Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans

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.

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.

Study Overview

• Status: Completed
• Conditions: Metastatic Bone Tumor
• Intervention / Treatment: Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.

• Study Type: Observational
• Enrollment (Actual): 2365

Contacts and Locations

Study Locations

• Netherlands
  • Limburg
    • Maastricht, Limburg, Netherlands, 6229ER
      • Maastricht University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	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.

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Maastricht University
• Collaborators: Aalborg University Hospital; Centre Hospitalier Universitaire de Liege; University Hospital, Aachen; University of Namur

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): March 10, 2021
• Primary Completion (Actual): December 30, 2021
• Study Completion (Actual): December 31, 2021

Study Registration Dates

• First Submitted: April 19, 2021
• First Submitted That Met QC Criteria: October 25, 2021
• First Posted (Actual): November 8, 2021

Study Record Updates

• Last Update Posted (Actual): March 20, 2023
• Last Update Submitted That Met QC Criteria: March 16, 2023
• Last Verified: March 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Bone Diseases
• Other Study ID Numbers: MBDDL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No