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Yang Y, Xu J, Wang W, Ma M, Huang Q, Zhou C, Zhao J, Duan Y, Luo J, Jiang J, Ye L. A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma. BMC Cancer 2024; 24:438. [PMID: 38594670 PMCID: PMC11005224 DOI: 10.1186/s12885-024-12207-8] [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: 05/22/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.
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Affiliation(s)
- Yantao Yang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jing Xu
- Department of Dermatology and Venereal Diseases, Yan'an Hospital of Kunming City, Kunming, China
| | - Wei Wang
- Department of Thoracic and Cardiovascular Surgery, Shiyan Taihe Hospital (Hubei University of Medicine), Hubei, Shiyan, China
| | - Mingsheng Ma
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Qiubo Huang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Chen Zhou
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jie Zhao
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Yaowu Duan
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jia Luo
- Department of Pathology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiezhi Jiang
- Department of Radiology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lianhua Ye
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China.
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Chen M, Ding L, Deng S, Li J, Li X, Jian M, Xu Y, Chen Z, Yan C. Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features. Acad Radiol 2024:S1076-6332(24)00082-5. [PMID: 38508939 DOI: 10.1016/j.acra.2024.02.011] [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: 12/20/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 03/22/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs). MATERIALS AND METHODS Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test. RESULTS Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05). CONCLUSION A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.
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Affiliation(s)
- Mingwang Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Li Ding
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Shuting Deng
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Jingxu Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
| | - Xiaomei Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Mingjue Jian
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Zhao Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Chenggong Yan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
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Jia C, Jiang HC, Liu C, Wang YF, Zhao HY, Wang Q, Xue XQ, Li XF. The correlation between tumor radiological features and spread through air spaces in peripheral stage IA lung adenocarcinoma: a propensity score-matched analysis. J Cardiothorac Surg 2024; 19:19. [PMID: 38263158 PMCID: PMC10804508 DOI: 10.1186/s13019-024-02498-0] [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: 05/08/2023] [Accepted: 01/14/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND The consolidation tumor ratio (CTR) is a predictor of invasiveness in peripheral T1N0M0 lung adenocarcinoma. However, its association with spread through air spaces (STAS) remains largely unexplored. We aimed to explore the correlation between the CTR of primary tumors and STAS in peripheral T1N0M0 lung adenocarcinoma. METHODS We collected data from patients who underwent surgery for malignant lung neoplasms between January and November 2022. Univariate and multivariate analyses following propensity-score matching with sex, age, BMI, were performed to identify the independent risk factors for STAS. The incidence of STAS was compared based on pulmonary nodule type. A smooth fitting curve between CTR and STAS was produced by the generalized additive model (GAM) and a multiple regression model was established using CTR and STAS to determine the dose-response relationship and calculate the odds ratio (OR) and 95% confidence interval (CI). RESULTS 17 (14.5%) were diagnosed with STAS. The univariate analysis demonstrated that the history of the diabetes, size of solid components, spiculation, pleural indentation, pulmonary nodule type, consolidation/tumor ratio of the primary tumor were statistically significant between the STAS-positive and STAS-negative groups following propensity-score matching(p = 0.047, 0.049, 0.030, 0.006, 0.026, and < 0.001, respectively), and multivariate analysis showed that the pleural indentation was independent risk factors for STAS (with p-value and 95% CI of 0.043, (8.543-68.222)). Moreover, the incidence of STAS in the partially solid nodule was significantly different from that in the solid nodule and ground-glass nodule (Pearson Chi-Square = 7.49, p = 0.024). Finally, the smooth fitting curve showed that CTR tended to be linearly associated with STAS by GAM, and the multivariate regression model based on CTR showed an OR value of 1.24 and a p-value of 0.015. CONCLUSIONS In peripheral stage IA lung adenocarcinoma, the risk of STAS was increased with the solid component of the primary tumor. The pleural indentation of the primary tumor could be used as a predictor in evaluating the risk of the STAS.
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Affiliation(s)
- Chao Jia
- Department of Radiology, The Xuzhou Hospital Affiliated to Jiangsu University, Xu Zhou, Jiang Su, 221004, People's Republic of China
| | - Hai-Cheng Jiang
- Department of Thoracic Surgery, Xuzhou Cancer Hospital, Xuzhou, 221000, People's Republic of China
| | - Cong Liu
- Department of Puncture Minimally Invasive, Xuzhou New Health Hospital, Xuzhou, 221000, People's Republic of China
- Department of Minimally Invasive Oncology, Xuzhou New Health Hospital, Xuzhou, 221000, People's Republic of China
| | - Yu-Feng Wang
- Department of Nuclear Medicine, Xuzhou Cancer Hospital, Xuzhou, 221000, People's Republic of China
| | - Hong-Ying Zhao
- Department of Medical Oncology, Xuzhou Cancer Hospital, Xuzhou, 221000, People's Republic of China
| | - Qiang Wang
- Department of Radiotherapy, Xuzhou Cancer Hospital, Xuzhou, 221000, People's Republic of China
| | - Xiu-Qing Xue
- Department of Nuclear Medicine, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, The First People's Hospital of Yancheng, Yancheng, 224005, People's Republic of China.
| | - Xiao-Feng Li
- Department of Radiology, The Xuzhou Hospital Affiliated to Jiangsu University, Xu Zhou, Jiang Su, 221004, People's Republic of China.
- Department of Radiology, Xuzhou Cancer Hospital, Xuzhou, 221000, People's Republic of China.
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Xiong Z, Yang Z, Hu X, Yi M, Cai J. Individualised prediction of progression of solitary sub-solid pulmonary nodules based on CT semantic and clinical features: a 3-year follow-up study. Clin Radiol 2024; 79:e174-e181. [PMID: 37945437 DOI: 10.1016/j.crad.2023.10.005] [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: 03/29/2023] [Revised: 09/27/2023] [Accepted: 10/01/2023] [Indexed: 11/12/2023]
Abstract
AIM To develop and validate a progressive prediction model for estimating the time to progression (TTP) of sub-solid pulmonary nodules (SSNs). MATERIALS AND METHODS A total of 126 cases who met inclusion and exclusion criteria were included in the study. The primary endpoint of the study was TTP of SSNs. Baseline characteristics were assessed in terms of clinical and CT semantic features. Kaplan-Meier analysis and Cox regression analysis were performed to determine the relationship between SSNs TTP and factors from the entire data set. The nomogram was constructed based on the result of multivariate analysis and internal validation was performed using the bootstrapping. The nomogram's performance was assessed with the C-index, calibration curves, and decision curve analysis. RESULTS The median follow-up time of the population was 42.5 (21.5) months. On Kaplan-Meier analysis, patients with higher or positive values of the indices had higher cumulative progression rates (p<0.05). Multivariate Cox regression models identified diameter, consolidation tumour ratio (CTR), morphology, and vasodilation sign (VDS) as independent risk factors of TTP. These predictors were included in the final model to estimate individual probabilities of progression in the 3 years, which performed well in the discrimination (the C-index was 0.901 [95%CI: 0.830-0.981] and 0.875 [95%CI: 0.805-0.942] in the training and internally validation sets). CONCLUSION The radiological semantic features nomogram is a promising and favourable prognostic biomarker for predicting progression and may aid in clinical risk stratification and decision-making for SSNs.
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Affiliation(s)
- Z Xiong
- Department of Radiology, The Fifth People's Hospital of Chongqing, China; Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, China
| | - Z Yang
- Department of Radiology, Kaiyang County People's Hospital of Guizhou Province, China
| | - X Hu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, China
| | - M Yi
- Department of Radiology, The Fifth People's Hospital of Chongqing, China
| | - J Cai
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, China.
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Fu BJ, Zhang XC, Lv FJ, Chu ZG. Potential Role of Intrapulmonary Concomitant Lesions in Differentiating Non-Neoplastic and Neoplastic Ground Glass Nodules. J Inflamm Res 2023; 16:6155-6166. [PMID: 38107382 PMCID: PMC10725751 DOI: 10.2147/jir.s437419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 12/06/2023] [Indexed: 12/19/2023] Open
Abstract
Purpose To determine the value of intrapulmonary concomitant lesions in differentiating non-neoplastic and neoplastic ground-glass nodules (GGNs). Patients and Methods From January 2014 to March 2022, 395 and 583 patients with confirmed non-neoplastic and neoplastic GGNs were retrospectively enrolled. Their clinical and chest CT data were evaluated. The CT features of target GGNs and intrapulmonary concomitant lesions in these two groups were analyzed and compared, and the role of intrapulmonary concomitant lesions in improving differentiation was evaluated. Results The intrapulmonary concomitant lesions were more common in patients with non-neoplastic GGNs than in those with neoplastic ones (87.88% vs 82.18%, P = 0.015). Specifically, patients with non-neoplastic GGNs had a higher incidence of multiple solid nodules (SNs), patchy ground-glass opacity/consolidation, and fibrosis/calcification in any lung fields (each P < 0.05). Logistic regression analysis indicated that patients < 44 years old, diameter < 7.35 mm, irregular shape, and coarse margin or ill-defined boundary for target GGN, pleural thickening, and concomitant SNs in the same lobe and fibrosis or calcification in any lung field were independent indicators for predicting non-neoplastic GGNs. The AUC of the model for predicting non-neoplastic GGNs increased from 0.894 to 0.926 (sensitivity, 83.10%; specificity, 87.10%) after including the concomitant lesions in the patients' clinical characteristics and CT features of target GGNs (P < 0.0001). Conclusion Besides the patients' clinical characteristics and CT features of target GGNs, the concomitant multiple SNs in the same lobe and fibrosis/calcification in any lung field should be considered in further differentiating non-neoplastic and neoplastic GGNs.
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Affiliation(s)
- Bin-Jie Fu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xiao-Chuan Zhang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Department of Radiology, Chonggang General Hospital, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
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Sun H, Zhang C, Ouyang A, Dai Z, Song P, Yao J. Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules. Biomed Eng Online 2023; 22:112. [PMID: 38037082 PMCID: PMC10687925 DOI: 10.1186/s12938-023-01180-1] [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: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023] Open
Abstract
PURPOSE To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. METHODS A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). RESULTS The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. CONCLUSIONS A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.
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Affiliation(s)
- Haitao Sun
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Chunling Zhang
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Aimei Ouyang
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Zhengjun Dai
- Scientific Research Department of Huiying Medical Technology Co., Ltd, 66 Xixiaokou Road, Haidian District, Beijing, 100192, China
| | - Peiji Song
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Jian Yao
- Medical Imaging Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Huaiyin District, Jinan, 250021, Shandong Province, China.
