Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans

Sponsor

Maastricht University (Other)

Overall Status

Active, not recruiting

CT.gov ID

NCT05110430

Collaborator

Aalborg University Hospital (Other), Centre Hospitalier Universitaire de Liege (Other), University Hospital, Aachen (Other), University of Namur (Other)

2,000

Enrollment

Location

9.7

Anticipated Duration (Months)

205.7

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Metastatic Bone Tumor	Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.

Study Design

Study Type:

Observational

Anticipated Enrollment :

2000 participants

Observational Model:

Cohort

Time Perspective:

Retrospective

Official Title:

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.

Actual Study Start Date :

Mar 10, 2021

Anticipated Primary Completion Date :

Dec 30, 2021

Anticipated Study Completion Date :

Dec 31, 2021

Arms and Interventions

Arm	Intervention/Treatment
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.	Other: Deep 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.
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.	Other: Deep 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.
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.	Other: Deep 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.

Arm

Intervention/Treatment

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.

Other: Deep 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.

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.

Other: Deep 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.

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.

Other: Deep 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.

Outcome Measures

Primary Outcome Measures

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 Outcome Measures

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

Eligibility Criteria

Criteria

Ages Eligible for Study:

N/A and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Maastricht University	Maastricht	Limburg	Netherlands	6229ER

Sponsors and Collaborators

Maastricht University
Aalborg University Hospital
Centre Hospitalier Universitaire de Liege
University Hospital, Aachen
University of Namur

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Maastricht University

ClinicalTrials.gov Identifier:

NCT05110430

Other Study ID Numbers:

MBDDL

First Posted:

Nov 8, 2021

Last Update Posted:

Nov 8, 2021

Last Verified:

Apr 1, 2021

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Additional relevant MeSH terms:

Bone Diseases

Study Results

No Results Posted as of Nov 8, 2021