Osteogenic SarcomaNCT04022512
Accuracy of Deep-learning Algorithm for Detection and Risk Stratification of Lung Nodules
An observational study of Osteogenic Sarcoma, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial summary
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. Read more about this study
- Who is it for?
- Ages Up to 18 years
- Conditions the study is for
- Osteogenic Sarcoma
- What is being tested?
- computed tomography
- Type of treatment
- Surgery or procedure
- How many people
- 100 participants
- Where
- 1 siteHong Kong
- Study dates
- 6 Nov 2019to 31 Jan 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 18 years
- All sexes
- Child, Adult
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.
Who may be able to join
- 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.
Who cannot join
- Patients with concurring lesions that may influence analysis of lung nodules.
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site
The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin
About this study
Official title: 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.
- Conditions
- Osteogenic Sarcoma
- Being tested
- computed tomography
- Keywords
- Lung nodules, Deep learning, Artificial intelligence, Computer-aided diagnosis
- Medical subject headings
- Osteosarcoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
computed tomographyRadiation
thoracic CT examinations for pre-treatment staging and/or subsequent post-treatment follow-up.
What is being measured
The primary outcome is the main question the study is designed to answer.
accuracy
proportion of true results(both true positives and true negatives) among whole instances
Measured: 3 years
sensitivity
true positive rate in percentage(%) derived by ROC analysis
Measured: 3 years
specificity
true negative rate in percentage (%) derived by ROC analysis
Measured: 3 years
area under curve (AUC)
area under ROC curve in percentage (%)
Measured: 3 years
Secondary outcomes (2)
average number of false positives per scan (FPs/scan)
FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis
Measured: 3 years
competition performance metric (CPM)
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.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jul 2019
- Study start6 Nov 2019
- Primary completion1 Aug 2023
- Study completion31 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Osteogenic SarcomaStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- IBM China/Hong Kong Limited
- Responsible party
- Principal investigator, Professor Winnie W.C. Chu, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04022512
- Sponsor's protocol ID
- 2019.421
- Status last verified
- Feb 2024
- First submitted
- 15 Jul 2019
- Last updated
- 7 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Feasibility Study: Accuracy and Sensitivity of Deep-learning Artificial Intelligence (AI) Algorithm for Detection and Risk Stratification of Lung Nodules in Osteogenic Sarcoma Patients
- ClinicalTrials.gov ID
- NCT04022512
- Acronym / sponsor ID
- 2019.421
- Conditions
- Osteogenic Sarcoma
- Keywords
- Lung nodules, Deep learning, Artificial intelligence, Computer-aided diagnosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Collaborators
- IBM China/Hong Kong Limited
- Study start
- 6 Nov 2019 (actual)
- Primary completion
- 1 Aug 2023 (actual)
- Study completion
- 31 Jan 2024 (actual)
- First posted
- 17 Jul 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Age groups
- Child, Adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (1 listed)
- Patients with concurring lesions that may influence analysis of lung nodules.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| computed tomographythoracic CT examinations for pre-treatment staging and/or subsequent post-treatment follow-up. | Radiation | – |
Primary outcome
accuracy
Time frame: 3 years
proportion of true results(both true positives and true negatives) among whole instances
sensitivity
Time frame: 3 years
true positive rate in percentage(%) derived by ROC analysis
specificity
Time frame: 3 years
true negative rate in percentage (%) derived by ROC analysis
area under curve (AUC)
Time frame: 3 years
area under ROC curve in percentage (%)
2 secondary outcomes
average number of false positives per scan (FPs/scan)
Time frame: 3 years
competition performance metric (CPM)
Time frame: 3 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Feb 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Osteogenic Sarcoma, run by Chinese University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 7 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
accuracy (time frame: 3 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Feb 2024.