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Classification of Benign and Malignant Lung Nodules Based on CT Raw Data

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ClinicalTrials.gov Identifier: NCT04241614
Recruitment Status : Recruiting
First Posted : January 27, 2020
Last Update Posted : January 28, 2020
Sponsor:
Collaborators:
First Hospital of Jilin University
Neusoft Medical Systems Co., Ltd.
Information provided by (Responsible Party):
Di Dong, Chinese Academy of Sciences

Brief 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. 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.

Condition or disease Intervention/treatment
Lung Cancer Image, Body Other: No interventions

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.

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Study Type : Observational
Estimated Enrollment : 400 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Comparison and Analysis of Predictive Performance of CT and Raw Data in Benign and Malignant Classification of Pulmonary Nodules
Actual Study Start Date : April 15, 2019
Estimated Primary Completion Date : February 15, 2020
Estimated Study Completion Date : April 15, 2024

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
The First Hospital of Ji Lin University
CT data and corresponding CT raw data of patients with lung nodule will be collected.
Other: No interventions
No interventions




Primary Outcome Measures :
  1. 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.

  2. 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.

  3. 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.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
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.
Criteria

Inclusion Criteria:

  1. Patients who are screened out lung nodule.
  2. The CT data and corresponding CT raw data are available before the surgery.
  3. Final pathology diagnosis of the malignancy of the nodule is available.

Exclusion Criteria:

  1. Previous history of lung malignancies.
  2. Artifacts on CT images seriously deteriorating the observation of the lesion.
  3. The time interval between CT scan and pathology diagnosis is more than 4 weeks.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04241614


Contacts
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Contact: Di Dong, Ph.D. +86 13811833760 di.dong@ia.ac.cn

Locations
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China, Jilin
The First Hospital of Ji Lin University Recruiting
Changchun, Jilin, China, 130021
Contact: Di Dong, Ph.D.    +86 13811833760    di.dong@ia.ac.cn   
Sponsors and Collaborators
Chinese Academy of Sciences
First Hospital of Jilin University
Neusoft Medical Systems Co., Ltd.
Investigators
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Study Director: Yali Zang, Ph.D. Institute of Automation, Chinese Academy of Sciences
Additional Information:
Publications:
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Responsible Party: Di Dong, Associate Researcher, Chinese Academy of Sciences
ClinicalTrials.gov Identifier: NCT04241614    
Other Study ID Numbers: CASMI001
First Posted: January 27, 2020    Key Record Dates
Last Update Posted: January 28, 2020
Last Verified: January 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Di Dong, Chinese Academy of Sciences:
radiomics
lung cancer
classification
CT
raw data
Additional relevant MeSH terms:
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Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases