Lung CancerNCT04241614
Classification of Benign and Malignant Lung Nodules Based on CT Raw Data
An observational study of Lung Cancer and Image, Body, run by Chinese Academy of Sciences at 1 site in China.
Trial summary
The employ of medical images combined with deep neural networks to assist in clinical diagnosis, therapeutic effect, and prognosis prediction is nowadays a hotspot. However, all the existing methods are designed based on the reconstructed medical images rather than the lossless raw data. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Image, Body
- How many people
- 626 participants
- Where
- 1 siteChina
- Study dates
- 15 Apr 2019to 30 Jun 2022
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who are screened out lung nodules by CT will be included in this study. The golden standard is the pathologically confirmed malignance of the nodule.
Who may be able to join
- Patients who are screened out lung nodule.
- The CT data and corresponding CT raw data are available before the surgery.
- Final pathology diagnosis of the malignancy of the nodule is available.
Who cannot join
- Previous history of lung malignancies.
- Artifacts on CT images seriously deteriorating the observation of the lesion.
- The time interval between CT scan and pathology diagnosis is more than 4 weeks.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparison and Analysis of Predictive Performance of CT and Raw Data in Benign and Malignant Classification of Pulmonary Nodules
The employ of medical images combined with deep neural networks to assist in clinical diagnosis, therapeutic effect, and prognosis prediction is nowadays a hotspot. However, all the existing methods are designed based on the reconstructed medical images rather than the lossless raw data. Considering that medical images are intended for human eyes rather than the AI, we try to use raw data to predict the malignancy of pulmonary nodules and compared the predictive performance with CT. Experiments will prove the feasibility of diagnosis by CT raw data. We believe that the proposed method is promising to change the current medical diagnosis pipeline since it has the potential to free the radiologists.
Read the detailed description
The routinely used diagnostic scheme of cancers follows the process of signal-to-image-to-diagnosis. It is essential to reconstruct the visible images from the signal of medical device so that the human doctor can perform diagnosis. However, the huge amount of information inside the signal is not optimally mined, which causes the current unsatisfactory performance of image based diagnosis.
In this clinical trial, we will develop an AI based diagnostic scheme for lung nodules directly from the signal (raw data) to diagnosis, skipping the reconstruction step. In this trial, we will focus on the discrimination of malignant from benign lung nodules. We will collect a dataset of patients who are screened out lung nodules. All patients undergo preoperative CT scan (raw data and CT images available) and have pathologically confirmed result of the nodules. We will build a model using only raw data for diagnosis of the lung nodules. Moreover, another model from CT image will be built for comparison.
Furthermore, we will perform follow-up on these patients and build a model based on CT raw data for prognosis analysis of lung cancer.
- Conditions
- Lung Cancer, Image, Body
- Being tested
- No interventions
- Keywords
- radiomics, lung cancer, classification, CT, raw data
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionsOther
No interventions
| Study group | Role | What they receive |
|---|---|---|
| The First Hospital of Ji Lin University | CT data and corresponding CT raw data of patients with lung nodule will be collected. Receives: Other: No interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (ROC)
Area under curve (AUC) of raw data in discriminating malignant nodules from benign nodules.
Measured: 8 months
Disease free survival
The association between raw data and disease free survival (DFS), which defined as the time from the beginning of diagnosis of lung cancer to the confirmed time of recurrence or metastatic disease, or death occurred.
Measured: 5 years
Overal survival
The association between raw data and overall survival (OS), which defined as the time from the beginning of diagnosis of lung cancer to the death with any causes.
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jan 2020
- Study start15 Apr 2019
- Primary completion30 Jun 2022
- Study completion30 Jun 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 626 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chinese Academy of Sciences
- Sponsor type
- Other government
- Collaborators
- The First Hospital of Jilin University, Neusoft Medical Systems Co., Ltd.
- Responsible party
- Principal investigator, Di Dong, Chinese Academy of Sciences
- Investigators
- Yali Zang, Ph.D., Study director, Institute of Automation, Chinese Academy of Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04241614
- Sponsor's protocol ID
- CASMI001
- Status last verified
- Jun 2022
- First submitted
- 23 Jan 2020
- Last updated
- 30 Jun 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Kalra M, Wang G, Orton CG. Radiomics in lung cancer: Its time is here. Med Phys. 2018 Mar;45(3):997-1000. doi: 10.1002/mp.12685. Epub 2017 Dec 12. No abstract available.