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Xue W, Kong L, Zhang X, Xin Z, Zhao Q, He J, Wu W, Duan G. Tumor blood vessel in 3D reconstruction CT imaging as an risk indicator for growth of pulmonary nodule with ground-glass opacity. J Cardiothorac Surg 2023; 18:333. [PMID: 37968739 PMCID: PMC10647107 DOI: 10.1186/s13019-023-02423-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 11/03/2023] [Indexed: 11/17/2023] Open
Abstract
OBJECTIVE Despite the vital role of blood perfusion in tumor progression, in patients with persistent pulmonary nodule with ground-glass opacity (GGO) is still unclear. This study aims to investigate the relationship between tumor blood vessel and the growth of persistent malignant pulmonary nodules with ground-glass opacity (GGO). METHODS We collected 116 cases with persistent malignant pulmonary nodules, including 62 patients as stable versus 54 patients in the growth group, from 2017 to 2021. Three statistical methods of logistic regression model, Kaplan-Meier analysis regression analysis were used to explore the potential risk factors for growth of malignant pulmonary nodules with GGO. RESULTS Multivariate variables logistic regression analysis and Kaplan-Meier analysis identified that tumor blood vessel diameter (p = 0.013) was an significant risk factor in the growth of nodules and Cut-off value of tumor blood vessel diameter was 0.9 mm with its specificity 82.3% and sensitivity 66.7%.While in subgroup analysis, for the GGO CTR < 0.5[C(the maximum diameter of consolidation in tumor)/T(the maximum diameter of the whole tumor including GGO) ratio], tumor blood vessel diameter (p = 0.027) was important during the growing processes of nodules. CONCLUSIONS The tumor blood vessel diameter of GGO lesion was closely associated with the growth of malignant pulmonary nodules. The results of this study would provide evidence for effective follow-up strategies for pulmonary nodule screening.
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Affiliation(s)
- Wenfei Xue
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
| | - Lingxin Kong
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
- Graduate School, Hebei Medical University, Shijiazhuang, 050000, China
| | - Xiaopeng Zhang
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
| | - Zhifei Xin
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
| | - Qingtao Zhao
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
| | - Jie He
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
| | - Wenbo Wu
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China
| | - Guochen Duan
- Department of Thoracic Surgery, Hebei Province General Hospital, No. 348, Heping Road West, Xinhua District, Shijiazhuang, 050000, China.
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Ma ZJ, Ma ZX, Sun YL, Li DC, Jin L, Gao P, Li C, Li M. Prediction of subsolid pulmonary nodule growth rate using radiomics. BMC Med Imaging 2023; 23:177. [PMID: 37936095 PMCID: PMC10629176 DOI: 10.1186/s12880-023-01143-x] [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: 07/24/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Pulmonary nodule growth rate assessment is critical in the management of subsolid pulmonary nodules (SSNs) during clinical follow-up. The present study aimed to develop a model to predict the growth rate of SSNs. METHODS A total of 273 growing SSNs with clinical information and 857 computed tomography (CT) scans were retrospectively analyzed. The images were randomly divided into training and validation sets. All images were categorized into fast-growth (volume doubling time (VDT) ≤ 400 days) and slow-growth (VDT > 400 days) groups. Models for predicting the growth rate of SSNs were developed using radiomics and clinical features. The models' performance was evaluated using the area under the curve (AUC) values for the receiver operating characteristic curve. RESULTS The fast- and slow-growth groups included 108 and 749 scans, respectively, and 10 radiomics features and three radiographic features (nodule density, presence of spiculation, and presence of vascular changes) were selected to predict the growth rate of SSNs. The nomogram integrating radiomics and radiographic features (AUC = 0.928 and AUC = 0.905, respectively) performed better than the radiographic (AUC = 0.668 and AUC = 0.689, respectively) and radiomics (AUC = 0.888 and AUC = 0.816, respectively) models alone in both the training and validation sets. CONCLUSION The nomogram model developed by combining radiomics with radiographic features can predict the growth rate of SSNs more accurately than traditional radiographic models. It can also optimize clinical treatment decisions for patients with SSNs and improve their long-term management.
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Affiliation(s)
- Zong Jing Ma
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Zhuang Xuan Ma
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Ying Li Sun
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - De Chun Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Cheng Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Ming Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
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Gao R, Gao Y, Zhang J, Zhu C, Zhang Y, Yan C. 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. J Cancer Res Clin Oncol 2023; 149:15323-15333. [PMID: 37624396 DOI: 10.1007/s00432-023-05262-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
PURPOSE To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance. METHODS 187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. RESULTS The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05). CONCLUSION For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.
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Affiliation(s)
- Rongji Gao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Yinghua Gao
- Department of Pathology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Juan Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Chunyu Zhu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Yue Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China.
| | - Chengxin Yan
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, 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: 2] [Impact Index Per Article: 2.0] [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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Zhao B, Wang X, Sun K, Kang H, Zhang K, Yin H, Liu K, Xiao Y, Liu S. Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images. J Thorac Imaging 2023; 38:297-303. [PMID: 37531613 DOI: 10.1097/rti.0000000000000731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
PURPOSE To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness. MATERIALS AND METHODS This retrospective study included 512 confirmed ground-glass nodules from 474 patients with 241 precursor glandular lesions (PGL), 126 minimally invasive adenocarcinomas (MIA), and 145 invasive adenocarcinomas (IAC). The pulmonary blood vessels were reconstructed on noncontrast computed tomography images using deep learning-based region-segmentation and region-growing techniques. The presence of intranodular vessels was evaluated based on the automatic calculation of vessel prevalence, vascular categories, and vessel volume percentage. Further comparisons were made between different invasive groups by the Mantel-Haenszel χ 2 test, χ 2 test, and analysis of variance. RESULTS The detection rate of intranodular vessels in PGL (33.2%) was significantly lower than that of MIA (46.8%, P = 0.011) and IAC (55.2%, P < 0.001), while the vascular categories were similar (all P > 0.05). Vascular changes were more common in IAC and MIA than in PGL, mainly in increased vessel volume percentage (12.4 ± 19.0% vs. 6.3 ± 13.1% vs. 3.9 ± 9.4%, P < 0.001). The average intranodular artery and vein volume percentage of IAC (7.5 ± 14.0% and 5.0 ± 10.1%) was higher than that of PGL (2.1 ± 6.9% and 1.7 ± 5.8%) and MIA (3.2 ± 9.1% and 3.1 ± 8.7%), with statistical significance (all P < 0.05). CONCLUSIONS The quantitative analysis of intranodular vessels on noncontrast computed tomography images demonstrated that the ground-glass nodules with increased internal vessel prevalence and volume percentages had higher possibility of tumor invasiveness.
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Affiliation(s)
- Baolian Zhao
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Ke Sun
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd, Ocean International Center, Beijing, China
| | - Kai Zhang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd, Ocean International Center, Beijing, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd, Ocean International Center, Beijing, China
| | - Kai Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Yi Xiao
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
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Si-Mohamed SA, Boccalini S, Villien M, Yagil Y, Erhard K, Boussel L, Douek PC. First Experience With a Whole-Body Spectral Photon-Counting CT Clinical Prototype. Invest Radiol 2023; 58:459-471. [PMID: 36822663 PMCID: PMC10259214 DOI: 10.1097/rli.0000000000000965] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/20/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT Spectral photon-counting computed tomography (SPCCT) technology holds great promise for becoming the next generation of computed tomography (CT) systems. Its technical characteristics have many advantages over conventional CT imaging. For example, SPCCT provides better spatial resolution, greater dose efficiency for ultra-low-dose and low-dose protocols, and tissue contrast superior to that of conventional CT. In addition, SPCCT takes advantage of several known approaches in the field of spectral CT imaging, such as virtual monochromatic imaging and material decomposition imaging. In addition, SPCCT takes advantage of a new approach in this field, known as K-edge imaging, which allows specific and quantitative imaging of a heavy atom-based contrast agent. Hence, the high potential of SPCCT systems supports their ongoing investigation in clinical research settings. In this review, we propose an overview of our clinical research experience of a whole-body SPCCT clinical prototype, to give an insight into the potential benefits for clinical human imaging on image quality, diagnostic confidence, and new approaches in spectral CT imaging.
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Affiliation(s)
- Salim A. Si-Mohamed
- From the University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, Villeurbanne, France
- Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, Bron, France
| | - Sara Boccalini
- From the University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, Villeurbanne, France
- Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, Bron, France
| | | | | | | | - Loic Boussel
- From the University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, Villeurbanne, France
- Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, Bron, France
| | - Philippe C. Douek
- From the University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, Villeurbanne, France
- Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, Bron, France
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13
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Song F, Fu B, Liu M, Liu X, Liu S, Lv F. Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study. Diagnostics (Basel) 2023; 13:2210. [PMID: 37443604 DOI: 10.3390/diagnostics13132210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/12/2023] [Accepted: 06/16/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. METHODS The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN-vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. RESULTS Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p < 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). CONCLUSIONS In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022.
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Affiliation(s)
- Feipeng Song
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Binjie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Mengxi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Xiangling Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Sizhu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
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Park J, Doo KW, Sung YE, Jung JI, Chang S. Computed Tomography Findings for Predicting Invasiveness of Lung Adenocarcinomas Manifesting as Pure Ground-Glass Nodules. Can Assoc Radiol J 2023; 74:137-146. [PMID: 35840350 DOI: 10.1177/08465371221110913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Purpose: To comprehensively evaluate qualitative and quantitative features for predicting invasiveness of pure ground-glass nodules (pGGNs) using multiplanar computed tomography. Methods: Ninety-three resected pGGNs (16 atypical adenomatous hyperplasia [AAH], 18 adenocarcinoma in situ [AIS], 31 minimally invasive adenocarcinoma [MIA], and 28 invasive adenocarcinoma [IA]) were retrospectively included. Two radiologists analyzed qualitative and quantitative features on three standard planes. Univariable and multivariable logistic regression analyses were performed to identify features to distinguish the pre-invasive (AAH/AIS) from the invasive (MIA/IA) group. Results: Tumor size showed high area under the curve (AUC) for predicting invasiveness (.860, .863, .874, and .893, for axial long diameter [AXLD], multiplanar long diameter, mean diameter, and volume, respectively). The AUC for AXLD (cutoff, 11 mm) was comparable to that of the volume (P = .202). The invasive group had a significantly higher number of qualitative features than the pre-invasive group, regardless of tumor size. Six out of 59 invasive nodules (10.2%) were smaller than 11 mm, and all had at least one qualitative feature. pGGNs smaller than 11 mm without any qualitative features (n = 16) were all pre-invasive. In multivariable analysis, AXLD, vessel change, and the presence or number of qualitative features were independent predictors for invasiveness. The model with AXLD and the number of qualitative features achieved the highest AUC (.902, 95% confidence interval .833-.971). Conclusion: In adenocarcinomas manifesting as pGGNs on computed tomography, AXLD and the number of qualitative features are independent risk factors for invasiveness; small pGGNs (<11 mm) without qualitative features have low probability of invasiveness.
