Constructing a Predictive Model for Differentiating Between Benign and Malignant Solid Pulmonary Nodules Based on Clinical and Imaging Features.

November 10, 2024 updated by: Yantao Yang, The Third Affiliated Hospital of Kunming Medical College.

Combined With Clinical and Imaging Features, the Prediction Model of Benign and Malignant Solid Pulmonary Nodules Was Constructed

Study Objective:

To comprehensively analyze the preoperative clinical and imaging characteristics of solid pulmonary nodules, investigate the risk factors associated with malignant solid pulmonary nodules, and provide a reference for preoperative treatment decisions.

Significance of the Study:

According to the 2020 Global Cancer Report, lung cancer remains the leading cause of cancer-related deaths worldwide. While the majority of patients with stage I lung cancer achieve long-term survival, survival rates for advanced-stage patients are extremely low. Early screening, diagnosis, and treatment of lung cancer are crucial.

With the widespread implementation of early lung cancer screening, a growing number of pulmonary nodules are being detected, among which solid pulmonary nodules constitute a significant proportion. Unlike ground-glass nodules, accurately distinguishing between benign and malignant solid nodules is critical for determining appropriate treatment strategies. For benign solid nodules, follow-up observation is the preferred approach, whereas early surgical intervention is essential for malignant solid nodules.

Although previous studies have explored the correlation between clinical and imaging characteristics, they have not conducted systematic analyses, and most have been based on small sample sizes. Therefore, this study aims to conduct a comprehensive analysis of preoperative clinical and imaging characteristics, build a predictive model to differentiate between benign and malignant solid pulmonary nodules, and provide a reliable reference for selecting treatment strategies.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Our study evaluate patients with SPN from the Third Affiliated Hospital of Kunming Medical University. The patient selection followed specific inclusion and exclusion criteria. Inclusion criteria included: (1) All subjects provided CT imaging obtained from the Third Affiliated Hospital of Kunming Medical University within 2-week period prior to surgery; (2) Complete clinicopathological data of solid nodules were obtained; (3) Surgical intervention for one or more SPN; (4) No prior anti-tumor treatments like radiotherapy or chemotherapy; (5) Age 18 years or older. Exclusion criteria involved: (1) Patients with incomplete imaging data or medical records; (2) Lung infections that could affect image analysis; (3) Significant respiratory movement artifacts in images impairing imaging analysis; (4) Inconsistent locations of SPN in postoperative pathology reports and preoperative CT images.

Study Type

Observational

Enrollment (Estimated)

320

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

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

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population includes patients aged 18 years and older with radiologically diagnosed solid pulmonary nodules. These patients are undergoing preoperative clinical and imaging evaluations and subsequent surgical resection to confirm the benign or malignant nature of the nodules

Description

Inclusion Criteria:

  • (1) All subjects provided CT imaging obtained from the Third Affiliated Hospital of Kunming Medical University within 2-week period prior to surgery; (2) Complete clinicopathological data of solid nodules were obtained; (3) Surgical intervention for one or more SPN; (4) No prior anti-tumor treatments like radiotherapy or chemotherapy; (5) Age 18 years or older.

Exclusion Criteria:

  • (1) Patients with incomplete imaging data or medical records; (2) Lung infections that could affect image analysis; (3) Significant respiratory movement artifacts in images impairing imaging analysis; (4) Inconsistent locations of SPN in postoperative pathology reports and preoperative CT images.

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Benign Nodule Group
Participants with benign solid pulmonary nodules.
Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
This study involves preoperative evaluation of clinical and imaging features for constructing a predictive model to differentiate benign and malignant solid pulmonary nodules. Surgical resection is performed to obtain pathological confirmation as the reference standard.
Malignant Nodule Group
Participants with malignant solid pulmonary nodules.
Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
This study involves preoperative evaluation of clinical and imaging features for constructing a predictive model to differentiate benign and malignant solid pulmonary nodules. Surgical resection is performed to obtain pathological confirmation as the reference standard.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic performance of predictive model
Time Frame: Within 2 years after surgical resection and pathological confirmation
The primary outcome is the area under the receiver operating characteristic curve (AUC) of the predictive model in distinguishing benign from malignant solid pulmonary nodules, based on preoperative clinical and imaging features.
Within 2 years after surgical resection and pathological confirmation

Collaborators and Investigators

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

Sponsor

The Third Affiliated Hospital of Kunming Medical College.

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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 (Estimated)

November 15, 2024

Primary Completion (Estimated)

January 30, 2025

Study Completion (Estimated)

February 20, 2025

Study Registration Dates

First Submitted

November 10, 2024

First Submitted That Met QC Criteria

November 10, 2024

First Posted (Estimated)

November 12, 2024

Study Record Updates

Last Update Posted (Estimated)

November 12, 2024

Last Update Submitted That Met QC Criteria

November 10, 2024

Last Verified

November 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • KYLX2024-271

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

The datasets generated and/or analyzed during the current study are not publicly available due sharing data is not included in our research institution review board.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

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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