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Updated: Mar 6, 2026
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
Published on: January 10, 2025
Quantitative CT analysis of pulmonary pure ground-glass nodule predicts histological invasiveness
Qiong Li1, Li Fan1, En-Tao Cao2
1Department of Radiology, Changzheng Hospital, Second Military Medical University, NO. 415, Fengyang Road, Shanghai 200003, China.
Objective:
To assess whether quantitative computed tomography (CT) can help predict histological invasiveness of pulmonary adenocarcinoma appearing as pure ground glass nodules (pGGNs).
Methods:
A total of 110 pulmonary pGGNs were retrospectively evaluated, and pathologically classified as pre-invasive lesions, minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA). Maximum nodule diameters, largest cross-sectional areas, volumes, mean CT values, weights, and CT attenuation values at the 0th,2th,5th, 25th, 50th,75th, 95th, 98th and100th percentiles on histogram, as well as 2th to 98th, 5th to 95th, 25th to 75th,and 0th to 100thslopes, respectively, were compared among the three groups.
Results:
Of the 110 pGGNs, 50, 28, and 32 were pre-invasive lesions, MIA, and IPA, respectively. Maximum nodule diameters, largest cross-sectional areas, andmass weights were significantly larger in the IPA group than in pre-invasive lesions. The 95th, 98th, 100th percentiles, and 2th to 98th, 25th to 75th, and 0th to 100thslopes were significantly different between pre-invasive lesions and MIA or IPA. Logistic regression analysis showed that the maximum nodule diameter (OR=1.21, 95%CI: 1.071-1.366, p<0.01) and 100th percentile on histogram (OR=1.02, 95%CI: 1.009-1.032, p<0.001) independently predicted histological invasiveness.
Conclusions:
Quantitative analysis of CT imaging can predict histological invasiveness of pGGNs, especiallythe maximum nodule diameter and 100th percentile on CT number histogram; this can instruct the long-term follow-up and selective surgical management.