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Affiliation(s)
- Jeaneun Park
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyung Won Doo
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Yeoun Eun Sung
- Department of Hospital Pathology, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jung Im Jung
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Suyon Chang
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
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Li Y, Liu J, Yang X, Xu F, Wang L, He C, Lin L, Qing H, Ren J, Zhou P. Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:191-202. [PMID: 36637740 DOI: 10.1007/s11547-023-01591-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
PURPOSE Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND METHODS A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. RESULTS No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). CONCLUSIONS The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Fuyang Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Lu Wang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Libo Lin
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China.
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Tan M, Ma W, Yang Y, Duan S, Jin L, Wu Y, Li M. Predictive value of peritumour radiomics in the diagnosis of benign and malignant pulmonary nodules with halo sign. Clin Radiol 2023; 78:e52-e62. [PMID: 36460488 DOI: 10.1016/j.crad.2022.09.130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/05/2022] [Accepted: 09/26/2022] [Indexed: 12/03/2022]
Abstract
AIM To evaluate peritumour radiomics in predicting benign and malignant pulmonary nodules with halo sign. MATERIALS AND METHODS In this retrospective study, 305 pulmonary nodules with halo sign (benign, 120; adenocarcinoma, 185) were collected. Manual segmentation was used to mark the gross tumour volume (GTV) and the peritumour volume (PTV) was established by uniform dilation (1 cm) of the tumour area in three dimensions. The GTV and PTV radiomic features were combined to produce the gross tumour and peritumour volume (GPTV). The minimum-redundancy maximum-relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) algorithm were used to eliminate redundant radiomic features. Predictive models combined with clinical features and radiomic signatures were established. Multivarible logistic regression analysis was used to establish the combined model and develop a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the model. RESULTS In the testing cohort, the area under the ROC curve (AUC) of the GTV, PTV, and GPTV radiomic models was 0.701 (95% CI: 0.589-0.814), 0.674 (95% CI: 0.557-0.791) and 0.755 (95% CI: 0.643-0.867), respectively. The AUC of the nomogram model based on clinical and GPTV radiomic signatures was 0.804 (95% CI: 0.707-0.901). CONCLUSION The nomogram model based on clinical and GPTV radiomic signatures can better predict benign and malignant pulmonary nodules with halo signs, demonstrating that the model has potential as a convenient and effective auxiliary diagnostic tool for radiologists.
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Affiliation(s)
- M Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Chengdu Second People's Hospital, Chengdu, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Shanghai Chest Hospital, Shanghai, China
| | - Y Yang
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Y Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 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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18
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Liu XL, Lv FJ, Fu BJ, Lin RY, Li WJ, Chu ZG. Correlations Between Inflammatory Cell Infiltration and Relative Density and the Boundary Manifestation of Pulmonary Non-Neoplastic Ground Glass Nodules. J Inflamm Res 2023; 16:1147-1155. [PMID: 36945317 PMCID: PMC10024903 DOI: 10.2147/jir.s399953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Purpose To investigate the influence factors for the various boundary manifestations of pulmonary non-neoplastic ground glass nodules (GGNs) on computed tomography (CT). Materials and Methods From January 2015 to March 2022, a total of 280 patients with 318 non-neoplastic GGNs were enrolled. The correlations between degree of inflammatory cell infiltration and relative density (ΔCT) and the boundary manifestations of lesions were evaluated, respectively. Results Nongranulomatous nodules (283, 89.0%) with fibrous tissue proliferation and/or inflammatory cells as the predominant pathological findings were the most common non-neoplastic GGNs, followed by granulomatous nodules (28, 8.8%). Among nongranulomatous GGNs, cases with more and less/no inflammatory cells were 15 (10.9%) and 122 (89.1%) in 137 well-defined ones with smooth margin, 16 (24.6%) and 49 (75.4%) in 65 well-defined ones with coarse margin, 43 (91.5%) and 4 (8.5%) in 47 ill-defined ones with higher ΔCT (>151HU), and 4 (11.8%) and 30 (88.2%) in 34 ill-defined ones with lower ΔCT (< 151HU). The proportion of cases with more inflammatory cells in well-defined nodules was similar to that in ill-defined ones with lower ΔCT (P = 0.587) but significantly lower than that in ill-defined ones with higher ΔCT (P < 0.001). Among the granulomatous nodules, ill-defined cases with higher ΔCT (16, 57.1%) were the most common, and they (7/8, 87.5%) frequently had changes during short-term follow-up. Conclusion Nongranulomatous nodules are the most common non-neoplastic GGNs, their diverse boundary manifestations closely correlate with degree of inflammatory cell infiltration and density difference.
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Affiliation(s)
- Xiang-Ling Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Correspondence: Zhi-Gang Chu, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86 18723032809, Fax +86 23 68811487, Email
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19
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A Nomogram Incorporating Tumor-Related Vessels for Differentiating Adenocarcinoma In Situ from Minimally Invasive and Invasive Adenocarcinoma Appearing as Subsolid Nodules. Acad Radiol 2022; 30:928-939. [PMID: 36150965 DOI: 10.1016/j.acra.2022.08.024] [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: 05/22/2022] [Revised: 08/08/2022] [Accepted: 08/20/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVES To develop a nomogram incorporating the quantity of tumor-related vessels (TRVs) and conventional CT features (CCTFs) for the preoperative differentiation of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) appearing as subsolid nodules. METHODS High-resolution CT target scans of 274 subsolid nodules from 268 patients were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. A nomogram incorporating CCTFs with the category of TRVs (CTRVs, using TRVs as categorical variables) and a final nomogram combining the number of TRVs (QTRVs) and CCTFs were constructed using multivariable logistic regression analysis. The performance levels of the two nomograms were evaluated and validated on the training and validation datasets and then compared. RESULTS The CCTF-QTRV nomogram incorporating abnormal air bronchogram, density, number of dilated and distorted vessels and number of adherent vessels showed more favorable predictive efficacy than the CCTF-CTRV nomogram (training cohort: area under the curve (AUC) = 0.893 vs. 0.844, validation cohort: AUC = 0.871 vs. 0.807). The net reclassification index (training cohort: 0.188, validation cohort: 0.326) and the integrated discrimination improvement values (training cohort: 0.091, validation cohort: 0.125) indicated that the CCTF-QTRV nomogram performed significantly better discriminative ability than the CCTF-CTRV nomogram (all p-value < 0.05). CONCLUSIONS The nomogram incorporating the QTRVs and CCTFs showed favorable predictive efficacy for differentiating AIS from MIA-IAC appearing as subsolid nodules and may serve as a potential tool to provide individual care for these patients.
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20
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Guo CR, Han R, Xue F, Xu L, Ren WG, Li M, Feng Z, Hu BC, Peng ZM. Expression and clinical significance of CD31, CD34, and CD105 in pulmonary ground glass nodules with different vascular manifestations on CT. Front Oncol 2022; 12:956451. [PMID: 36185269 PMCID: PMC9521677 DOI: 10.3389/fonc.2022.956451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Blood vessel passage on CT exerts a vital part in early diagnosis as well as treatment of carcinoma of the lungs. Intratumoral microvascular density (iMVD) has gradually become the focus of research on biological behavior, appearance, and evolution of malignant tumors nowadays. The aim of this paper was to verify whether there is a correlation between the iMVD and the vascular morphology of ground glass nodules (GGNs). A total of 109 patients with pulmonary GGN were classified into three groups (I,II, and III) according to the vascular morphology on CT, and their expression of CD31-, CD34-, and CD105-labeled iMVD was detected by the streptoavidin–biotin method, statistically analyzing the iMVD values of each group. The expression of CD31, CD34, and CD105 in different lung tissues was significantly different, with remarkably higher iMVD in lung cancer tissues than in adjacent normal lung tissues. In the imaging sort of types I, II, and III according to the means of vascular passage, the iMVD expression of CD31, CD34, and CD105 was significantly different between groups. These data suggest that the presence and the abnormal morphology of vessels seen within GGNs indicate the occurrence and progression of lung cancer in pathology. It offers a strong theoretical foundation for early diagnosis of carcinoma of the lungs, thus providing a more precise clinical diagnosis and prognosis of early-stage lung cancer.
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Affiliation(s)
- Chen-ran Guo
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong University, Jinan, China
| | - Rui Han
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Feng Xue
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong University, Jinan, China
| | - Lin Xu
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Wan-gang Ren
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Meng Li
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Zhen Feng
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Ben-chuang Hu
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Zhong-min Peng
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong University, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
- *Correspondence: Zhong-min Peng,
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21
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Song Q, Song B, Li X, Wang B, Li Y, Chen W, Wang Z, Wang X, Yu Y, Min X, Ma D. A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification. Cancer Imaging 2022; 22:46. [PMID: 36064495 PMCID: PMC9446567 DOI: 10.1186/s40644-022-00483-1] [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: 03/01/2022] [Accepted: 08/23/2022] [Indexed: 11/10/2022] Open
Abstract
Purpose To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. Methods A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves. Results Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit. Conclusions A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00483-1.
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Affiliation(s)
- Qilong Song
- Department of Radiology, Anhui Chest Hospital, Hefei, China.,Clinical College of Chest, Anhui Medical University, Hefei, China
| | - Biao Song
- Department of Radiology, Anhui Chest Hospital, Hefei, China.,Clinical College of Chest, Anhui Medical University, Hefei, China
| | - Xiaohu Li
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bin Wang
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Yuan Li
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Wu Chen
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Zhaohua Wang
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Xu Wang
- Department of Radiology, Anhui Chest Hospital, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Xuhong Min
- Department of Radiology, Anhui Chest Hospital, Hefei, China. .,Clinical College of Chest, Anhui Medical University, Hefei, China.
| | - Dongchun Ma
- Clinical College of Chest, Anhui Medical University, Hefei, China. .,Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, China.
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22
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Ding Y, He C, Zhao X, Xue S, Tang J. Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests. Front Med (Lausanne) 2022; 9:936595. [PMID: 36059824 PMCID: PMC9433577 DOI: 10.3389/fmed.2022.936595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.
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Affiliation(s)
- Yizong Ding
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunming He
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojing Zhao
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Song Xue
- Department of Cardiovascular Surgery, Reiji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Tang
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Jian Tang,
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23
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Yang Y, Tan M, Ma W, Duan S, Huang X, Jin L, Tang L, Li M. Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram. Clin Radiol 2022; 77:e680-e688. [PMID: 35718542 DOI: 10.1016/j.crad.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/05/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
AIM To develop and validate a radiomics nomogram for prediction of degree of differentiation in lung adenocarcinoma presenting as sub-solid or solid nodules. MATERIALS AND METHODS A total of 438 patients with histopathologically confirmed adenocarcinoma (248 non-poorly differentiated and 190 poorly differentiated) were divided into training cohort (n=235) and internal validation cohort (n=203) according to surgery sequence. Sixty patients form public TCIA dataset were selected for external validation. One thousand, two hundred and eighteen radiomics features were extracted from each volumetric region of interest and a least absolute shrinkage and selection operator logistic regression was applied to select meaningful radiomic features for building a radiomics score (Rad-score) model. A nomogram model incorporating the Rad-score and type was established after multivariable logistic regression. The discrimination efficiency, calibration efficacy, and clinical utility value of the nomogram were evaluated. RESULTS The Rad-score model could predict the differentiation degree of lung adenocarcinoma with an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78-0.89) in the internal validation cohort. The AUC of the nomogram and radiographic model was 0.86 (95% CI: 0.80-0.91), 0.78 (95% CI: 0.72-0.84) in the internal validation cohort respectively. The AUC of the nomogram in the external validation cohort was 0.73 (95% CI: 0.58-0.88). Delong's test showed that the nomogram performed better than radiographic features alone (p=0.001). CONCLUSIONS The proposed radiomics nomogram has the potential to predict the differentiation degree of lung adenocarcinoma preoperatively.
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Affiliation(s)
- Y Yang
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - M Tan
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - X Huang
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - L Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China.
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24
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Liu J, Yang X, Li Y, Xu H, He C, Qing H, Ren J, Zhou P. Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening. Quant Imaging Med Surg 2022; 12:2917-2931. [PMID: 35502397 PMCID: PMC9014141 DOI: 10.21037/qims-21-912] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 02/03/2022] [Indexed: 08/04/2023]
Abstract
BACKGROUND Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS). METHODS A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. RESULTS The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively). CONCLUSIONS The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.
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Affiliation(s)
- Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Li
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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25
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He XQ, Li X, Wu Y, Wu S, Luo TY, Lv FJ, Li Q. Differential Diagnosis of Nonabsorbable Inflammatory and Malignant Subsolid Nodules with a Solid Component ≤5 mm. J Inflamm Res 2022; 15:1785-1796. [PMID: 35300212 PMCID: PMC8923683 DOI: 10.2147/jir.s355848] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 03/01/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To investigate the differential clinical and computed tomography (CT) characteristics of pulmonary nonabsorbable inflammatory and malignant subsolid nodules (SSNs) with a solid component ≤5 mm. Patients and Methods We retrospectively analyzed 576 consecutive patients who underwent surgical resection and had SSNs with a solid component ≤5 mm on CT images. These patients were divided into inflammatory and malignant groups according to pathology. Their clinical and imaging data were analyzed and compared. Multiple logistic regression analysis was used to identify independent prognostic factors differentiating inflammatory from malignant SSNs. Furthermore, 146 consecutive patients were included as internal validation cohort to test the prediction efficiency of this model. Results Significant differences in 11 clinical characteristics and CT features were found between both groups (P < 0.05). Presence of respiratory symptoms, distribution of middle/lower lobe, irregular shape, part-solid nodule (PSNs), CT value of ground-glass opacity (GGO) areas <−657 Hu, presence of abnormal intra-nodular vessel sign, and interlobular septal thickening were the most effective factors for diagnosing nonabsorbable inflammatory SSNs, with an AUC (95% CI), accuracy, sensitivity, and specificity of 0.843 (95% CI: 0.811–0.872), 89.76%, 72.86%, and 81.23%, respectively. The internal validation cohort obtained an AUC (95% CI), accuracy, sensitivity, and specificity of 0.830 (95% CI: 0.759–0.887), 83.56%, 73.91%, and 76.42%, respectively. Conclusion Nonabsorbable inflammatory and malignant SSNs with a solid component ≤5 mm exhibited different clinical and imaging characteristics.
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Affiliation(s)
- Xiao-Qun He
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xian Li
- Department of Pathology, Chongqing Medical University, Chongqing, People’s Republic of China
| | - Yan Wu
- Nursing School, Chongqing Medical University, Chongqing, People’s Republic of China
| | - Shun Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Tian-You Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Correspondence: Qi Li, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, People’s Republic of China, Tel +86 15823408652, Fax +86 23 68811487, Email
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26
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Zheng T, Ren H, Wu Y, Wang J. Association between clinical characteristics and CT findings in patients with coronavirus disease-2019. Medicine (Baltimore) 2021; 100:e27435. [PMID: 34871209 PMCID: PMC8568448 DOI: 10.1097/md.0000000000027435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 05/20/2021] [Accepted: 09/17/2021] [Indexed: 01/08/2023] Open
Abstract
ABSTRACT This retrospective study was to investigate the association between clinical characteristics and computerized tomography (CT) findings in patients with coronavirus disease-2019 (COVID-19). The clinical data of COVID-19 patients were retrospectively analyzed. Spearman correlation analysis was used to identify the correlation. Totally 209 consecutive COVID-19 patients were eligible for the study, with the mean age of 47.53 ± 13.52 years. At onset of the disease, the most common symptoms were fever (85.65%) and cough (61.24%). The CT features of COVID-19 included pulmonary, bronchial, and pleural changes, with the significant pulmonary presentation of ground-glass opacification (93.30%), consolidation (48.80%), ground-glass opacification plus a reticular pattern (54.07%), telangiectasia (84.21%), and pulmonary fibrotic streaks (49.76%). Spearman analysis showed that the CT findings had significantly inverse associations with the platelets, lymphocyte counts, and sodium levels, but were positively related to the age, erythrocyte sedimentation rate, D-dimer, lactic dehydrogenase, α-hydroxybutyrate dehydrogenase, and C-reactive protein levels (P < .05). In conclusion, the severity of lung abnormalities on CT in COVID-19 patients is inversely associated with the platelets, lymphocyte count, and sodium levels, whereas positively with the age, erythrocyte sedimentation rate, D-dimer, lactic dehydrogenase, hydroxybutyrate dehydrogenase, and C-reactive protein levels.
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Affiliation(s)
- Ting Zheng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hao Ren
- Department of Radiology, Xiangyang Central Hospital, The Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yongjuan Wu
- Department of Radiology, Xiangyang Central Hospital, The Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Jiangtao Wang
- Department of Radiology, Xiangyang Central Hospital, The Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
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27
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Zhang P, Li T, Tao X, Jin X, Zhao S. HRCT features between lepidic-predominant type and other pathological subtypes in early-stage invasive pulmonary adenocarcinoma appearing as a ground-glass nodule. BMC Cancer 2021; 21:1124. [PMID: 34666705 PMCID: PMC8524968 DOI: 10.1186/s12885-021-08821-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 10/01/2021] [Indexed: 01/15/2023] Open
Abstract
Background Different pathological subtypes of invasive pulmonary adenocarcinoma (IPA) have different surgical methods and heterogeneous prognosis. It is essential to clarify IPA subtypes before operation and high-resolution computed tomography (HRCT) plays a very important role in this regard. We aimed to investigate the HRCT features of lepidic-predominant type and other pathological subtypes of early-stage (T1N0M0) IPA appearing as a ground-glass nodule (GGN). Methods We performed a retrospective analysis on clinical data and HRCT features of 630 lesions in 589 patients with pathologically confirmed IPA (invasive foci > 5 mm) appearing as pure GGN (pGGN) and mixed GGN (mGGN) with consolidation-to-tumor ratio (CTR) ≤0.5 from January to December 2019. All GGNs were classified as lepidic-predominant adenocarcinoma (LPA) and nonlepidic-predominant adenocarcinoma (n-LPA) groups. Univariate analysis was performed to analyze the differences of clinical data and HRCT features between the LPA and n-LPA groups. Multivariate analysis was conducted to determine the variables to distinguish the LPA from n-LPA group independently. The diagnostic performance of different parameters was compared using receiver operating characteristic curves. Results In total, 367 GGNs in the LPA group and 263 GGNs in the n-LPA group were identified. In the univariate analysis, the CTR, mean CT values, and mean diameters as well as mixed GGN, deep lobulation, spiculation, vascular change, bronchial change, and tumor–lung interface were smaller in the LPA group than in the n-LPA group (P < 0.05). Logistic regression model was reconstructed including the mean CT value, CTR, deep lobulation, spiculation, vascular change, and bronchial change (P < 0.05). Area under the curve of the logistic regression model for differentiating LPA and n-LPA was 0.840 (76.4% sensitivity, 78.7% specificity), which was significantly higher than that of the mean CT value or CTR. Conclusions Deep lobulation, spiculation, vascular change, and bronchial change, CT value > − 472.5 HU and CTR > 27.4% may indicate nonlepidic predominant invasive pulmonary adenocarcinoma in GGNs.
