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Liu JC, Sheng J, Xue S, Fang M, Huang J, Chen ZZ, Wang RK, Han M. Value of the Air Bronchogram Sign and Other Computed Tomography Findings in the Early Diagnosis of Lung Adenocarcinoma. J Comput Assist Tomogr 2024:00004728-990000000-00295. [PMID: 38446716 DOI: 10.1097/rct.0000000000001604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
OBJECTIVES The present study aims to explore the application value of the air bronchogram (AB) sign and other computed tomography (CT) signs in the early diagnosis of lung adenocarcinoma (LUAD). METHOD The pathological information and CT images of 130 patients diagnosed with N0 and M0 solitary pulmonary nodules (diameter ≤3 cm) and treated with surgical resection in our hospital between June 2021 and June 2022 were analyzed. RESULTS The patients were divided into the benign pulmonary nodule (BPN) group (14 cases), the AIS group (30 cases), the MIA group (10 cases), and the IAC group (76 cases). Among the 116 patients with AIS and LUAD, 96 showed an AB sign. Among the 14 patients with BPN, only 4 patients showed an AB sign. The average CT value and maximum diameter were significantly higher in the IAC group than in the AIS and MIA groups. In the BPN group, 5 patients had an average CT value of >80 HU. Among all LUAD-based groups, there was only 1 patient with a CT value of >60 HU. CONCLUSIONS The identification of the AB sign based on CT imaging facilitates the differentiation between benign and malignant nodules. The CT value and maximum diameter of pulmonary adenocarcinoma nodules increase with the increase of the malignancy degree. The nodule type, CT value, and maximum diameter are useful for predicting the pathological type and prognosis. If the average CT value of pulmonary nodules is >80 HU, LUAD may be excluded.
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Affiliation(s)
- Jia-Chang Liu
- From the Departments of Respiratory and Critical Care Medicine
| | | | - Song Xue
- Pathology, First Affiliated Hospital of Anhui University of Science & Technology, Huainan, China
| | - Ming Fang
- From the Departments of Respiratory and Critical Care Medicine
| | - Juan Huang
- From the Departments of Respiratory and Critical Care Medicine
| | | | - Rui-Kai Wang
- From the Departments of Respiratory and Critical Care Medicine
| | - Mei Han
- From the Departments of Respiratory and Critical Care Medicine
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Xue G, Jia W, Wang G, Zeng Q, Wang N, Li Z, Cao P, Hu Y, Xu J, Wei Z, Ye X. Lung microwave ablation: Post-procedure imaging features and evolution of pulmonary ground-glass nodule-like lung cancer. J Cancer Res Ther 2023; 19:1654-1662. [PMID: 38156934 DOI: 10.4103/jcrt.jcrt_837_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/01/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE To retrospectively examine the imaging characteristics of chest-computed tomography (CT) following percutaneous microwave ablation (MWA) of the ground-glass nodule (GGN)-like lung cancer and its dynamic evolution over time. MATERIALS AND METHODS From June 2020 to May 2021, 147 patients with 152 GGNs (51 pure GGNs and 101 mixed GGNs, mean size 15.0 ± 6.3 mm) were enrolled in this study. One hundred and forty-seven patients underwent MWA procedures. The imaging characteristics were evaluated at predetermined time intervals: immediately after the procedure, 24-48 h, 1, 3, 6, 12, and ≥18 months (47 GGNs). RESULTS This study population included 147 patients with 152 GGNs, as indicated by the results: 43.5% (66/152) adenocarcinoma in situ, 41.4% (63/152) minimally invasive adenocarcinoma, and 15.1% (23/152) invasive adenocarcinoma. Immediate post-procedure tumor-level analysis revealed that the most common CT features were ground-glass opacities (93.4%, 142/152), hyperdensity within the nodule (90.7%, 138/152), and fried egg sign or reversed halo sign (46.7%, 71/152). Subsequently, 24-48 h post-procedure, ground-glass attenuations, hyperdensity, and the fried egg sign remained the most frequent CT findings, with incidence rates of 75.0% (114/152), 71.0% (108/152), and 54.0% (82/152), respectively. Cavitation, pleural thickening, and consolidation were less frequent findings. At 1 month after the procedure, consolidation of the ablation region was the most common imaging feature. From 3 to 12 months after the procedure, the most common imaging characteristics were consolidation, involutional parenchymal bands and pleural thickening. At ≥18 months after the procedure, imaging features of the ablation zone revealed three changes: involuting fibrosis (80.8%, 38/47), consolidation nodules (12.8%, 6/47), and disappearance (6.4%, 3/47). CONCLUSIONS This study outlined the anticipated CT imaging characteristics of GGN-like lung cancer following MWA. Diagnostic and interventional radiologists should be familiar with the expected imaging characteristics and dynamic evolution post-MWA in order to interpret imaging changes with a reference image.
