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. 2023 Jun;128(6):734-743.
doi: 10.1007/s11547-023-01648-z. Epub 2023 May 25.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

Andrea Borghesi  1 , Felice Leopoldo Coviello  2 , Alessandra Scrimieri  2 , Pietro Ciolli  2 , Marco Ravanelli  2 , Davide Farina  2
Affiliations

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

Andrea Borghesi et al. Radiol Med. 2023 Jun.

Abstract

Purpose: Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population.

Material and methods: We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses.

Results: In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth.

Conclusion: According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Keywords: Computed tomography; Computer-assisted image analysis; Nonsolid nodule; Pulmonary nodule; Pure ground-glass nodule; Subsolid nodule.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Images showing a summary of the steps of software-based analysis using ImageJ in a nonsolid nodule (NSN): a polygonal region of interest outlining the NSN contours, b threshold image obtained by applying an attenuation value of − 800 HU suitable for NSN segmentation, c list of quantitative CT features selected for NSN analysis
Fig. 2
Fig. 2
ROC curve for nonsolid nodule growth: model based on skewness of CT attenuation
Fig. 3
Fig. 3
ROC curve for nonsolid nodule growth: model based on linear mass density (LMD)
Fig. 4
Fig. 4
ROC curve for nonsolid nodule growth: model based on skewness of CT attenuation and linear mass density (LMD)
Fig. 5
Fig. 5
Growing nonsolid nodule (doubling time of 586 days) in the left upper lobe in a 71-year-old man, former smoker with no previous oncologic history. The interval between the baseline (left) and the last follow-up CT examination (right) was 1020 days. The software-based analysis performed on the baseline CT images obtained a skewness value of 1.61 and a LMD of 29.42 mg/mm. This nodule was surgically excised with a histopathological diagnosis of invasive pulmonary adenocarcinoma graded as pT1cN0
See this image and copyright information in PMC

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