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Affiliation(s)
- Pengju Zhang
- Department of Radiology, Fourth Medical Center of PLA General Hospital, 51 Fucheng Road, Beijing, 100048, China
| | - Tianran Li
- Department of Radiology, Fourth Medical Center of PLA General Hospital, 51 Fucheng Road, Beijing, 100048, China
| | - Xuemin Tao
- Department of Radiology, First Medical Center of PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xin Jin
- Department of Radiology, First Medical Center of PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Shaohong Zhao
- Department of Radiology, First Medical Center of PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
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Predictors of Invasive Adenocarcinomas among Pure Ground-Glass Nodules Less Than 2 cm in Diameter. Cancers (Basel) 2021; 13:cancers13163945. [PMID: 34439100 PMCID: PMC8391557 DOI: 10.3390/cancers13163945] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/26/2021] [Accepted: 08/02/2021] [Indexed: 12/19/2022] Open
Abstract
Simple Summary Benign lesions, atypical adenomatous hyperplasia, and malignancies such as adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma may feature pure ground-glass nodules on chest CT images, and the prognosis of patients with invasive adenocarcinoma is worse than others. The early detection and adequate management of invasive adenocarcinoma is crucial, but the pathology diagnosis of small nodules is difficult to obtain without surgery. Our study aimed to analyze the CT characteristics of pure ground-glass nodules <2 cm for the identification of invasive adenocarcinomas. A total of 181 nodules in 171 patients were enrolled. The larger size, lobulation, and air cavity were significantly more common in invasive adenocarcinoma. The air cavity is the significant predictor in multivariate analysis. In conclusion, the possibility of invasive adenocarcinoma is higher in a pure ground-glass nodules when it is associated with a larger size, lobulation, and air cavity. Abstract Benign lesions, atypical adenomatous hyperplasia (AAH), and malignancies such as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) may feature a pure ground-glass nodule (pGGN) on a thin-slide computed tomography (CT) image. According to the World Health Organization (WHO) classification for lung cancer, the prognosis of patients with IA is worse than those with AIS and MIA. It is relatively risky to perform a core needle biopsy of a pGGN less than 2 cm to obtain a reliable pathological diagnosis. The early and adequate management of patients with IA may provide a favorable prognosis. This study aimed to disclose suggestive signs of CT to accurately predict IA among the pGGNs. A total of 181 pGGNs of less than 2 cm, in 171 patients who had preoperative CT-guided localization for surgical excision of a lung nodule between December 2013 and August 2019, were enrolled. All had CT images of 0.625 mm slice thickness during CT-guided intervention to confirm that the nodules were purely ground glass. The clinical data, CT images, and pathological reports of those 171 patients were reviewed. The CT findings of pGGNs including the location, the maximal diameter in the long axis (size-L), the maximal short axis diameter perpendicular to the size-L (size-S), and the mean value of long and short axis diameters (size-M), internal content, shape, interface, margin, lobulation, spiculation, air cavity, vessel relationship, and pleural retraction were recorded and analyzed. The final pathological diagnoses of the 181 pGGNs comprised 29 benign nodules, 14 AAHs, 25 AISs, 55 MIAs, and 58 IAs. Statistical analysis showed that there were significant differences among the aforementioned five groups with respect to size-L, size-S, and size-M (p = 0.029, 0.043, 0.025, respectively). In the univariate analysis, there were significant differences between the invasive adenocarcinomas and the non-invasive adenocarcinomas with respect to the size-L, size-S, size-M, lobulation, and air cavity (p = 0.009, 0.016, 0.008, 0.031, 0.004, respectively) between the invasive adenocarcinomas and the non-invasive adenocarcinomas. The receiver operating characteristic (ROC) curve of size for discriminating invasive adenocarcinoma also revealed similar area under curve (AUC) values among size-L (0.620), size-S (0.614), and size-M (0.623). The cut-off value of 7 mm in size-M had a sensitivity of 50.0% and a specificity of 76.4% for detecting IAs. In the multivariate analysis, the presence of air cavity was a significant predictor of IA (p = 0.042). In conclusion, the possibility of IA is higher in a pGGN when it is associated with a larger size, lobulation, and air cavity. The air cavity is the significant predictor of IA.
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Malguria N, Yen LH, Lin T, Hussein A, Fishman EK. Role of Chest CT in COVID-19. J Clin Imaging Sci 2021; 11:30. [PMID: 34221639 PMCID: PMC8247924 DOI: 10.25259/jcis_138_2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 04/19/2021] [Indexed: 01/08/2023] Open
Abstract
In December 2019, a disease attributed to a new severe acute respiratory syndrome coronavirus 2, and named coronavirus disease 2019 (COVID-19), broke out in Wuhan, China and has spread rapidly throughout the world. CT has been advocated in selected indications as a tool toward rapid and early diagnosis. The CT patterns of COVID-19 include ground glass opacities GGO, consolidation, and crazy paving. Additional signs include a “rounded morphology” of lesions, vascular enlargement sign, nodules, and fibrous stripe. Signs of healing and organization include subpleural bands, a reticular pattern, reversed halo sign and traction bronchiectasis. Cavitation and tree in bud signs are absent and pleural effusions are rare. There is a high incidence of pulmonary embolism associated with COVID-19. CT findings in COVID-19 appear to follow a predictable timeline with maximal involvement approximately 6–11 days after symptom onset. The stages of evolution include early stage (days 0–4) with GGO being the predominant abnormality, progressive stage (days 5–8) with increasing crazy paving; and peak stage (days 9–13) with predominance of consolidation and absorption phase (after day 14) with gradual absorption of consolidation with residual GGO and subpleural bands. CT findings in COVID-19 have a high sensitivity and low specificity, determined to be 98% and 25% in a retrospective study of 1014 patients. The low specificity of CT for the diagnosis of COVID-19 pneumonia is due to the overlap of CT findings with other viral pneumonias and other infections, lung involvement in connective tissue disorders, drug reaction, pulmonary edema, and hemorrhage.
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Affiliation(s)
- Nagina Malguria
- Department of Radiology, Johns Hopkins Hospital, Baltimore, Maryland, United States
| | - Li-Hsiang Yen
- Department of Radiology, University of Rochester, Rochester, New York, United States
| | - Tony Lin
- Department of Radiology, Johns Hopkins Hospital, Baltimore, Maryland, United States
| | - Amira Hussein
- Department of Radiology, University of Rochester, Rochester, New York, United States
| | - Elliot K Fishman
- Department of Radiology, Johns Hopkins Hospital, Baltimore, Maryland, United States
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Zhang T, Wang Y, Sun Y, Yuan M, Zhong Y, Li H, Yu T, Wang J. High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules. Eur J Radiol 2021; 141:109810. [PMID: 34102564 DOI: 10.1016/j.ejrad.2021.109810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/19/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVE To investigate whether 3D convolutional neural network (CNN) is able to enhance the classification performance of radiologists in classifying pulmonary non-solid nodules (NSNs). MATERIALS AND METHODS Data of patients with solitary NSNs and diagnosed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC) in pathological after surgical resection were analyzed retrospectively. Ultimately, 532 patients in our institution were included in the study: 427 cases (144 AIS, 167 MIA, 116 IAC) were assigned to training dataset and 105 cases (36 AIS, 41 MIA and 28 IAC) were assigned to validation dataset. For external validation, 177 patients (60 AIS, 69 MIA and 48 IAC) from another hospital were assigned to testing dataset. The clinical and morphological characteristics of NSNs were established as radiologists' model. The trained classification model based on 3D CNN was used to identify NSNs types automatically. The evaluation and comparison on classification performance of the two models and CNN + radiologists' model were performed via receiver operating curve (ROC) analysis and integrated discrimination improvement (IDI) index. The Akaike information criterion (AIC) was calculated to find the best-fit model. RESULTS In external testing dataset, radiologists' model showed inferior classification performance than CNN model both in discriminating AIS from MIA-IAC and AIS-MIA from IAC (the area under the ROC curve (Az value), 0.693 vs 0.820, P = 0.011; 0.746 vs 0.833, P = 0.026, respectively). However, combining CNN significantly enhanced the classification performance of radiologists and exhibited higher Az values than CNN model alone (Az values, 0.893 vs 0.820, P < 0.001; 0.906 vs 0.833, P < 0.001, respectively). The IDI index further confirmed CNN's contribution to radiologists in classifying NSNs (IDI = 25.8 % (18.3-46.1 %), P < 0.001; IDI = 30.1 % (26.1-45.2 %), P < 0.001, respectively). The CNN + radiologists' model also provided the best fit over radiologists' model and CNN model alone (AIC value 63.3 % vs. 29.5 %, 49.5 %, P < 0.001; 69.2 % vs. 34.9 %, 53.6 %, P < 0.001, respectively). CONCLUSION CNN successfully classified NSNs based on CT images and its classification performance were superior to radiologists' model. But the classification performance of radiologists can be significantly enhanced when combined with CNN in classifying NSNs.
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Affiliation(s)
- Teng Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China.
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Yan Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Tongfu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Jie Wang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Fu BJ, Lv FJ, Li WJ, Lin RY, Zheng YN, Chu ZG. Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules. Insights Imaging 2021; 12:65. [PMID: 34037864 PMCID: PMC8155149 DOI: 10.1186/s13244-021-01012-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/07/2021] [Indexed: 11/10/2022] Open
Abstract
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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Affiliation(s)
- Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, China
| | - Yi-Neng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, China.
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Bianco A, Valente T, Perrotta F, Stellato E, Brunese L, Wood BJ, Carrafiello G, Parrella R. Remarkable vessel enlargement within lung consolidation in COVID-19 compared to AH1N1 pneumonia: A retrospective study in Italy. Heliyon 2021; 7:e07112. [PMID: 34036187 PMCID: PMC8135228 DOI: 10.1016/j.heliyon.2021.e07112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 12/12/2020] [Accepted: 05/17/2021] [Indexed: 12/23/2022] Open
Abstract
Purpose To investigate the early CT findings in COVID-19 pneumonia as compared to influenza A virus H1N1 (AH1N1), with focus on vascular enlargement within consolidation or ground glass opacity (GGO) areas. Methods 50 patients with COVID-19 pneumonia were retrospectively compared to 50 patients with AH1N1 pneumonia diagnosed during the 2009 pandemic. Two radiologists reviewed chest CT scans independently and blindly, with discordance resolved by consensus. Dilated or tortuous vessels within hyperdense lesions were recorded. Results COVID-19 pneumonia presented with bilateral (96%), peripheral areas of GGO (22%), consolidation (4%) or combined GGO-consolidation (74%). The vascular enlargement sign in COVID-19 pneumonia was much more commonly present in COVID-19 (45/50, 90%) versus AH1N1 pneumonia (12/50, 24%) (p < 0.001). Vascular enlargement was more often present in lower lobes with a peripheral distribution. Conclusions Vascular enlargement in consolidative/GGO areas may represent a reasonably common early CT marker in COVID-19 patients and is of uncertain etiology. Although speculative, theoretical mechanisms could potentially reflect acute inflammatory changes, pulmonary endothelial activation, or acute stasis. Further studies are necessary to verify specificity and to study if prognostic for clinical outcomes.
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Affiliation(s)
- Andrea Bianco
- Department of Translational Medical and Surgical Science, University of Campania Luigi Vanvitelli, Naples, Italy.,COViD Unit PNL Vanvitelli, Hospital Monaldi, A.O. R.N. dei Colli, Naples, Il Italy
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, A.O.R.N. dei Colli, Naples, Italy
| | - Fabio Perrotta
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Italy
| | - Elvira Stellato
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Italy
| | - Brad J Wood
- Center for Interventional Oncology, Radiology and Imaging Science, National Institutes of Health, Bethesda, USA
| | - Gianpaolo Carrafiello
- Radiology Department, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | - Roberto Parrella
- Department of Infectious Diseases, COVID Unit D. Cotugno Hospital, A.O.R.N. dei Colli, Naples, Italy
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Wang X, Liu D, Zeng X, Jiang S, Li L, Yu T, Zhang J. Dual-energy CT quantitative parameters for the differentiation of benign from malignant lesions and the prediction of histopathological and molecular subtypes in breast cancer. Quant Imaging Med Surg 2021; 11:1946-1957. [PMID: 33936977 DOI: 10.21037/qims-20-825] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Dual-energy computed tomography (DECT) is widely used to characterize and differentiate tumors. However, data regarding its diagnostic performance for the characterization of breast tumors are limited. In this study, we assessed the diagnostic performance of quantitative parameters derived from DECT in differentiating benign from malignant lesions and predicting histopathological and molecular subtypes in patients with breast cancer. Methods Dual-phase contrast-enhanced DECT of the thorax was performed on participants with breast tumors. Conventional CT attenuation and DECT quantitative parameters, including normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λHu), and normalized effective atomic number (nZeff), were obtained and compared between benign and malignant lesions, invasive non-special carcinoma, and ductal carcinoma in situ (DCIS), and among the four molecular subtypes of breast cancer. The diagnostic performance of the quantitative parameters was analyzed using receiver operating characteristic (ROC) curves. Results This study included 130 participants with 161 breast lesions (44 benign and 117 malignant). In the arterial and venous phase, NICs, λHu, nZeff, and attenuation were higher in malignant lesions than benign lesions (all P<0.001). The venous phase λHu had the best differential diagnostic capability, with an area under the curve (AUC) of 0.90, a sensitivity of 84.1% (37 of 44), a specificity of 86.3% (101 of 117), and an accuracy of 85.7% (138 of 161). The NICs in the arterial and venous phases were higher in invasive non-special carcinoma than DCIS (both P<0.001). In terms of diagnostic performance, NIC in the venous phase had an AUC of 0.77, a sensitivity of 75.0% (12 of 16), a specificity of 81.2% (82 of 101), and an accuracy of 80.3% (94 of 117). The luminal A subtype produced a lower venous phase NIC, and arterial and venous phase nZeff than the non-luminal A subtype (AUC of 0.91 for the combination of these three parameters). Conclusions Dual-energy CT quantitative parameters are a feasible and valuable noninvasive means of differentiating between benign and malignant lesions, and predicting histopathological and molecular subtypes in patients with breast cancer.
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Affiliation(s)
- Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Xiangfei Zeng
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Shixi Jiang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Tao Yu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
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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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Tan M, Ma W, Sun Y, Gao P, Huang X, Lu J, Chen W, Wu Y, Jin L, Tang L, Kuang K, Li M. Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics. Front Oncol 2021; 11:658138. [PMID: 33937070 PMCID: PMC8082461 DOI: 10.3389/fonc.2021.658138] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/22/2021] [Indexed: 01/15/2023] Open
Abstract
Objectives To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. Methods From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. Results Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set. Conclusions The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.
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Affiliation(s)
- Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Xuemei Huang
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Jinjuan Lu
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Wufei Chen
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yue Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Lin Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
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Fu G, Yu H, Liu J, Xia T, Xiang L, Li P, Huang D, Lin L, Zhuang Y, Yang Y. Arc concave sign on thin-section computed tomography:A novel predictor for invasive pulmonary adenocarcinoma in pure ground-glass nodules. Eur J Radiol 2021; 139:109683. [PMID: 33836337 DOI: 10.1016/j.ejrad.2021.109683] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/01/2021] [Accepted: 03/23/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE We aimed to investigate the risk factors of invasive pulmonary adenocarcinoma, especially to report and validate the use of our newly identified arc concave sign in predicting invasiveness of pure ground-glass nodules (pGGNs). METHODS From January 2015 to August 2018, we retrospectively enrolled 302 patients with 306 pGGNs ≤ 20 mm pathologically confirmed (141 preinvasive lesions and 165 invasive lesions). Arc concave sign was defined as smooth and sunken part of the edge of the lesion on thin-section computed tomography (TSCT). The degree of arc concave sign was expressed by the arc chord distance to chord length ratio (AC-R); deep arc concave sign was defined as AC-R larger than the optimal cut-off value. Logistic regression analysis was used to identify the independent risk factors of invasiveness. RESULTS Arc concave sign was observed in 65 of 306 pGGNs (21.2 %), and deep arc concave sign (AC-R > 0.25) were more common in invasive lesions (P = 0.008). Under microscope, interlobular septal displacements were found at tumour surface. Multivariate analysis indicated that irregular shape (OR, 3.558; CI: 1.374-9.214), presence of deep arc concave sign (OR, 3.336; CI: 1.013-10.986), the largest diameter > 10.1 mm (OR, 4.607; CI: 2.584-8.212) and maximum density > -502 HU (OR, 6.301; CI: 3.562-11.148) were significant independent risk factors of invasive lesions. CONCLUSIONS Arc concave sign on TSCT is caused by interlobular septal displacement. The degree of arc concave sign can reflect the invasiveness of pGGNs. Invasive lesions can be effectively distinguished from preinvasive lesions by the presence of deep arc concave sign, irregular shape, the largest diameter > 10.1 mm and maximum density > -502 HU in pGGNs ≤ 20 mm.
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Affiliation(s)
- Gangze Fu
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Huibo Yu
- Department of Radiology, Xiangshan Affiliated Hospital of Wenzhou Medical University, Xiangshan, 315700, China
| | - Jinjin Liu
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Tianyi Xia
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Lanting Xiang
- Depatment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Peng Li
- Depatment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Dingpin Huang
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Liaoyi Lin
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Yuandi Zhuang
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Yunjun Yang
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China.
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Li WJ, Lv FJ, Tan YW, Fu BJ, Chu ZG. Pulmonary Benign Ground-Glass Nodules: CT Features and Pathological Findings. Int J Gen Med 2021; 14:581-590. [PMID: 33679139 PMCID: PMC7930605 DOI: 10.2147/ijgm.s298517] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 01/22/2021] [Indexed: 12/18/2022] Open
Abstract
Background Some pulmonary ground-glass nodules (GGNs) are benign and frequently misdiagnosed due to lack of understanding of their CT characteristics. This study aimed to reveal the CT features and corresponding pathological findings of pulmonary benign GGNs to help improve diagnostic accuracy. Patients and Methods From March 2016 to October 2019, patients with benign GGNs confirmed by operation or follow-up were enrolled retrospectively. According to overall CT manifestations, GGNs were classified into three types: I, GGO with internal high-attenuation zone; II, nodules lying on adjacent blood vessels; and other type, lesions without obvious common characteristics. CT features and pathological findings of each nodule type were evaluated. Results Among the 40 type I, 25 type II, and 14 other type GGNs, 24 (60.0%), 19 (76.0%), and 10 (71.4%) nodules were resected, respectively. Type I GGNs were usually irregular (25 of 40, 62.5%) with only one high-attenuation zone (38 of 40, 95.0%) (main pathological components: thickened alveolar walls with inflammatory cells, fibrous tissue, and exudation), which was usually centric (24 of 40, 60.0%), having blurred margin (38 of 40, 95.0%), and connecting to blood vessels (32 of 40, 80.0%). The peripheral GGO (main pathological component: a small amount of inflammatory cell infiltration with fibrous tissue proliferation) was usually ill-defined (28 of 40, 70.0%). Type II GGNs (main pathological components: focal interstitial fibrosis with or without inflammatory cell infiltration) lying on adjacent vessel branches were usually irregular (19 of 25, 76.0%) and well defined (16 of 25, 64.0%) but showed coarse margins (15 of 16, 93.8%). Other type GGNs had various CT manifestations but their pathological findings were similar to that of type II. Conclusion For subsolid nodules with CT features manifested in type I or II GGNs, follow-up should be firstly considered in further management.
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Affiliation(s)
- Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Yi-Wen Tan
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
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Mohamed YG, Mohamud MFY, Medişoğlu MS, Atamaca IY, Ali IH. Clinical and chest CT presentations from 27 patients with COVID-19 pneumonia in Mogadishu, Somalia: a descriptive study. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [PMCID: PMC7484493 DOI: 10.1186/s43055-020-00302-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is an acute viral pneumonia that had recently been found in humans. The first case was discovered in Wuhan, Hubei province, China, in December 2019. In this article, we aimed to demonstrate the clinical and radiological characteristics of COVID-19 patients in Somalia from 20 March 2020 to 20 April 2020. Results Twenty-seven patients that had a positive RT-PCR test between 20 March 2020 and 20 April 2020 were retrospectively observed. This study included 19 (70.4%) males and 8 (29.6%) females, and the mean age and range were 43 years (SD ± 14.0) and 27–70 years, respectively. The majority (59.3%) of COVID-19-infected patients had no obvious history of exposure to infected patients. The participants of our study mostly presented with dry cough 24 (88.9%) patients, fever 19 (70.4%), myalgia 18 (66.6%), and sore throat 16 (59.3%). Twenty-five of 27 patients had abnormal chest CT, while 2 (7.4%) patients had normal chest CT. The most common patterns of abnormality seen on chest CT in patients with COVID-19 were ground-glass opacity (GGO) 74.1%, crazy paving pattern 18.5%, consolidation 14.8%, and mixed GCO 11.1%. Also, the most common predominant lesion distributions were bilateral lung involvement (88.9%), peripheral distribution (77.8%), and lower lung predominance (63%). Particularly, lung cavitation, discrete pulmonary nodules, pleural effusion, and underlying pulmonary fibrosis or emphysema had not been observed. Conclusion Dry cough, fever, myalgia, and sore throat were the most clinical presentations. GGO, crazy paving pattern, patchy consolidation, and mixed GCO were the typical chest CT manifestations.
