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

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.

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.