PubMed 29159886
Study at a glance
From the registry record, last updated by the sponsor on 30 Jun 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison and Analysis of Predictive Performance of CT and Raw Data in Benign and Malignant Classification of Pulmonary Nodules
- ClinicalTrials.gov ID
- NCT04241614
- Acronym / sponsor ID
- CASMI001
- Conditions
- Lung Cancer, Image, Body
- Keywords
- radiomics, lung cancer, classification, CT, raw data
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 626 participants (actual)
- Lead sponsor
- Chinese Academy of SciencesOther government
- Collaborators
- The First Hospital of Jilin University, Neusoft Medical Systems Co., Ltd.
- Study start
- 15 Apr 2019 (actual)
- Primary completion
- 30 Jun 2022 (actual)
- Study completion
- 30 Jun 2022 (actual)
- First posted
- 27 Jan 2020 (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.
The employ of medical images combined with deep neural networks to assist in clinical diagnosis, therapeutic effect, and prognosis prediction is nowadays a hotspot. However, all the existing methods are designed based on the reconstructed medical images rather than the lossless raw data. Considering that medical images are intended for human eyes rather than the AI, we try to use raw data to predict the malignancy of pulmonary nodules and compared the predictive performance with CT. Experiments will prove the feasibility of diagnosis by CT raw data. We believe that the proposed method is promising to change the current medical diagnosis pipeline since it has the potential to free the radiologists.
Detailed description
The routinely used diagnostic scheme of cancers follows the process of signal-to-image-to-diagnosis. It is essential to reconstruct the visible images from the signal of medical device so that the human doctor can perform diagnosis. However, the huge amount of information inside the signal is not optimally mined, which causes the current unsatisfactory performance of image based diagnosis.
In this clinical trial, we will develop an AI based diagnostic scheme for lung nodules directly from the signal (raw data) to diagnosis, skipping the reconstruction step. In this trial, we will focus on the discrimination of malignant from benign lung nodules. We will collect a dataset of patients who are screened out lung nodules. All patients undergo preoperative CT scan (raw data and CT images available) and have pathologically confirmed result of the nodules. We will build a model using only raw data for diagnosis of the lung nodules. Moreover, another model from CT image will be built for comparison.
Furthermore, we will perform follow-up on these patients and build a model based on CT raw data for prognosis analysis of lung cancer.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| The First Hospital of Ji Lin University | – | Other: No interventionsCT data and corresponding CT raw data of patients with lung nodule will be collected. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who are screened out lung nodules by CT will be included in this study. The golden standard is the pathologically confirmed malignance of the nodule.
Key inclusion criteria (3 listed)
- Patients who are screened out lung nodule.
- The CT data and corresponding CT raw data are available before the surgery.
- Final pathology diagnosis of the malignancy of the nodule is available.
Key exclusion criteria (3 listed)
- Previous history of lung malignancies.
- Artifacts on CT images seriously deteriorating the observation of the lesion.
- The time interval between CT scan and pathology diagnosis is more than 4 weeks.
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 |
|---|---|---|
| No interventionsNo interventions | Other | The First Hospital of Ji Lin University |
Primary outcome
Area under the receiver operating characteristic curve (ROC)
Time frame: 8 months
Area under curve (AUC) of raw data in discriminating malignant nodules from benign nodules.
Disease free survival
Time frame: 5 years
The association between raw data and disease free survival (DFS), which defined as the time from the beginning of diagnosis of lung cancer to the confirmed time of recurrence or metastatic disease, or death occurred.
Overal survival
Time frame: 5 years
The association between raw data and overall survival (OS), which defined as the time from the beginning of diagnosis of lung cancer to the death with any causes.
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 30 Jun 2022.
- Background: PubMed 29159886Kalra M, Wang G, Orton CG. Radiomics in lung cancer: Its time is here. Med Phys. 2018 Mar;45(3):997-1000. doi: 10.1002/mp.12685. Epub 2017 Dec 12. No abstract available.
- Lung CancerLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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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.
Yali Zang, Ph.D. study director
Institute of Automation, Chinese Academy of Sciences
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 Lung Cancer and Image, Body, run by Chinese Academy of Sciences at 1 site in China.
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 3 inclusion and 3 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 30 Jun 2022. 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 China. 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?
Area under the receiver operating characteristic curve (ROC) (time frame: 8 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04241614, which also lists 1 linked publication.
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 30 Jun 2022.