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

Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorThe Third Affiliated Hospital of Kunming Medical College.

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

(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.

Disqualifiers

(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.

Trial 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

Trial design

Design model

Case-control

Time perspective

Retrospective

Treatments tested in this trial

  • Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling

    Other intervention

    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.

Treatment groups

320 Participants
are divided into 2 treatment groups
Group A: Benign Nodule Group1 intervention
Group B: Malignant Nodule Group1 intervention

Trial outcomes

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.

Time frame
Within 2 years after surgical resection and pathological confirmation

Secondary outcomes

Other outcomes

Locations

This trial has no locations.

Sponsors and contacts

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