Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans

NCT05110430 | Completed DMC

• 1 other identifier: MBDDL
• Study Type: observational
• Target: 2,365
• Locations: 1 country
• Sites: 1

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.

Conditions

Metastatic Bone Tumor

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Comparing the classification performance of the DL algorithm to that of physicians

  June 2021

Study Arms (3)

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.

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.

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.

Interventions

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.

BS-Aalborg; BS-Namur; BS-UKA

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Any patient who had an indication for undergoing bone scintigraphy in any of the participating centers.

You may qualify if:

• Patients who underwent a bone scintigraphy scan that is available with the radiologic report between 2010-2018

You may not qualify if:

• The lack of a bone scan, or corresponding radiologic report

Sponsors & Collaborators

• Maastricht University: lead
• Aalborg University Hospital: collaborator
• Centre Hospitalier Universitaire de Liege: collaborator
• University Hospital, Aachen: collaborator
• University of Namur: collaborator

Study Sites (1)

Maastricht University

Maastricht, Limburg, 6229ER, Netherlands

Location

Related Publications (1)

• 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.

  PMID: 36698217 RESULT

MeSH Terms

Conditions

Bone Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Bone Diseases; Musculoskeletal Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 19, 2021
• First Posted: November 8, 2021
• Study Start: March 10, 2021
• Primary Completion: December 30, 2021
• Study Completion: December 31, 2021
• Last Updated: March 20, 2023

Record last verified: 2023-03

Locations

NL (1)