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| Name | Class |
|---|---|
| Aalborg University Hospital | OTHER |
| Centre Hospitalier Universitaire de Liege | OTHER |
| University Hospital, Aachen | OTHER |
| University of Namur |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| BS-UKA | Patients who underwent bone scintigraphy scanning between 2010 and 2018 at RTWH Aachen university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. |
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| BS-Namur | Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Namur university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. |
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| BS-Aalborg | Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Aalborg university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Deep learning based detection of metastatic bone disease on bone scintigraphy scans. | Other | The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity. |
| Measure | Description | Time Frame |
|---|---|---|
| The classification performance of DL algorithm compared to the ground truth | Reporting the performance measures (Area under the curve, accuracy, specificity..etc) | June 2021 |
| Measure | Description | Time Frame |
|---|---|---|
| Comparing the classification performance of the DL algorithm to that of physicians | 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 | June 2021 |
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Inclusion Criteria:
Exclusion Criteria:
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Any patient who had an indication for undergoing bone scintigraphy in any of the participating centers.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Maastricht University | Maastricht | Limburg | 6229ER | Netherlands |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36698217 | Result | Ibrahim A, Vaidyanathan A, Primakov S, Belmans F, Bottari F, Refaee T, Lovinfosse P, Jadoul A, Derwael C, Hertel F, Woodruff HC, Zacho HD, Walsh S, Vos W, Occhipinti M, Hanin FX, Lambin P, Mottaghy FM, Hustinx R. Deep learning based identification of bone scintigraphies containing metastatic bone disease foci. Cancer Imaging. 2023 Jan 25;23(1):12. doi: 10.1186/s40644-023-00524-3. |
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| ID | Term |
|---|---|
| D001859 | Bone Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001847 | Bone Diseases |
| D009140 | Musculoskeletal Diseases |
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| OTHER |
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