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

Completed

• Conditions: Lung Cancer; Image, Body
• Interventions: Other: No interventions

• Registration Number: NCT04241614
• Lead Sponsor: 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.

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 Timeline

• Study Start: 2019-04-15
• Study First Submitted: 2020-01-23
• Study First Submitted QC: 2020-01-23
• Study First Posted: 2020-01-27
• Status Verified: 2022-06
• Last Update Submitted: 2022-06-29
• Primary Completion: 2022-06-30
• Study Completion: 2022-06-30
• Last Update Posted: 2022-06-30

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 626

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

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

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

The First Hospital of Ji Lin University; 🇨🇳 Changchun, Jilin, China