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

NCT06685458 | Not Yet Recruiting

• 1 other identifier: KYLX2024-271
• Study Type: observational
• Target: 320
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Lung Cancer; Solid Pulmonary Nodules; Pulmonary Nodules

Keywords

Lung Cancer Screening; Nodule Malignancy Prediction; Preoperative Imaging Analysis

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of predictive model

  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

Study Arms (2)

Benign Nodule Group

Participants with benign solid pulmonary nodules.

Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling

Malignant Nodule Group

Participants with malignant solid pulmonary nodules.

Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling

Interventions

Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling OTHER

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.

Benign Nodule Group; Malignant Nodule Group

Eligibility Criteria

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

You may qualify if:

• (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.

You may not qualify if:

• (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.

Sponsors & Collaborators

• The Third Affiliated Hospital of Kunming Medical College.: lead

Related Publications (15)

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MeSH Terms

Conditions

Lung Neoplasms; Multiple Pulmonary Nodules

Condition Hierarchy (Ancestors)

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

Central Study Contacts

yantao yang

CONTACT

18288509115; 2443804244@qq.com

lianhua ye

CONTACT

13769123627; Lhye1204@aliyun.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Physician

Study Record Dates

• First Submitted: November 10, 2024
• First Posted: November 12, 2024
• Study Start: November 15, 2024
• Primary Completion: January 30, 2025
• Study Completion: February 20, 2025
• Last Updated: November 12, 2024

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will not share