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

NCT04241614 | Completed DMC

• 1 other identifier: CASMI001
• Study Type: observational
• Target: 626
• Locations: 1 country
• Sites: 1

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.

Conditions

Lung Cancer; Image, Body

Keywords

radiomics; lung cancer; classification; CT; raw data

Outcome Measures

Primary Outcomes (3)

• Area under the receiver operating characteristic curve (ROC)

  Area under curve (AUC) of raw data in discriminating malignant nodules from benign nodules.

  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.

  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.

  5 years

Study Arms (1)

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

Interventions

No interventions OTHER

No interventions

The First Hospital of Ji Lin University

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Chinese Academy of Sciences: lead
• The First Hospital of Jilin University: collaborator
• Neusoft Medical Systems Co., Ltd.: collaborator

Study Sites (1)

The First Hospital of Ji Lin University

Changchun, Jilin, 130021, China

Location

Related Publications (1)

• 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.

  PMID: 29159886 BACKGROUND

Related Links

• Lung Cancer

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Yali Zang, Ph.D.

  Institute of Automation, Chinese Academy of Sciences

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Researcher

Study Record Dates

• First Submitted: January 23, 2020
• First Posted: January 27, 2020
• Study Start: April 15, 2019
• Primary Completion: June 30, 2022
• Study Completion: June 30, 2022
• Last Updated: June 30, 2022

Record last verified: 2022-06

Locations

CN (1)