Clinical trial · Observational

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

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

NCT06685458CI-TRIAL-00082478not yet recruitingClinicalTrials.gov clinicaltrialsPublishedExplore in graph →

Summary

Brief summary (as posted)

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

Conditions (3)

Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.

Condition (as posted)	Mapped entity	Match	Confidence
Lung Cancer	Malignant Lung Neoplasm	CURATED_EXACT	0.92
Pulmonary Nodules	—	UNRESOLVED	—
Solid Pulmonary Nodules	—	UNRESOLVED	—

Interventions

Interventions (1)

Intervention	Type	Mapped drug	Match
Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling	Other	—	UNRESOLVED

Design

Arms and outcomes

Arms (2)

label
Benign Nodule Group
description
Participants with benign solid pulmonary nodules.
interventionNames
1. Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling
label
Malignant Nodule Group
description
Participants with malignant solid pulmonary nodules.
interventionNames
1. Other: Preoperative Clinical and Imaging Feature Evaluation for Predictive Modeling

Primary outcomes (1)

measure
Diagnostic performance of predictive model
timeFrame
Within 2 years after surgical resection and pathological confirmation
description
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.

Eligibility

Eligibility (as posted)

Sex: All
Minimum age: 18 Years

Show eligibility criteria text

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.

References

Publications (15)

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