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Updated: May 2, 2026
05:24
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
Published on: January 10, 2025
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Transient subsolid nodules in patients with extrapulmonary malignancies: their frequency and differential features.
Won Seok Choi1, Chang Min Park2, Yong Sub Song1
1Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
Acta Radiologica (Stockholm, Sweden : 1987)
|March 12, 2014
Summary
Approximately 46% of pulmonary subsolid nodules (SSNs) in cancer patients are transient. Differentiating these transient SSNs using clinical and CT scan features improves diagnostic accuracy.
Area of Science:
- Pulmonary Medicine
- Oncology
- Radiology
Background:
- Pulmonary subsolid nodules (SSNs) are frequently observed in patients with extrapulmonary malignancies.
- The proportion of transient SSNs and accurate diagnostic methods for them remain unclear in this population.
Purpose of the Study:
- To determine the frequency of transient SSNs in patients with extrapulmonary malignancies.
- To identify clinical and thin-section CT features that differentiate transient from persistent SSNs.
Main Methods:
- Retrospective review of 78 SSNs in 63 patients with extrapulmonary malignancies (2005-2012).
- Comparison of clinical and thin-section CT characteristics between transient and persistent SSNs.
- Multivariate analysis to identify significant discriminators of transient SSNs.
Main Results:
- 46.2% of SSNs were transient.
- Significant differences observed in age, sex, detection mode, eosinophilia, lesion multiplicity, margin, and pleural retraction between transient and persistent SSNs.
- Follow-up detection, multiple lesions, and ill-defined margins were key discriminators of transient SSNs; combined features improved accuracy.
Conclusions:
- Nearly half of SSNs in patients with extrapulmonary malignancies are transient.
- Transient SSNs can be accurately identified using a combination of clinical and thin-section CT features.
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