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Meng F, Guo Y, Li M, Lu X, Wang S, Zhang L, Zhang H. Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules. Transl Oncol 2020; 14:100936. [PMID: 33221688 PMCID: PMC7689413 DOI: 10.1016/j.tranon.2020.100936] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 12/17/2022] Open
Abstract
It is vital to distinguish indolent pulmonary adenocarcinomas from invasive pulmonary adenocarcinomas before surgery. Radiomics is a cutting-edge technology that mines quantitative features from CT images. We designed a nomogram, which incorporated clinical and CT morphological characteristics with the radiomics signature. We applied the radiomics nomogram to preoperatively predict the invasiveness of GGNs.
In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916–0.964) and validation set (AUC, 0.946; 95% CI, 0.907–0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Mingyang Li
- State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China
| | - Xiaoqian Lu
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Shuo Wang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
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Xia T, Cai M, Zhuang Y, Ji X, Huang D, Lin L, Liu J, Yang Y, Fu G. Risk Factors for The Growth of Residual Nodule in Surgical Patients with Adenocarcinoma Presenting as Multifocal Ground-glass Nodules. Eur J Radiol 2020; 133:109332. [PMID: 33152625 DOI: 10.1016/j.ejrad.2020.109332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 09/18/2020] [Accepted: 09/30/2020] [Indexed: 11/19/2022]
Abstract
PURPOSE We aim to investigate the risk factors influencing the growth of residual nodule (RN) in surgical patients with adenocarcinoma presenting as multifocal ground-glass nodules (GGNs). METHOD From January 2014 to June 2018, we enrolled 238 patients with multiple GGNs in a retrospective review. Patients were categorized into growth group 63 (26.5%), and non-growth group 175 (73.5%). The median follow-up time was 28.2 months (range, 6.3-73.0 months). To obtain the time of RN growth and find the risk factors for growth, data such as age, gender, history of smoking, history of malignancy, type of surgery, pathology and radiological characteristics were analyzed to use Kaplan-Meier method with the log-rank test and Cox regression analysis. RESULTS The median growth time of RN was 56.0 months (95% CI, 45.0-67.0 months) in all 238 patients. Roundness (HR 4.62, 95% CI 2.20-9.68), part-solid nodule (CTR ≥ 50%) (HR 4.39, 95% CI 2.29-8.45), vascular convergence sign (HR 2.32, 95% CI 1.36-3.96) of RN, and age (HR 1.04, 95% CI 1.01-1.07) were independent predictors of further nodule growth. However, radiological characteristics and pathology of domain tumour (DT) cannot be used as indicators to predict RN growth. CONCLUSIONS RN showed an indolent growth pattern in surgical patients with multifocal GGNs. RN with a higher roundness, presence of vascular convergence sign, more solid component, and in the elder was likely to grow. However, the growth of RN showed no association with the radiological features and pathology of DT.
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Affiliation(s)
- Tianyi Xia
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Mengting Cai
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Yuandi Zhuang
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Xiaowei Ji
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Dingpin Huang
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Liaoyi Lin
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Jinjin Liu
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Yunjun Yang
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China.
| | - Gangze Fu
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China.
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Deng L, Tang H, Qiang J, Wang J, Xiao S. Blood Supply of Early Lung Adenocarcinomas in Mice and the Tumor-supplying Vessel Relationship: A Micro-CT Angiography Study. Cancer Prev Res (Phila) 2020; 13:989-996. [PMID: 32816806 DOI: 10.1158/1940-6207.capr-20-0036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/05/2020] [Accepted: 08/04/2020] [Indexed: 12/24/2022]
Abstract
This study aimed to investigate the blood supply of early lung adenocarcinomas in mice and the relationship between tumors and their supplying vessels by using micro-CT. An early lung adenocarcinoma model was established in 10 female mice with subcutaneous injections of a 1-methyl-3-nitro-1-nitrosoguanidine solution. Micro-CT pulmonary and bronchial arteriography were performed to demonstrate the blood supply of early lung adenocarcinomas, especially the tumor-vessel relationships, and the findings were correlated with the pathology results. The quantitative and texture changes in the tumor-supplying vessels were analyzed. Micro-CT showed that the pulmonary artery was densely distributed in and around tumors in 141 (84%) of 167 early lung adenocarcinomas, the bronchial artery was not related to tumors, and there were four patterns of tumor-pulmonary artery relationships that correlated well with pathologic findings. Quantitative and texture analyses showed that the tumor size had positive correlations with vessel volume (VV), VV fraction (VVF), vessel thickness (VT), vessel number (VN), inverse difference moment, long run emphasis, gray level nonuniformity (GLN), and run length nonuniformity (RLN) and negative correlations with vessel separation (VS), inertia, and short run emphasis (SRE); the size of the solid component had positive correlations with VV, VVF, VT, VN, GLN, and RLN and negative correlations with VS, cluster shade, and SRE. This study concluded that early lung adenocarcinomas are mainly supplied by the pulmonary arteries in mice, and micro-CT angiography can clearly demonstrate the morphologic changes of pulmonary arteries and their relationships with tumors.
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Affiliation(s)
- Lin Deng
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China
| | - Hanzhou Tang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jie Wang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China
| | - Shiman Xiao
- Department of Radiology, Suzhou Municipal Hospital (Eastern), Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou, China
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Lv H, Chen T, Pan Y, Wang H, Chen L, Lu Y. Pulmonary vascular enlargement on thoracic CT for diagnosis and differential diagnosis of COVID-19: a systematic review and meta-analysis. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:878. [PMID: 32793722 DOI: 10.21037/atm-20-4955] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background The 2019 coronavirus disease (COVID-19) has become a global pandemic. To date, although many studies have reported on the computed tomography (CT) manifestations of COVID-19, the vascular enlargement sign (VES) of COVID-19 has not been deeply examined, with the few available studies reporting an inconsistent prevalence. We thus performed a systematic review and meta-analysis based on the best available studies to estimate the prevalence and identify the underlying differential diagnostic value of VES. Methods We searched nine English and Chinese language databases up to April 23, 2020. Studies that evaluated CT features of COVID-19 patients and reported VES, with or without comparison with other pneumonia were included. The methodologic quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Meta-analyses with random effects models were performed to calculate the aggregate prevalence and pooled odds ratios (ORs) of VES. We also conducted meta-regression and subgroup analyses to analyze heterogeneity. Results VES findings from a total of 1969 patients were summarized and pooled across 22 studies. Our analysis demonstrated that the prevalence of VES among COVID-19 patients was 69.37% [95% confidence interval (CI): 57.40-79.20%]. Compared with non-COVID-19 patients, VES manifestation was more frequently observed in confirmed COVID-19 patients (OR =6.43, 95% CI: 3.39-12.22). Studies that explicitly defined distribution of VES in the lesion area demonstrated a significantly higher prevalence (P=0.03). Subgroup analyses also revealed a relatively higher VES rate in studies with a sample size larger than 50, but the difference was not statistically significant. No significant difference in VES rates was found between different countries (China/Italy), regions (Hubei/outside Hubei), average age groups (over/less than 50-year-old), or slice thicknesses of CT scan. Extensive heterogeneity was identified across most estimates (I2>80%). Some of the variations (R2=19.73%) could be explained by VES distribution, and sample size. No significant publication bias was seen (P=0.29). Conclusions VES on thoracic CT was found in almost two-thirds of COVID-19 patients, and was more prevalent compared with that of the non-COVID-19 patients, supporting a promising role for VES in identifying pneumonia caused by coronavirus.
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Affiliation(s)
- Haiying Lv
- Department of Radiology, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Tongtong Chen
- Department of Radiology, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yaling Pan
- Department of Radiology, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Hanqi Wang
- Department of Radiology, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Liuping Chen
- Department of Radiology, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, China
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Qiu T, Ru X, Yin K, Yu J, Song Y, Wu J. Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis. Jpn J Radiol 2020; 38:761-770. [PMID: 32356236 DOI: 10.1007/s11604-020-00957-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 03/16/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE The aim of the study is to construct two nomograms for predicting the invasive extent of pulmonary adenocarcinoma and nodule growth in patients with pulmonary pure ground-glass nodules (pGGN). METHOD Consecutive patients with pGGNs (n = 172) were retrospectively studied at one institution, formed the development cohort in predicting IPAs' nomogram. A separate cohort of patients with pGGNs (n = 116) from another institution was used for validation. For the predicting growth nomogram, the primary cohort of patients with pGGNs (n = 80) was from the former institution. We developed the nomogram for predicting IPA using binary logistic regression model, and a Cox multivariable model for the growth nomogram. We assessed nomogram model performance by calibration and discrimination (C-index). RESULTS The variables selected in binary logistic regression model (lesion size and shape) had a significant effect on identifying IPA from preinvasive lesion. The C-index of the development and validation cohort were 0.819 (95% CI 0.753-0.874) and 0.811 (95% CI 0.728-0.878), respectively. The risk variables (lesion size, blood vessel types) were selected in the multivariable Cox model. The C-index was 0.880 in the development cohort. CONCLUSION Our nomograms are reliable prognostic methods that can predict the invasiveness of pulmonary adenocarcinomas and the growth of pure GGN in preoperative.
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Affiliation(s)
- Taichun Qiu
- Rodiology Department, People's Hospital of Deyang City, No. 173, Taishan North Rd, Deyang, Sichuan, China.,Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China
| | - Xiaoshuang Ru
- Radiology Department, Dalian Municipal Central Hospital Affiliated of Dalian Medical University, Dalian Medical University, No. 42, Xuegong Rd, Shahekou District, Dalian, China
| | - Ke Yin
- Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China
| | - Jing Yu
- Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China
| | - Yang Song
- Radiology Department, Dalian Municipal Central Hospital Affiliated of Dalian Medical University, Dalian Medical University, No. 42, Xuegong Rd, Shahekou District, Dalian, China
| | - Jianlin Wu
- Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China.
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CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules. AJR Am J Roentgenol 2020; 215:351-358. [PMID: 32348187 DOI: 10.2214/ajr.19.22381] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm2; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm2), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.
