Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Qi, Lin; Xue, Ke; Li, Cheng; He, Wenjie; Mao, Dingbiao; Xiao, Li; Hua, Yanqing; Li, Ming

doi:10.1038/s41598-019-50989-1

Cited by 24 publications

(22 citation statements)

References 25 publications

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“…Patients with IAC tended to be older, while patients with MIA were younger. This was consistent with previous findings [ 28 ]. Morphological features from CT images are frequently used in routine clinical practice for the ADC with pGGNs differential diagnostic process [ 29 ].…”

Section: Discussionsupporting

confidence: 94%

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Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

Jiang

Che

et al. 2021

Cancer Imaging

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Background Pure ground-glass nodules (pGGNs) with pleural contact (P-pGGNs) comprise not only invasive adenocarcinoma (IAC), but also minimally invasive adenocarcinoma (MIA). Radiomics recognizes complex patterns in imaging data by extracting high-throughput features of intra-tumor heterogeneity in a non-invasive manner. In this study, we sought to develop and validate a radiomics signature to identify IAC and MIA presented as P-pGGNs. Methods In total, 100 patients with P-pGGNs (69 training samples and 31 testing samples) were retrospectively enrolled from December 2012 to May 2018. Imaging and clinical findings were also analyzed. In total, 106 radiomics features were extracted from the 3D region of interest (ROI) using computed tomography (CT) imaging. Univariate analyses were used to identify independent risk factors for IAC. The least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation was used to generate predictive features to build a radiomics signature. Receiver-operator characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy of the radiomics signature. Decision curve analyses (DCA) were also conducted to evaluate whether the radiomics signature was sufficiently robust for clinical practice. Results Univariate analysis showed significant differences between MIA (N = 47) and IAC (N = 53) groups in terms of patient age, lobulation signs, spiculate margins, tumor size, CT values and relative CT values (all P < 0.05). ROC curve analysis showed, when MIA was identified from IAC, that the critical value of tumor length diameter (TLD) was1.39 cm and the area under the ROC curve (AUC) was 0.724 (sensitivity = 0.792, specificity = 0.553). The critical CT value on the largest axial plane (CT-LAP) was − 597.45 HU, and the AUC was 0.666 (sensitivity = 0.698, specificity= 0.638). The radiomics signature consisted of seven features and exhibited a good discriminative performance between IAC and MIA, with an AUC of 0.892 (sensitivity = 0.811, specificity 0.719), and 0.862 (sensitivity = 0.625, specificity = 0.800) in training and testing samples, respectively. Conclusions Our radiomics signature exhibited good discriminative performance in differentiating IAC from MIA in P-pGGNs, and may offer a crucial reference point for follow-up and selective surgical management.

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Section: Discussionsupporting

confidence: 94%

“…Patients with IAC tended to be older, while patients with MIA were younger. This was consistent with previous findings [28]. Morphological features Fig.…”

Section: Discussionsupporting

confidence: 93%

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

Jiang

Che

et al. 2021

Cancer Imaging

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“…Pulmonary GGN is an early manifestation of various benign and malignant lesions; thus, early diagnosis determines the way of treatment, which influences the patients' prognosis [1,[16][17][18][19]. Several studies have revealed that benign and malignant GGNs can be distinguished according to their specific CT morphological features, such as the size, lobulation, spiculation, vacuoles, pleural traction, and air bronchogram sign [17,[20][21][22][23].…”

Section: Discussionmentioning

confidence: 99%

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules

et al. 2021

Insights Imaging

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Background The presence of pulmonary vessels inside ground-glass nodules (GGNs) of different nature is a very common occurrence. This study aimed to reveal the significance of pulmonary vessels displayed in GGNs in their diagnosis and differential diagnosis. Results A total of 149 malignant and 130 benign GGNs confirmed by postoperative pathological examination were retrospectively enrolled in this study. There were significant differences in size, shape, nodule-lung interface, pleural traction, lobulation, and spiculation (each p < 0.05) between benign and malignant GGNs. Compared with benign GGNs, intra-nodular vessels were more common in malignant GGNs (67.79% vs. 54.62%, p = 0.024), while the vascular categories were similar (p = 0.663). After adjusting the nodule size and the distance between the nodule center and adjacent pleura [radius–distance ratio, RDR], the occurrences of internal vessels between them were similar. The number of intra-nodular vessels was positively correlated with nodular diameter and RDR. Vascular changes were more common in malignant than benign GGNs (52.48% vs. 18.31%, p < 0.0001), which mainly manifested as distortion and/or dilation of pulmonary veins (61.19%). The occurrence rate, number, and changes of internal vessels had no significant differences among all the pre-invasive and invasive lesions (each p > 0.05). Conclusions The incidence of internal vessels in GGNs is mainly related to their size and the distance between nodule and pleura rather than the pathological nature. However, GGNs with dilated or distorted internal vessels, especially pulmonary veins, have a higher possibility of malignancy.

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“… 9–12 There have been several studies of the CT features of absorbable nodules, but most of them have focused on part-solid nodules. 5 , 13–17 …”

Section: Introductionmentioning

confidence: 99%

Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography

Lin¹,

Lv²,

Fu³

et al. 2021

JIR

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Purpose To investigate the clinical and computed tomography (CT) characteristics of absorbable pulmonary solid nodules (PSNs) and to clarify CT features for distinguishing absorbable PSNs from malignant ones. Materials and Methods From January 2015 to February 2021, a total of 316 patients with 348 PSNs (171 absorbable and 177 size-matched malignant) were retrospectively enrolled. Their clinical and CT data were analyzed and compared to determine CT features for predicting absorbable PSNs. Results Between absorbable and malignant PSNs, there were significant differences in patients’ age, lesions’ locations, shapes, homogeneity, borders, distance from the pleura, vacuoles, air bronchograms, lobulation, spiculation, halo sign, multiple concomitant nodules and pleural indentation (each P < 0.05). Multivariate analysis revealed that the independent predictors of absorbable PSNs were the following: patient age ≤55 years (OR, 2.660; 95% CI, 1.432–4.942; P = 0.002), homogeneous density (OR, 2.487; 95% CI, 1.107–5.590; P = 0.027), ill-defined border (OR, 5.445; 95% CI, 1.661–17.846; P = 0.005), halo sign (OR, 3.135; 95% CI, 1.154–8.513; P = 0.025), multiple concomitant nodules (OR, 8.700; 95% CI, 4.401–17.197; P <0.001), and abutting pleura (OR, 3.759; 95% CI, 1.407–10.044; P = 0.008). The indicators for malignant PSNs were the following: lobulation (OR, 3.904; 95% CI, 1.956–7.791; P <0.001), spiculation (OR, 4.980; 95% CI, 2.202–11.266, P <0.001), and pleural indentation (OR, 4.514; 95% CI, 1.223–16.666; P = 0.024). Conclusion In patients younger than 55 years, PSNs with homogeneous density, ill-defined border, halo sign, multiple concomitant nodules, and abutting pleura should be highly suspected as absorbable ones.

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Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Cited by 24 publications

References 25 publications

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules

Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography

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