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

Status

Indicates the current recruitment status or the expanded access status.
Completed

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Actual)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
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

  • Hong Kong
    • Shatin
      • Hong Kong, Shatin, Hong Kong
        • The Chinese University of Hong Kong, Prince of Wale Hospital

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

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

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

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
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
true positive rate in percentage(%) derived by ROC analysis
3 years
specificity
Time Frame: 3 years
true negative rate in percentage (%) derived by ROC analysis
3 years
area under curve (AUC)
Time Frame: 3 years
area under ROC curve in percentage (%)
3 years

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
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

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Chinese University of Hong Kong

Collaborators

Collaborators

An organization other than the sponsor that provides support for a clinical study. This support may include activities related to funding, design, implementation, data analysis, or reporting.
IBM China/Hong Kong Limited

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)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
November 6, 2019

Primary Completion (Actual)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
August 1, 2023

Study Completion (Actual)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
January 31, 2024

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
July 15, 2019

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
July 15, 2019

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
July 17, 2019

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
February 7, 2024

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
February 6, 2024

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
February 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant 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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