Automated 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.

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Key information

Conditions

Metastatic Bone Tumor Bone Diseases Bone Neoplasms Musculoskeletal Diseases Neoplasms Neoplasms by Site

Sex eligibility

All sexes

Study type

Observational

Primary location

Maastricht University

Maastricht, Limburg, 6229ER, Netherlands

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

The classification performance of DL algorithm compared to the ground truth

Time frame: June 2021

Reporting the performance measures (Area under the curve, accuracy, specificity..etc)

Secondary outcomes

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

Time frame: 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

Sponsors and collaborators

Lead sponsor

Maastricht University

Other

Collaborators

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

Registry information

Official study 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.

Important dates

Study start: 2021
Primary completion: 2021
Study completion: 2021
First posted: Nov 8, 2021
Registry last updated: Mar 20, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed