A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

Gao, Rongji; Gao, Yinghua; Zhang, Juan; Zhu, Chunyu; Zhang, Yue; Yan, Chengxin

doi:10.1007/s00432-023-05262-4

J Cancer Res Clin Oncol

2023

DOI: 10.1007/s00432-023-05262-4

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A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

Rongji Gao,

Yinghua Gao,

Juan Zhang

et al.

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2024

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Cited by 2 publications

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Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface

Zhu,

Jiang,

Zhou

et al. 2024

Ther Adv Respir Dis

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Background: The preoperative determination of visceral pleural invasion (VPI) in patients with malignant solitary pulmonary nodules (SPNs) is essential for determining the surgical range and selecting adjuvant chemotherapy. Objectives: This study aimed to systematically investigate risk factors of VPI in patients with SPN and construct a preoperative predictive model for such patients. Design: This is a retrospective study. The clinical, radiological, and pathological characteristics of study subjects were reviewed, and the groups with and without VPI were compared. Methods: Multivariate logistic analysis was utilized to identify independent risk factors for VPI. Moreover, a predictive nomogram was constructed to assess the likelihood of VPI occurrence. Results: Of the 364 enrolled cases, SPNs adjacent to the pleura with VPI were found in 110 (30.2%) patients. By incorporating four preoperative variables, including tumor diameter (>2 cm), maximum computed tomography value (>200 Hu), air bronchogram sign, and age, a preoperative predictive nomogram was constructed. The nomogram demonstrated good discriminative ability, with a C-index of 0.736 (95% CI (0.662–0.790)). Furthermore, our data indicated that the air bronchogram sign (odd ratio (OR) 1.81, 95% CI (0.99–3.89), p = 0.048), a maximum diameter >2 cm (OR 24.48, 95% CI (8.43–71.07), p < 0.001), pathological type (OR 5.01, 95% CI (2.61–9.64), p < 0.001), and Ki-67 >30% (OR 2.95, 95% CI (1.40–6.21), p = 0.004) were overall independent risk factors for VPI. Conclusion: This study investigated the risk factors for VPI in malignant SPNs touching the pleural surface. Additionally, a nomogram was developed to predict the likelihood of VPI in such patients, facilitating informed decision-making regarding surgical approaches and treatment protocols.

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Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface

Zhu,

Jiang,

Zhou

et al. 2024

Ther Adv Respir Dis

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An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

Ma,

Zhang,

et al. 2024

BMC Med Imaging

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Background This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs. Methods Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group. Results When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels ( P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant ( P > 0.05). Conclusion The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-024-01467-2.

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A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

Cited by 2 publications

References 37 publications

Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface

Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

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