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Published on: December 19, 2020
Development of a Radiologic Nomogram to Predict Invasiveness in Pulmonary Pure Ground-Glass Opacities: Analysis of
Chiara Catelli1, Susanna Guerrini2, Miriana D'Alessandro3
1Thoracic Surgery and Lung Transplant Unit, Department of Medicine, Surgery and Neurosciences, Azienda Ospedaliero-Universitaria Senese, University of Siena, 53100 Siena, Italy.
Background:
Most predictive models for assessing the invasiveness of pure ground-glass nodules (pGGOs) have been developed in Asian populations, which may limit their applicability to Western cohorts. As the detection of pGGOs continues to increase, there is a growing need for reliable, population-specific tools to support preoperative decision-making.
Methods:
This multicenter retrospective study analyzed patients from the GORDON database who underwent surgical resection for pGGOs < 40 mm between January 2013 and June 2024. Radiologic features were assessed using preoperative high-resolution and contrast-enhanced CT scans. Univariate and multivariable logistic regression analyses were performed to identify independent predictors of invasive adenocarcinoma (IAC). A radiologic nomogram was developed and internally validated using a training (80%) and validation (20%) cohort.
Results:
A total of 490 pGGOs were included, of which 421 (85.9%) were IAC and 69 (14.1%) noninvasive (Adenocarcinoma in Situ or Minimally Invasive Adenocarcinoma). Upon multivariable analysis, maximum radiologic diameter (adjusted odds ratio [aOR] = 1.09, p = 0.001), spiculated margins (aOR = 3.07, p = 0.006), and unenhanced CT attenuation (aOR = 1.01, p < 0.001) were independent predictors of invasiveness. These variables were incorporated into a nomogram demonstrating good discrimination, with an area under the curve (AUC) of 0.86 (95% CI, 0.81-0.90) in the training cohort and 0.80 (95% CI, 0.70-0.90) in the validation cohort.
Conclusions:
A radiologic nomogram based on routinely available CT features enables accurate estimation of invasive adenocarcinoma risk in pGGOs. By integrating parameters beyond lesion size, this tool supports personalized management and may improve preoperative decision-making.