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Affiliation(s)
- Guoliang Xue
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China
| | - Gang Wang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China
| | - Nan Wang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Zhichao Li
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Pikun Cao
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Yanting Hu
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Jie Xu
- Department of Radiology, Guangrao County People's Hospital, Dongying, Shandong Province, China
| | - Zhigang Wei
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
| | - Xin Ye
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, China
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Dong H, Wang X, Qiu Y, Lou C, Ye Y, Feng H, Ye X, Chen D. Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:10519-10530. [PMID: 37289235 DOI: 10.1007/s00432-023-04854-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/13/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT). METHODS Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram. RESULTS The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model. CONCLUSION The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.
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Affiliation(s)
- Hao Dong
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Xinbin Wang
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Yonggang Qiu
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Cuncheng Lou
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Yinfeng Ye
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Han Feng
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Xiaodan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Institute of Medical Imaging, Shanghai, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Dihong Chen
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China.
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Dong H, Yin LK, Qiu YG, Wang XB, Yang JJ, Lou CC, Ye XD. Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study. Eur Radiol 2023; 33:3931-3940. [PMID: 36600124 DOI: 10.1007/s00330-022-09379-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. METHODS The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. RESULTS The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%). CONCLUSION The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. KEY POINTS • The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.
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Affiliation(s)
- Hao Dong
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Le-Kang Yin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong-Gang Qiu
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Xin-Bin Wang
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Jun-Jie Yang
- Department of Pathology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Cun-Cheng Lou
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Xiao-Dan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,Shanghai Institute of Medical Imaging, Shanghai, China. .,Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
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Liang ZR, Ye M, Lv FJ, Fu BJ, Lin RY, Li WJ, Chu ZG. Differential diagnosis of benign and malignant patchy ground-glass opacity by thin-section computed tomography. BMC Cancer 2022; 22:1206. [DOI: 10.1186/s12885-022-10338-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022] Open
Abstract
Abstract
Background
Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis.
Methods
From January 2016 to September 2021, 226 patients with 247 patchy GGOs (103 benign and 144 malignant) confirmed by postoperative pathological examination or follow-up were retrospectively enrolled. Their clinical and CT data were reviewed, and their CT features were compared. A binary logistic regression analysis was performed to reveal the predictors of malignancy.
Results
Compared to patients with benign patchy GGOs, malignant cases were older (P < 0.001), had a lower incidence of malignant tumor history (P = 0.003), and more commonly occurred in females (P = 0.012). Based on CT images, there were significant differences in the location, distribution, density pattern, internal bronchial changes, and boundary between malignant and benign GGOs (P < 0.05). The binary logistic regression analysis revealed that the independent predictors of malignant GGOs were the following: patient age ≥ 58 years [odds ratio (OR), 2.175; 95% confidence interval (CI), 1.135–6.496; P = 0.025], locating in the upper lobe (OR, 5.481; 95%CI, 2.027–14.818; P = 0.001), distributing along the bronchovascular bundles (OR, 12.770; 95%CI, 4.062–40.145; P < 0.001), centrally distributed solid component (OR, 3.024; 95%CI, 1.124–8.133; P = 0.028), and well-defined boundary (OR, 5.094; 95%CI, 2.079–12.482; P < 0.001).
Conclusions
In older patients (≥58 years), well-defined patchy GGOs with centric solid component, locating in the upper lobe, and distributing along the bronchovascular bundles should be highly suspected as malignancy.
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Duan L, Shan W, Bo G, Lu G, Guo L. Qualitative (and Quantitative) Values of the Lung-RADS and Computed Tomography in Diagnosing Solitary Pulmonary Nodules. Diagnostics (Basel) 2022; 12:diagnostics12112699. [PMID: 36359542 PMCID: PMC9689942 DOI: 10.3390/diagnostics12112699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.
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Affiliation(s)
- Lizhen Duan
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Wenli Shan
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Genji Bo
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Guangming Lu
- Department of Medical Imaging, The Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Lili Guo
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
- Correspondence: ; Tel.: +86-13651549848
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Correlation Analysis of Computed Tomography Features and Pathological Types of Multifocal Ground-Glass Nodular Lung Adenocarcinoma. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7267036. [PMID: 35928980 PMCID: PMC9345702 DOI: 10.1155/2022/7267036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/24/2022] [Accepted: 07/01/2022] [Indexed: 11/17/2022]
Abstract
To investigate the correlation between computed tomography (CT) image characteristics of multiple lung ground-glass nodules (GGNs) and pathological classification, the CT image data of multiple lung GGN patients confirmed by pathology (n = 132) in our hospital were collected. The imaging features of GGNs were analyzed by qualified physicians, including lesion size (diameter, volume, and mass), location, CT values (mean and relative CT values), lesion morphology (round and irregular), marginal structure (pagination and burr), internal structure (bronchial inflation sign), and adjacent structure (pleural depression). CT imaging analysis was performed for the subtype of infiltrating adenocarcinoma (IAC). In CT findings, GGNs were greatly different from adenomatous hyperplasia (AAH), pure GGN adenocarcinoma in situ (AIS), and microinvasive adenocarcinoma (MIA) in terms of marginal structure, lesion morphology, internal structure, adjacent structure, and size (P < 0.05). The mean and relative CT values of mural adenocarcinoma, acinar adenocarcinoma, and papillary adenocarcinoma of IAC subtypes were greatly different from those of AAH/AIS/MIA (P < 0.05). In summary, the CT images of GGNs can be used as the basis for the differentiation of AAH, AIS, and MIA early noninvasive types and IAC invasive types, and the CT value of the IAC subtype can be used as the basis for the classification and differentiation of IAC pathological subtypes.
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Wang C, Wu N, Zhang Z, Zhang LX, Yuan XD. Evaluation of the dual vascular supply patterns in ground-glass nodules with a dynamic volume computed tomography. World J Radiol 2022; 14:155-164. [PMID: 35978977 PMCID: PMC9258305 DOI: 10.4329/wjr.v14.i6.155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/20/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In recent years, the detection rate of ground-glass nodules (GGNs) has been improved dramatically due to the popularization of low-dose computed tomography (CT) screening with high-resolution CT technique. This presents challenges for the characterization and management of the GGNs, which depends on a thorough investigation and sufficient diagnostic knowledge of the GGNs. In most diagnostic studies of the GGNs, morphological manifestations are used to differentiate benignancy and malignancy. In contrast, few studies are dedicated to the assessment of the hemodynamics, i.e., perfusion parameters of the GGNs.
AIM To assess the dual vascular supply patterns of GGNs on different histopathology and opacities.
METHODS Forty-seven GGNs from 47 patients were prospectively included and underwent the dynamic volume CT. Histopathologic diagnoses were obtained within two weeks after the CT examination. Blood flow from the bronchial artery [bronchial flow (BF)] and pulmonary artery [pulmonary flow (PF)] as well as the perfusion index (PI) = [PF/(PF + BF)] were obtained using first-pass dual-input CT perfusion analysis and compared respectively between different histopathology and lesion types (pure or mixed GGNs) and correlated with the attenuation values of the lesions using one-way ANOVA, student’s t test and Pearson correlation analysis.
RESULTS Of the 47 GGNs (mean diameter, 8.17 mm; range, 5.3-12.7 mm), 30 (64%) were carcinoma, 6 (13%) were atypical adenomatous hyperplasia and 11 (23%) were organizing pneumonia. All perfusion parameters (BF, PF and PI) demonstrated no significant difference among the three conditions (all P > 0.05). The PFs were higher than the BFs in all the three conditions (all P < 0.001). Of the 30 GGN carcinomas, 14 showed mixed GGNs and 16 pure GGNs with a higher PI in the latter (P < 0.01). Of the 17 benign GGNs, 4 showed mixed GGNs and 13 pure GGNs with no significant difference of the PI between the GGN types (P = 0.21). A negative correlation (r = -0.76, P < 0.001) was demonstrated between the CT attenuation values and the PIs in the 30 GGN carcinomas.
