Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans

Study Overview
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Brief Summary
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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.

Official Title
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In Silico Clinical Trial Comparing the Reading Accuracy of Doctors and a Deep Learning Algorithm for Detection of Metastatic Bone Disease on Bone Scintigraphy Scans.

Conditions
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Metastatic Bone Tumor

Intervention / Treatment
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* Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| BS-UKA<br>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. | Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.<br>* The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.<br>|
| BS-Namur<br>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. | Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.<br>* The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.<br>|
| BS-Aalborg<br>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. | Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.<br>* The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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 | 

Secondary Outcome Measures

| Outcome Measure | 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 |