Clinical trial NCT04241614: Classification of Benign and Malignant Lung Nodules Based on CT Raw Data
Official title: Comparison and Analysis of Predictive Performance of CT and Raw Data in Benign and Malignant Classification of Pulmonary Nodules
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Lung Cancer
Image, Body
Keywords: radiomics
lung cancer
classification
CT
raw data
Lead sponsor: Chinese Academy of Sciences
Sponsor class: OTHER_GOV
Collaborators: name: The First Hospital of Jilin University
name: Neusoft Medical Systems Co., Ltd.
First posted: 2020-01-27
Enrollment: 626
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

Eligibility 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.

Primary outcomes:
measure: Area under the receiver operating characteristic curve (ROC)
timeFrame: 8 months
measure: Disease free survival
timeFrame: 5 years
measure: Overal survival
timeFrame: 5 years
Secondary outcomes:
Other outcomes:
Locations:
city: Changchun
country: China
facility: The First Hospital of Ji Lin University
state: Jilin
Study officials:
affiliation: Institute of Automation, Chinese Academy of Sciences
name: Yali Zang, Ph.D.
role: STUDY_DIRECTOR
References:
citation: Kalra M, Wang G, Orton CG. Radiomics in lung cancer: Its time is here. Med Phys. 2018 Mar;45(3):997-1000. doi: 10.1002/mp.12685. Epub 2017 Dec 12. No abstract available.