CONCLUSION The GGNs are perfused dominantly by the PF regardless of its histopathology while the weight of the BF in the GGN carcinomas increases gradually during the progress of its opacification.
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Affiliation(s)
- Chao Wang
- Department of Graduate, Hebei North University, Zhangjiakou 075000, Hebei Province, China
| | - Ning Wu
- Department of Radiology, The Eighth Medical Center of the People's Liberation Army General Hospital, Beijing 100091, China
| | - Zhuang Zhang
- Department of Graduate, Hebei North University, Zhangjiakou 075000, Hebei Province, China
| | - Lai-Xing Zhang
- Department of Graduate, Hebei North University, Zhangjiakou 075000, Hebei Province, China
| | - Xiao-Dong Yuan
- Department of Radiology, The Eighth Medical Center of the People's Liberation Army General Hospital, Beijing 100091, China
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Jiang Y, Xiong Z, Zhao W, Zhang J, Guo Y, Li G, Li Z. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs. Gan To Kagaku Ryoho 2022; 70:880-890. [PMID: 35301662 DOI: 10.1007/s11748-022-01801-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/03/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs. PATIENTS AND METHODS We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC). RESULTS The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness. CONCLUSION The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.
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Affiliation(s)
- Yining Jiang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Guosheng Li
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China. .,Dalian Engineering Research Centre for Artificial Intelligence in Medical Imaging, Dalian, China.
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Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool. J Pers Med 2021; 11:jpm11070641. [PMID: 34357108 PMCID: PMC8305822 DOI: 10.3390/jpm11070641] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/20/2021] [Accepted: 07/03/2021] [Indexed: 02/06/2023] Open
Abstract
Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.
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Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19. Jpn J Radiol 2021; 39:721-732. [PMID: 33900542 PMCID: PMC8071755 DOI: 10.1007/s11604-021-01120-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/07/2021] [Indexed: 02/06/2023]
Abstract
Thoracic imaging is fundamental in the diagnostic route of Coronavirus disease 2019 (COVID-19) especially in patients admitted to hospitals. In particular, chest computed tomography (CT) has a key role in identifying the typical features of the infection. Ground-glass opacities (GGO) are one of the main CT findings, but their presence is not specific for this viral pneumonia. In fact, GGO is a radiological sign of different pathologies with both acute and subacute/chronic clinical manifestations. In the evaluation of a subject with focal or diffuse GGO, the radiologist has to know the patient’s medical history to obtain a valid diagnostic hypothesis. The authors describe the various CT appearance of GGO, related to the onset of symptoms, focusing also on the ancillary signs that can help radiologist to obtain a correct and prompt diagnosis.
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Ye X, Fan W, Wang Z, Wang J, Wang H, Wang J, Wang C, Niu L, Fang Y, Gu S, Tian H, Liu B, Zhong L, Zhuang Y, Chi J, Sun X, Yang N, Wei Z, Li X, Li X, Li Y, Li C, Li Y, Yang X, Yang W, Yang P, Yang Z, Xiao Y, Song X, Zhang K, Chen S, Chen W, Lin Z, Lin D, Meng Z, Zhao X, Hu K, Liu C, Liu C, Gu C, Xu D, Huang Y, Huang G, Peng Z, Dong L, Jiang L, Han Y, Zeng Q, Jin Y, Lei G, Zhai B, Li H, Pan J. [Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2021; 24:305-322. [PMID: 33896152 PMCID: PMC8174112 DOI: 10.3779/j.issn.1009-3419.2021.101.14] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
局部热消融技术在肺部结节治疗领域正处在起步与发展阶段,为了肺结节热消融治疗的临床实践和规范发展,由“中国医师协会肿瘤消融治疗技术专家组”“中国医师协会介入医师分会肿瘤消融专业委员会”“中国抗癌协会肿瘤消融治疗专业委员会”“中国临床肿瘤学会消融专家委员会”组织多学科国内有关专家,讨论制定了“热消融治疗肺部亚实性结节专家共识(2021年版)”。主要内容包括:①肺部亚实性结节的临床评估;②热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症;③存在的问题和未来发展方向。
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Affiliation(s)
- Xin Ye
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Weijun Fan
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510050, China
| | - Zhongmin Wang
- Department of Interventional Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Junjie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China
| | - Hui Wang
- Interventional Center, Jilin Provincial Cancer Hospital, Changchun 170412, China
| | - Jun Wang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Chuntang Wang
- Department of Thoracic Surgery, Dezhou Second People's Hospital, Dezhou 253022, China
| | - Lizhi Niu
