An observational study in Lung Cancer and Image, Body, sponsored by Chinese Academy of Sciences. Completed at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2022-06-30.
Sponsored by Chinese Academy of Sciences · Observational
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.
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.
7,243 studies on the registry are indexed under Lung Neoplasms; 1,557 are open to participants now.
This study's enrollment of 626 is above the median of 189 across 1,514 observational studies indexed under Lung Neoplasms.
Browse Lung Neoplasms studies →Chinese Academy of Sciences is the lead sponsor of 65 studies on the registry; 13 are open to participants now.
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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.
Exclusion Criteria:
CT data and corresponding CT raw data of patients with lung nodule will be collected.
Other: No interventions
No interventions
Area under the receiver operating characteristic curve (ROC)
Area under curve (AUC) of raw data in discriminating malignant nodules from benign nodules.
Time frame: 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.
Time frame: 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.
Time frame: 5 years
Plan to share: Undecided
This study is completed, as verified in Jun 2022. You cannot join it, but the record below documents what was studied.
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Chinese Academy of Sciences