Classification of Benign and Malignant Lung Nodules Based on CT Raw Data
Study Details
Study Description
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 | Phase |
|---|---|---|
|
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
Arms and Interventions
| Arm | 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
|
Outcome Measures
Primary Outcome Measures
- Area under the receiver operating characteristic curve (ROC) [8 months]
Area under curve (AUC) of raw data in discriminating malignant nodules from benign nodules.
- Disease free survival [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 [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.
Eligibility Criteria
Criteria
Inclusion Criteria:
-
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.
Exclusion Criteria:
-
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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | The First Hospital of Ji Lin University | Changchun | Jilin | China | 130021 |
Sponsors and Collaborators
- Chinese Academy of Sciences
- The First Hospital of Jilin University
- Neusoft Medical Systems Co., Ltd.
Investigators
- Study Director: Yali Zang, Ph.D., Institute of Automation, Chinese Academy of Sciences
Study Documents (Full-Text)
None provided.More Information
Additional Information:
Publications
- CASMI001