- Department of Oncology, Affiliated Fuda Cancer Hospital, Jinan University, Guangzhou 510665, China
| | - Yong Fang
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Shanzhi Gu
- Department of Interventional Radiology, Hunan Cancer Hospital, Changsha 410013, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Baodong Liu
- Department of Thoracic Surgery, Xuan Wu Hospital Affiliated to Capital Medical University, Beijing 100053, China
| | - Lou Zhong
- Thoracic Surgery Department, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Yiping Zhuang
- Department of Interventional Therapy, Jiangsu Cancer Hospital, Nanjing 210009, China
| | - Jiachang Chi
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Xichao Sun
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Nuo Yang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Zhigang Wei
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Xiao Li
- Department of Interventional Therapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiaoguang Li
- Minimally Invasive Tumor Therapies Center, Beijing Hospital, Beijing 100730, China
| | - Yuliang Li
- Department of Interventional Medicine, The Second Hospital of Shandong University, Jinan 250033, China
| | - Chunhai Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Yan Li
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Xia Yang
- Department of Oncology, Shandong Provincial Hospital Afliated to Shandong First Medical University, Jinan 250101, China
| | - Wuwei Yang
- Department of Oncology, The Fifth Medical Center, Chinese PLA General Hospital, Beijing 100071, China
| | - Po Yang
- Interventionael & Vascular Surgery, The Fourth Hospital of Harbin Medical University, Harbin 150001, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yueyong Xiao
- Department of Radiology, Chinese PLA Gneral Hospital, Beijing 100036, China
| | - Xiaoming Song
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Kaixian Zhang
- Department of Oncology, Tengzhou Central People's Hospital, Tengzhou 277500, China
| | - Shilin Chen
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Nanjing 210009, China
| | - Weisheng Chen
- Department of Thoracic Surgery, Fujian Medical University Cancer Hospital, Fujian 350011, China
| | - Zhengyu Lin
- Department of Intervention, The First Affiliated Hospital of Fujian Medical University, Fujian 350005, China
| | - Dianjie Lin
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Zhiqiang Meng
- Minimally Invasive Therapy Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Xiaojing Zhao
- Department of Thoracic Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Kaiwen Hu
- Department of Oncology, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100078, China
| | - Chen Liu
- Department of Interventional Therapy, Beijing Cancer Hospital, Beijing 100161, China
| | - Cheng Liu
- Department of Radiology, Shandong Medical Imaging Research Institute, Jinan 250021, China
| | - Chundong Gu
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Yong Huang
- Department of Imaging, Affiliated Cancer Hospital of Shandong First Medical University, Jinan 250117, China
| | - Guanghui Huang
- Department of Oncology, Shandong Provincial Hospital Afliated to Shandong First Medical University, Jinan 250101, China
| | - Zhongmin Peng
- Department of Thoracic Surgery , Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Liang Dong
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Lei Jiang
- Department of Radiology, The Convalescent Hospital of East China, Wuxi 214063, China
| | - Yue Han
- Department of Interventional Therapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Qingshi Zeng
- Department of Medical Imaging, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Yong Jin
- Interventionnal Therapy Department, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Guangyan Lei
- Department of Thoracic Surgery, Shanxi Provincial Cancer Hospital, Xi'an 710061, China
| | - Bo Zhai
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Hailiang Li
- Department of Interventional Radiology, Henan Cancer Hospital, Zhengzhou 450003, China
| | - Jie Pan
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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Impact of coronavirus disease 2019 (COVID-19) outbreak on radiology research: An Italian survey. Clin Imaging 2021; 76:144-148. [PMID: 33601188 PMCID: PMC7875708 DOI: 10.1016/j.clinimag.2021.02.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 02/08/2023]
Abstract
