ArticleLa Radiologia medica2023
Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- A multiparameter diagnostic model based on MRI volumetric ADC histogram and clinical variables accurately differentiates thymic epithelial tumors from mediastinal lymphomas.BMC medical imaging · 2026Article
- Preoperative CT-based radiomics to predict the 5-year growth of residual nodules after resection of dominant lung tumors in patients with multiple lung subsolid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposePersistent 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
methodsWe 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.
resultsIn 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.
conclusionAccording 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.
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