Deep Learning Model for Pure Solid Nodules Classification

Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study

The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer
• Intervention / Treatment: Diagnostic test: CT-based deep learning model

• Study Type: Observational
• Enrollment (Anticipated): 260

Contacts and Locations

Study Locations

• China
  • China, Gansu, China
    • Recruiting
    • Lanzhou
    • Contact: Minjie Ma, Dr; Phone Number: 021-65115006; Email: 721405952@qq.com
  • China, Guizhou, China
    • Recruiting
    • Zunyi
    • Contact: Yongxiang Song, Dr; Phone Number: 021-65115006; Email: zhaosurgery@163.com
  • China, Jiangxi, China
    • Recruiting
    • NanChang
    • Contact: Bentong Yu, Dr; Phone Number: 021-65115006; Email: 1151697503@qq.com
  • China, Zhejiang, China
    • Recruiting
    • Ningbo
    • Contact: Minglei Yang, Dr; Phone Number: 021-65115006; Email: almondjj@163.com
  • Shanghai
    • Yangpu, Shanghai, China
      • Recruiting
      • Shanghai Pulmonary Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients with pulmonary radiological pure-solid nodules with size less than 3cm

Description

Inclusion Criteria:

• Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;
• The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;
• Age ranging from 18-75 years;
• definied pathological examination report available;
• Obtained written informed consent.

Exclusion Criteria:

• Multiple lung lesions;
• Poor quality of CT images;
• Participants with incomplete clinical information;
• Participants who have received neoadjuvant therapy before initial CT evaluation.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUC	Area under the curve of the receiver operating characteristic	2022.01-2023.12

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy	Ratio of the number of correctly classified samples to the total number of samples	2022.01-2023.12
sensitivity	The probability of detecting a positive test in the population with the gold standard for disease (positive)	2022.01-2023.12
Specificity	Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard	2022.01-2023.12
PPV	Positive predictive value	2022.01-2023.12
NPV	Negative predictive value	2022.01-2023.12

Collaborators and Investigators

• Sponsor: Chang Chen
• Collaborators: Zunyi Medical College; Ningbo HwaMei Hospital, Zhejiang, China; The First Affiliated Hospital of Nanchang University, Jiangxi, China; The First Hospital of Lanzhou University, Gansu, China

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2022
• Primary Completion (Anticipated): December 31, 2023
• Study Completion (Anticipated): December 31, 2023

Study Registration Dates

• First Submitted: September 13, 2022
• First Submitted That Met QC Criteria: September 14, 2022
• First Posted (Actual): September 16, 2022

Study Record Updates

• Last Update Posted (Actual): September 16, 2022
• Last Update Submitted That Met QC Criteria: September 14, 2022
• Last Verified: September 1, 2022

More Information

Terms related to this study

• Keywords: PET-CT; deep learning model; pure-solid nodules
• Other Study ID Numbers: L21-022

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No