- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04022512
Accuracy of Deep-learning Algorithm for Detection and Risk Stratification of Lung Nodules
February 6, 2024 updated by: Professor Winnie W.C. Chu, Chinese University of Hong Kong
Feasibility Study: Accuracy and Sensitivity of Deep-learning Artificial Intelligence (AI) Algorithm for Detection and Risk Stratification of Lung Nodules in Osteogenic Sarcoma Patients
Osteosarcoma is regarded as most common malignant bone tumor in children and adolescents.
Approximately 15% to 20% of patients with osteosarcoma present with detectable metastatic disease, and the majority of whom (85%) have pulmonary lesions as the sole site of metastasis.
Previous studies have shown that the overall survival rate among patients with localized osteosarcoma without metastatic disease is approximately 60% to 70% whereas survival rate reduces to 10% to 30% in patients with metastatic disease.
Though lately, neoadjuvant and adjuvant chemotherapeutic regimens can decline the mortality rate, 30% to 50% of patients still die of pulmonary metastases.
Number, distribution and timing of lung metastases are of prognostic value for survival and hence computed tomography (CT) thorax imaging still plays a vital role in disease surveillance.
In the last decade, the technology of multidetector CT scanner has enhanced the detection of numerous smaller lung lesions, which on one hand can increase the diagnostic sensitivity for lung metastasis, however, the specificity may be reduced.
In recent years, deep-learning artificial intelligence (AI) algorithm in a wide variety of imaging examinations is a hot topic.
Currently, an increasing number of Computer-Aided Diagnosis (CAD) systems based on deep learning technologies aiming for faster screening and correct interpretation of pulmonary nodules have been rapidly developed and introduced into the market.
So far, the researches concentrating on the improving the accuracy of benign/malignant nodule classification have made substantial progress, inspired by tremendous advancement of deep learning techniques.
Consequently, the majority of the existing CAD systems can perform pulmonary nodule classification with accuracy of 90% above.
In clinical practice, not only the malignancy determination for pulmonary nodule, but also the distinction between primary carcinoma and intrapulmonary metastasis is crucial for patient management.
However, most existing classification of pulmonary nodule applied in CAD system remains to be binary pattern (benign Vs malignant), in the lack of more thorough nodule classification characterized with splitting of primary and metastatic nodule.
To the best of our knowledge, only a few studies have focuses on the performance of deep learning-based CAD system for identifying metastatic pulmonary nodule till now.
In this proposed study, the investigators sought to determine the accuracy and sensitivity of one computer-aided system based on deep-learning artificial intelligence algorithm for detection and risk stratification of lung nodules in osteogenic sarcoma patients.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Study Type
Observational
Enrollment (Actual)
100
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
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Shatin
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Hong Kong, Shatin, Hong Kong
- The Chinese University of Hong Kong, Prince of Wale Hospital
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
No older than 18 years (Child, Adult)
Accepts Healthy Volunteers
N/A
Sampling Method
Probability Sample
Study Population
This is a single institutional retrospective cohort study of patients diagnosed with osteogenic sarcoma between the year 2000 and 2018.
All patients' data will be retrieved via the electronic patient database of our institution.
Patient demographics, imaging and histological data, disease and treatment history will be recorded, including age at onset, details of chemotherapy, time interval of pulmonary metastasis from diagnosis, surgery for the primary bony tumor, subsequent pulmonary metastatectomy if performed, the length of survival, clinical outcome and so on.
Description
Inclusion Criteria:
- Patients with histologically confirmed osteogenic sarcoma
- With an age younger than 18 years old.
- Patients who underwent thin-section thoracic CT examinations for pre-treatment staging and/or subsequent post-treatment follow-up.
- With suspicious lung nodules detected on thoracic CT images.
Exclusion Criteria:
- Patients with concurring lesions that may influence analysis of lung nodules.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
accuracy
Time Frame: 3 years
|
proportion of true results(both true positives and true negatives) among whole instances
|
3 years
|
sensitivity
Time Frame: 3 years
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true positive rate in percentage(%) derived by ROC analysis
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3 years
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specificity
Time Frame: 3 years
|
true negative rate in percentage (%) derived by ROC analysis
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3 years
|
area under curve (AUC)
Time Frame: 3 years
|
area under ROC curve in percentage (%)
|
3 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
average number of false positives per scan (FPs/scan)
Time Frame: 3 years
|
FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis
|
3 years
|
competition performance metric (CPM)
Time Frame: 3 years
|
Competitive performance metric (CPM) is a criterion used for CAD system evaluation.
Based on FROC paradigm, CPM score is computed as an average sensitivity at seven predefined average false positive rates.
CPM score ranges from 0 to 1, with higher CPM score indicating better CAD performance.
|
3 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
November 6, 2019
Primary Completion (Actual)
August 1, 2023
Study Completion (Actual)
January 31, 2024
Study Registration Dates
First Submitted
July 15, 2019
First Submitted That Met QC Criteria
July 15, 2019
First Posted (Actual)
July 17, 2019
Study Record Updates
Last Update Posted (Actual)
February 7, 2024
Last Update Submitted That Met QC Criteria
February 6, 2024
Last Verified
February 1, 2024
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 2019.421
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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