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

NCT04241614 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 626

Last updated 2022-06-30

No results posted yet for this study

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

Interventions

OTHER

No interventions

No interventions

Sponsors & Collaborators

  • The First Hospital of Jilin University

    collaborator OTHER

  • Neusoft Medical Systems Co., Ltd.

    collaborator UNKNOWN

  • Chinese Academy of Sciences

    lead OTHER_GOV

Principal Investigators

  • Yali Zang, Ph.D. · Institute of Automation, Chinese Academy of Sciences

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-04-15
Primary Completion
2022-06-30
Completion
2022-06-30

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04241614 on ClinicalTrials.gov