Researchers investigated computed tomography parameters to help differentiate non-invasive from invasive pathology in surgically resected pulmonary micronodules. The retrospective study, published in BMC Pulmonary Medicine, focused on surgically resected pulmonary micronodules measuring 5 millimeters or less. In this subgroup of patients, conventional imaging characteristics are often subtle or atypical, making diagnosis challenging. The study utilized histopathology as the reference standard to evaluate the diagnostic utility of CT-derived quantitative parameters combined with qualitative imaging features.

The investigation retrospectively analyzed 96 surgically resected pulmonary micronodules from 61 patients. The cohort was divided into two primary groups based on pathological findings. The non-invasive group consisted of benign lesions, atypical adenomatous hyperplasia, and adenocarcinoma in situ. The invasive group included minimally invasive adenocarcinoma and invasive adenocarcinoma.

To account for the statistical complexity of multiple nodules originating from the same patient, the researchers employed generalized estimating equations. Model performance was assessed using receiver operating characteristic analysis and patient-level bootstrap resampling to estimate the optimism in the apparent performance metrics. A sensitivity analysis was also conducted to evaluate the robustness of the findings by excluding benign lesions from the dataset.

The cohort comprised 47 non-invasive nodules and 49 invasive nodules. In the multivariable generalized estimating equations model, surface area emerged as a significant factor associated with invasive pathology. The odds ratio for surface area was 1.016, with a 95 percent confidence interval ranging from 1.006 to 1.027. The association was statistically significant, with a P-value of 0.002. This indicates that as the surface area of the micronodule increases, the likelihood of the pathology being invasive also increases, even after adjusting for other variables in the model.

The multivariable model demonstrated an apparent area under the curve of 0.773. After correcting for optimism using bootstrap resampling, the AUC was 0.766. When compared to using surface area alone, which had an AUC of 0.716, the difference was not statistically significant, with a DeLong P-value of 0.097. This suggests that while the combined model provides a comprehensive assessment, surface area alone retains substantial discriminative power for differentiating between non-invasive and invasive pathology in these small nodules.

To test the stability of the results, the researchers performed a sensitivity analysis by excluding 29 benign lesions from the cohort. In this reduced dataset, surface area remained significantly associated with invasive pathology. The odds ratio increased to 1.023, with a 95 percent confidence interval of 1.002 to 1.043, and a P-value of 0.030.

The study concludes that surface area is a meaningful quantitative parameter for differentiating non-invasive from invasive pathology in surgically resected pulmonary micronodules. The association held true after adjustment for within-patient clustering and the exclusion of benign lesions. However, the authors emphasize that the exploratory model requires independent validation before it can be applied broadly in clinical settings.

By focusing on micronodules, the study addresses a critical gap where standard imaging features may not provide sufficient diagnostic clarity.

Subject of Research: Diagnostic utility of CT quantitative parameters in pulmonary micronodules

Article Title: Quantitative CT parameters and imaging features for differentiating non-invasive from invasive pathology in surgically resected pulmonary micronodules: a retrospective study

Article References: Deng, L., Liu, X., Qiu, L., Li, J., Zhou, W., Gao, M., & Cheng, G. (2026). Quantitative CT parameters and imaging features for differentiating non-invasive from invasive pathology in surgically resected pulmonary micronodules: a retrospective study. BMC Pulmonary Medicine. https://doi.org/10.1186/s12890-026-04926-4

Image Credits: AI Generated

DOI: 10.1186/s12890-026-04926-4

Keywords: pulmonary micronodules, computed tomography, lung cancer, retrospective study, surface area, invasive pathology, radiology, diagnostic imaging, Quantitative, parameters, imaging, features