Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations

Chu, Zhi‐gang; Zhang, Yan; Li, Wang-jia; Li, Qi; Zheng, Yineng; Lv, Fajin

doi:10.1186/s12885-019-6274-0

Cited by 22 publications

(19 citation statements)

References 42 publications

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“…There were statistically significant differences in nodule size between the two groups, and Chu et al. ( 30 ) found that larger pulmonary nodules (diameter >1 cm) had more malignant CT features compared with smaller nodules (diameter <1 cm) in patients with solid lung cancerous nodules. Patients with LTB tended to have mediastinal lymphadenectasis compared to LAC patients in our study, but Zhu et al.…”

Section: Discussionmentioning

confidence: 98%

“…Vacuole sign is often seen in malignant nodules, which is an important sign of early lung cancer, but also occasionally seen in benign nodules (28,29). There were statistically significant differences in nodule size between the two groups, and Chu et al (30) found that larger pulmonary nodules (diameter >1 cm) had more malignant CT features compared with smaller nodules (diameter <1 cm) in patients with solid lung cancerous nodules. Patients with LTB tended to have mediastinal lymphadenectasis compared to LAC patients in our study, but Zhu et al (31) found that malignant diseases were mostly in the diseases with mediastinal lymphadenectasis, and the benign diseases were mainly granuloma in a cohort study with 846 patients who underwent endobronchial ultrasound-guided transbronchial needle aspiration.…”

Section: Discussionmentioning

confidence: 99%

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Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

et al. 2021

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AimTo investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN).Materials and MethodsA total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model.ResultsA total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99–1.00] and validation set (AUC, 0.99; 95% CI, 0.98–1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings.ConclusionThe radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

et al. 2021

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“…approximately 35% (range: 21-58%) (9-12). Chu et al reported that 95% (214/225) of solid cancerous nodule had a regular shape (30). Also, Zerhouni et al recorded that 25% of benign nodules showed irregular margins with lobulation or spiculation, and only 18% of these lesions were correctly assessed on CT (9).…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography

et al. 2021

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PurposeTo establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT).MethodA total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility.ResultsFor the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83–0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P<0.01; 87 vs. 61%, P<0.01; 85 vs. 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all).ConclusionThe CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.

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“… 19 In lung cancers, heterogeneous lesions would become homogeneous as growing due to tumor cell proliferation, but this change would not happen in inflammatory nodules. 19 , 24–26 Therefore, follow-up for monitoring density change is useful for differentiating heterogeneous nodules.…”

Section: Discussionmentioning

confidence: 99%

Solitary Pulmonary Inflammatory Nodule: CT Features and Pathological Findings

Xiao

et al. 2021

JIR

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Purpose Solitary pulmonary inflammatory nodules (SPINs) are frequently misdiagnosed as malignancy. We aimed to investigate CT features and pathological findings of SPINs for improving diagnosis strategies. Patients and Methods In this retrospective study, 225 and 310 consecutive patients with confirmed SPINs and lung cancerous nodules were enrolled from January 2013 to December 2020. Nodules were classified into different types based on the key CT features: I, homogeneous and well-defined nodules with smooth (Ia), coarse (Ib), or spiculated margins (Ic); II, nodules with blurred boundaries, peripheral patches, or both; III, nodules exhibiting heterogeneous density; and IV, polygonal nodules. The pathological findings of SPINs were simultaneously studied and summarized. Results Among the 225 SPINs, type I (Ia, Ib, and Ic), II, III, and IV were 137 (60.9%) (47 [20.9%], 33 [14.7%], and 57 [25.3%]), 62 (27.6%), 12 (5.3%) and 14 (6.2%), respectively. Correspondingly, those in 310 cancerous nodules were 275 (88.7%) (119 [38.4%], 70 [22.6%], and 86 [27.7%]), 20 (6.5%), 15 (4.8%), and 0, respectively. Compared with lung cancers, type I nodules were less common but type II and IV nodules were more common in SPINs (each P < 0.0001). Though the frequencies of subtype I ( P = 0.095) and type III ( P = 0.796) nodules were similar between two groups, their specific CT features were significantly different. The main pathological findings of each type of SPINs were most extensively identical (82.2 – 100%). Conclusion Between cancerous nodules and SPINs, differences in overall or specific CT features exist. The type II and IV nodules are highly indicative of SPINs, and each type of SPINs have almost similar pathological findings.

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Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations

Cited by 22 publications

References 42 publications

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography

Solitary Pulmonary Inflammatory Nodule: CT Features and Pathological Findings

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