on a subset of the validation data, using a McNemar statistical test |
Countries
Netherlands
Outcome results
None listed