Purpose To understand how COVID-19 pandemic has changed radiology research in Italy. Methods A questionnaire (n = 19 questions) was sent to all members of the Italian Society of Radiology two months after the first Italian national lockdown was lifted. Results A total of 327 Italian radiologists took part in the survey (mean age: 49 ± 12 years). After national lockdown, the working-flow came back to normal in the vast majority of cases (285/327, 87.2%). Participants reported that a total of 462 radiological trials were recruiting patients at their institutions prior to COVID-19 outbreak, of which 332 (71.9%) were stopped during the emergency. On the other hand, 252 radiological trials have been started during the pandemic, of which 156 were non-COVID-19 trials (61.9%) and 96 were focused on COVID-19 patients (38.2%). The majority of radiologists surveyed (61.5%) do not conduct research. Of the radiologists who carried on research activities, participants reported a significant increase of the number of hours per week spent for research purposes during national lockdown (mean 4.5 ± 8.9 h during lockdown vs. 3.3 ± 6.8 h before lockdown; p = .046), followed by a significant drop after the lockdown was lifted (3.2 ± 6.5 h per week, p = .035). During national lockdown, 15.6% of participants started new review articles and completed old papers, 14.1% completed old works, and 8.9% started new review articles. Ninety-six surveyed radiologists (29.3%) declared to have submitted at least one article during COVID-19 emergency. Conclusion This study shows the need to support radiology research in challenging scenarios like COVID-19 emergency.
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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. Cancer Imaging 2021; 21:1. [PMID: 33407884 PMCID: PMC7788838 DOI: 10.1186/s40644-020-00376-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 12/18/2020] [Indexed: 12/13/2022] Open
Abstract
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. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-020-00376-1.
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Zhu Y, Hou D, Lan M, Sun X, Ma X. A comparison of ultra-high-resolution CT target scan versus conventional CT target reconstruction in the evaluation of ground-glass-nodule-like lung adenocarcinoma. Quant Imaging Med Surg 2019; 9:1087-1094. [PMID: 31367562 DOI: 10.21037/qims.2019.06.09] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background The aim of this study was to determine whether the clinical value of scanned computed tomography (CT) images is higher when using ultra-high-resolution CT (U-HRCT) target scanning than conventional CT target reconstruction scanning in the evaluation of ground-glass-nodule (GGN)-like lung adenocarcinoma. Methods A total of 91 consecutive patients with isolated GGN-like lung adenocarcinoma were included in this study from April 2017 to June 2018. U-HRCT and conventional CT scans were conducted in all enrolled patients. Two experienced thoracic radiologists independently assessed image quality and made diagnoses. Based on the pathological results, the accuracies of U-HRCT target scanning and conventional CT target reconstruction for detecting morphological features on CT, including spiculation of GGNs, bronchial vascular bundles, solid components in the nodules, burr, vacuole, air bronchial signs, and fissure distortion, were calculated. All statistical analyses were performed using SPSS 17.0 software. Enumeration data were tested using the Chi-square test. A P value of <0.05 was considered statistically significant. Results When both techniques were compared with the pathological findings, the detection rate for CT images obtained using U-HRCT target scanning and conventional CT target reconstruction with regard to the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules were 78% vs. 61.5%, 72.5% vs. 54.9%, 65.9% vs. 49.5%, respectively. The presence of the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules in U-HRCT target scanning was significantly higher than that in conventional CT target reconstruction (all P<0.05). However, no significant difference was observed between the two techniques with regard to the burr, vacuole, air bronchial signs, and fissure distortion (all P>0.05). Conclusions When viewing GGNs, the detection rate was higher for U-HRCT target scanning than for conventional CT target reconstruction, and this improvement significantly enhanced the diagnostic accuracy of early lung adenocarcinoma.
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Affiliation(s)
- Yanyan Zhu
- Division of Computed Tomography, Department of Radiology, Shandong University School of Medicine, Shandong Provincial Third Hospital, Jinan 250031, China
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Meihong Lan
- Department of Radiology, Shandong Chest Hospital, Jinan 250101, China
| | - Xiaoli Sun
- Department of Computed Tomography, Beijing Shijitan Hospital, Ninth Clinical Medical College of Peking University, Capital Medical University, Beijing 100038, China
| | - Xiangxing Ma
- Department of Radiology, Qilu Hospital, Shandong University, Jinan 250012, China
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