Pathological Classification of Pulmonary Nodules in Images Using Deep Learning

January 23, 2022 updated by: Haiyu Zhou, Jiangxi Provincial Cancer Hospital

Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning

This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

All white-light photographs of pulmonary nodules from phones of pathologically confirmed adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were retrospectively collected from consecutive patients who underwent surgery between June 30, 2020 and September 15, 2021 at Guangdong Provincial People's Hospital.Finally, a total of 1037 white-light images from 973 individuals were included in the study. The entire dataset was divided into training and test datasets, which were mutually exclusive, using random sampling. Of these, 830 images were used as the training dataset and 104 images from were used as the test dataset. The CNN model was used in classifying images, namely, Resnet-50. For the CNN model, pretrained model with the ImageNet Dataset were adopted using transfer learning. After constructing the CNN models using the training dataset, the performance of the models was evaluated using the test dataset and the prospective validation dataset.

Study Type

Observational

Enrollment (Anticipated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510000
        • Recruiting
        • Guagndong Provincial People's Hospital
        • Contact:
    • Jiangxi
      • Nanchang, Jiangxi, China, 330000
        • Recruiting
        • Jiangxi Cancer Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients in Guangdong Provincial People's hospital from June 30, 2020 to September 15, 2021.

Description

Inclusion Criteria:

  1. Male or female，18 years and older.
  2. Patients haven't undergone any therapy.
  3. The pulmonary nodules were confirmed AIS, MIA or IAC.
  4. The sizes of pulmonary nodules were less than 3cm.
  5. The images were jpg format.

Exclusion Criteria:

  1. Suffering from other tumor disease before or at the same time.
  2. Images with poor quality or low resolution that precluded proper classification.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
1. Pathological subtype
Time Frame: through study completion, an average of 2 year
According to WHO classification of pulmonary tumors in 2020, this study classify pulmonary tumors into adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). We would collect the reports of pathological type of pulmonary nodules after surgery.
through study completion, an average of 2 year
Area Under the Curve (AUC)
Time Frame: through study completion, an average of 2 year
The area under the ROC curve based the predicton efficency of model
through study completion, an average of 2 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Jiangxi Provincial Cancer Hospital

Collaborators

Guangdong Provincial People's Hospital

Investigators

  • Principal Investigator: Haiyu Zhou, Guangdong Provincial People's Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 1, 2020

Primary Completion (Anticipated)

June 1, 2022

Study Completion (Anticipated)

January 1, 2023

Study Registration Dates

First Submitted

January 5, 2022

First Submitted That Met QC Criteria

January 23, 2022

First Posted (Actual)

February 3, 2022

Study Record Updates

Last Update Posted (Actual)

February 3, 2022

Last Update Submitted That Met QC Criteria

January 23, 2022

Last Verified

January 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2021ky228

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

Yes

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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