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Updated: Jun 15, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Imaging features and clinical evaluation of pulmonary nodules in children
Muheremu Dilimulati1, Shuhua Yuan1, Hejun Jiang1
1Department of Respiratory Medicine, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Insights
Pediatric pulmonary nodules are increasingly detected. A new model using nodule size, distribution, and medical history helps predict malignancy in children, aiding diagnosis and management.
Area of Science:
- Pediatric Radiology
- Thoracic Oncology
- Medical Informatics
Background:
- Computed tomography (CT) use has increased pediatric pulmonary nodule detection.
- Lack of pediatric-specific guidelines necessitates improved diagnostic tools.
- This study aimed to provide a clinical reference for diagnosing and managing pediatric pulmonary nodules.
Purpose of the Study:
- To develop and validate a clinical assessment model for differentiating benign from malignant pulmonary nodules in children.
- To identify key predictors for pulmonary nodule malignancy in pediatric patients.
Main Methods:
- Retrospective analysis of 1341 pediatric patients with pulmonary nodules from April 2012 to July 2021.
- Categorization into tumor and non-tumor groups based on pre-CT diagnosis.
- Construction and validation of a decision tree model for malignancy prediction using clinical and imaging data.
Main Results:
- 1341 pediatric patients (average age 7.2 years) were analyzed; 51.7% had prior malignancies.
- Malignant nodules were associated with larger size, multiple nodules, and osteolytic lesions.
- The decision tree model identified nodule diameter (≥5mm), specific distributions, and malignancy history as key predictors (AUC=0.828).
Conclusions:
- A clinical assessment model effectively differentiates benign and malignant pediatric pulmonary nodules.
- Nodule diameter, distribution, and patient's malignancy history are significant predictors.
- The model provides a valuable tool for clinical decision-making in pediatric pulmonary nodule management.
Background:
With the widespread use of computed tomography (CT), the detection rate of pulmonary nodules in children has gradually increased. Due to the lack of epidemiological evidence and clinical guideline on pulmonary nodule treatment in children, we aimed to provide a reference for the clinical diagnosis and management of pediatirc pulmonary nodules.
Methods:
This retrospective study collected consecutive cases from April 2012 to July 2021 in the Shanghai Children's Medical Center. The sample included children with pulmonary nodules on chest CT scans and met the inclusion criteria. All patients were categorized into tumor and non-tumor groups by pre-CT clinical diagnosis. Nodule characteristics between groups were analyzed. To establish a clinical assessment model for the benign versus malignant pulmonary nodules, patients who have been followed-up for three months were detected and a decision tree model for nodule malignancy prediction was constructed and validated.
Results:
The sample comprised 1341 patients with an average age of 7.2 ± 4.6 years. More than half of them (51.7%) were diagnosed with malignancies before CT scan. 48.3% were diagnosed with non-tumor diseases or healthy. Compared to non-tumor group, children with tumor were more likely to have multiple nodules in both lungs, with larger size and often be accompanied by osteolytic or mass lesions. Based on the decision tree model, patients' history of malignancies, nodules diameter size≥5mm, and specific nodule distribution (multiple in both lungs, multiple in the right lung or solitary in the upper or middle right lobe) were important potential predictors for malignity. In the validation set, sensitivity, specificity and AUC were 0.855, 0.833 and 0.828 (95%CI: 0.712-0.909), respectively.
Conclusion:
This study conducted a clinical assessment model to differentiate benignity and malignancy of pediatric pulmonary nodules. We suggested that a nodule's diameter, distribution and patient's history of malignancies are predictable factors in benign or malignant determination.
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