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Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study. AJR Am J Roentgenol 2020; 214:1072-1077. [PMID: 32125873 DOI: 10.2214/ajr.20.22976] [Citation(s) in RCA: 681] [Impact Index Per Article: 170.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE. The increasing number of cases of confirmed coronavirus disease (COVID-19) in China is striking. The purpose of this study was to investigate the relation between chest CT findings and the clinical conditions of COVID-19 pneumonia. MATERIALS AND METHODS. Data on 101 cases of COVID-19 pneumonia were retrospectively collected from four institutions in Hunan, China. Basic clinical characteristics and detailed imaging features were evaluated and compared between two groups on the basis of clinical status: nonemergency (mild or common disease) and emergency (severe or fatal disease). RESULTS. Patients 21-50 years old accounted for most (70.2%) of the cohort, and five (5.0%) patients had disease associated with a family outbreak. Most patients (78.2%) had fever as the onset symptom. Most patients with COVID-19 pneumonia had typical imaging features, such as ground-glass opacities (GGO) (87 [86.1%]) or mixed GGO and consolidation (65 [64.4%]), vascular enlargement in the lesion (72 [71.3%]), and traction bronchiectasis (53 [52.5%]). Lesions present on CT images were more likely to have a peripheral distribution (88 [87.1%]) and bilateral involvement (83 [82.2%]) and be lower lung predominant (55 [54.5%]) and multifocal (55 [54.5%]). Patients in the emergency group were older than those in the non-emergency group. Architectural distortion, traction bronchiectasis, and CT involvement score aided in evaluation of the severity and extent of the disease. CONCLUSION. Patients with confirmed COVID-19 pneumonia have typical imaging features that can be helpful in early screening of highly suspected cases and in evaluation of the severity and extent of disease. Most patients with COVID-19 pneumonia have GGO or mixed GGO and consolidation and vascular enlargement in the lesion. Lesions are more likely to have peripheral distribution and bilateral involvement and be lower lung predominant and multifocal. CT involvement score can help in evaluation of the severity and extent of the disease.
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Meng Q, Ren P, Gao P, Dou X, Chen X, Guo L, Song Y. Effectiveness and Feasibility of Complementary Lung-RADS Version 1.1 in Risk Stratification for pGGN in LDCT Lung Cancer Screening in a Chinese Population. Cancer Manag Res 2020; 12:189-198. [PMID: 32021435 PMCID: PMC6957006 DOI: 10.2147/cmar.s232269] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 12/30/2019] [Indexed: 12/18/2022] Open
Abstract
Purpose To evaluate the effectiveness of using a modified lung imaging reporting and data system (Lung-RADS) for risk stratification of pure ground-glass nodules (pGGNs) in low-dose computed tomography (LDCT) for lung cancer (LC) screenings in China. Patients and Methods Eight subjects with nine pGGNs originating from a Cancer Screening Program were enrolled as training set and 32 asymptomatic subjects with 35 pGGNs were selected as validation set from November 2013 to October 2018. The complementary Lung-RADS categories were set based on the GGN-vessel relationship (GVR). The correlations between GGN-vessel relationships and pathology were evaluated, and the diagnostic value of complementary Lung-RADS version 1.1 in discriminating malignant pGGNs were analyzed. Results The inter-reader agreements for Lung-RADS 1.1 (intraclass correlation coefficient (ICC= 0.999) and complementary Lung-RADS 1.1 (ICC= 0.971) displayed good reliability. The combined incidence of invasive adenocarcinoma in type III and IV was more than that of benign and preinvasive diseases (30% vs 75%, P=0.013). Type II GVR between two benign (66.7%), seven preinvasive (53.8%), and six invasive (21.4%) GGN cases was statistically significant (χ2=5.415, P=0.019). GGN pathological groups and GVR had a significant correlation (r=0.584, P=0.00). Compared to Lung-RADS 1.1, complementary Lung-RADS 1.1 had better performance in the training set, with its sensitivity increased from 33.3% to 88.9%, accuracy increased from 44.4% to 88.9%, false-negative proportion (FNP) decreased from 66.7% to 11.1%, and the sensitivity to predict malignant nodules increased from 13.8% to 93.1%, accuracy increased from 28.6% to 80.0%, and FNP decreased from 86.2% to 6.9% in validation set. The detection rate of preinvasive disease and adenocarcinoma was increased from 12.5% to 90.6% and that of missed diagnosis decreased from 87.5% to 9.4% in the validation set, P=0.004. Conclusion Complementary Lung-RADS 1.1 is superior to Lung-RADS 1.1 and would be beneficial for LC screening of LDCT in China.
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Affiliation(s)
- Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Pengfei Ren
- Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Pengrui Gao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xinmin Dou
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xuejun Chen
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Lanwei Guo
- Department of Cancer Prevention Office, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yongping Song
- Department of Hematology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
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Attenuation and Morphologic Characteristics Distinguishing a Ground-Glass Nodule Measuring 5-10 mm in Diameter as Invasive Lung Adenocarcinoma on Thin-Slice CT. AJR Am J Roentgenol 2019; 213:W162-W170. [PMID: 31216199 DOI: 10.2214/ajr.18.21008] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE. The purpose of this study is to comprehensively investigate the role of multiple features seen on thin-section CT (TSCT) in the differential diagnosis of ground-glass nodules (GGNs) measuring 5-10 mm in diameter as invasive adenocarcinoma (IAC). MATERIALS AND METHODS. The TSCT features of 313 surgically diagnosed GGNs from 288 patients were retrospectively reviewed. A logistic regression model was applied, and the AUC values for the model and the size and attenuation of the lesions were compared using ROC curve analysis. RESULTS. A total of 247 lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) (hereafter referred to as the AIS-MIA group) and 66 invasive adenocarcinomas (IACs) were identified. Compared with the AIS-MIA group, the IAC groups were significantly larger in size and had higher attenuation values, a higher frequency of mixed GGNs (all p < 0.001), bubblelike appearance, spiculation, pleural indentation, different locations, and a lower frequency of clear tumor-lung interface (all p < 0.05). The logistic model included size and attenuation (both p < 0.001; odds ratio [OR], 1.872 and 1.009, respectively) as well as tumor-lung interface (p = 0.001; OR, 0.242), bubblelike appearance (p < 0.05; OR, 2.205), and type of nodule. The AUC value for the logistic model was 0.847 (sensitivity, 80.3%; specificity, 81.0%) and was significantly higher than that for size or attenuation (both p < 0.01). CONCLUSION. Radiologic features could help in the differential diagnosis of a GGN that was 5-10 mm in diameter as IAC versus AIS or MIA. GGNs larger than 8.12 mm and with attenuation greater than -449.52 HU were more likely to be IAC.
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Invasive Pulmonary Adenocarcinomas Versus Preinvasive Lesions Appearing as Pure Ground-Glass Nodules: Differentiation Using Enhanced Dual-Source Dual-Energy CT. AJR Am J Roentgenol 2019; 213:W114-W122. [PMID: 31082273 DOI: 10.2214/ajr.19.21245] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE. The objective of our study was to investigate the potentials of enhanced dual-source dual-energy CT (DECT) and three-planar measurements for differentiating invasive pulmonary adenocarcinomas (IPAs) from preinvasive lesions appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS. Thirty-nine patients with 53 pGGNs who underwent enhanced dual-source DECT were included in this retrospective study. All pGGNs were pathologically confirmed and categorized into two groups: preinvasive lesions or IPAs. The traditional CT features of the pGGNs were evaluated on unenhanced images. Quantitative parameters were measured on iodine-enhanced images of dual-source DECT in three planes, and both intra- and interobserver reproducibility analyses were performed to assess the measurement reproducibility of quantitative parameters. To identify significant factors for differentiating IPAs from preinvasive lesions, we performed logistic regression analysis and ROC curve analysis. RESULTS. For traditional CT features, only lesion size and unenhanced CT attenuation value showed significant differences between preinvasive lesions and IPAs (p < 0.05). Preinvasive lesions and IPAs exhibited significant differences in attenuation on virtual images, so-called "virtual HU" or "VHU," and the modified normalized iodine concentration (NIC) (p < 0.05), and both intra- and interobserver agreement for the quantitative measurements were excellent. Multivariate logistic regression analysis revealed that larger lesion size (adjusted odds ratio [OR], 3.65) and higher modified NIC (adjusted OR, 19.01) were significant differentiators of IPAs from preinvasive lesions (p < 0.05). ROC curve analysis revealed that modified NIC showed excellent performance (AUC, 0.924) and significantly higher performance than lesion size (AUC, 0.711) for differentiating IPAs from preinvasive lesions. CONCLUSION. In pGGNs, a lesion with a modified NIC value of more than 0.29 can be a very specific discriminator of IPAs from preinvasive lesions, and IPAs can be accurately and reliably differentiated from preinvasive lesions using enhanced dual-source DECT and three-planar measurements.
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Zhang C, Li J, Sun M, Li S, Li J, Li Q, Zhu Z. Peripheral vessel and air bronchograms for detecting the pathologic patterns of subsolid nodules. Clin Imaging 2019; 56:63-68. [PMID: 30933847 DOI: 10.1016/j.clinimag.2019.03.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 03/14/2019] [Accepted: 03/20/2019] [Indexed: 12/17/2022]
Abstract
PURPOSE To assess the relationships of subsolid nodules (SSNs) with peripheral vessels and aerated bronchi using computed tomography (CT), and to correlate the imaging features with the benign/malignant pathological diagnoses. METHODS This study retrospectively analyzed data from 83 patients with a solitary SSN (January 2008 to December 2016). SSNs were imaged (LightSpeed 64-slice spiral CT, General Electric, USA), their mean diameter determined, and the relationship with peripheral vessels (types I-IV) and aerated bronchi (types I-V) were classified. Pathologic diagnoses were obtained from the surgical specimens. RESULTS SSNs were diagnosed as benign (n = 29), pre-invasive (n = 9), micro-invasive adenocarcinoma (n = 7) and invasive adenocarcinoma (n = 38). SSN size, peripheral vessel class and aerated bronchus class differed between pathologic types (P < 0.05). For benign SSNs, peripheral vessel type II (58.6%) was most common, followed by III (20.7%) and IV (6.9%). Aerated bronchus type V (65.5%) was most frequent, followed by IV (27.6%); type I aerated bronchus was not observed. No cases of micro-invasive or invasive adenocarcinoma were peripheral vessel type I or aerated bronchus type V. For invasive adenocarcinoma, 92.1% were peripheral vessel types III + IV while 71.8% were aerated bronchus types I + II. CONCLUSIONS SSN pathologic types differ with regard to peripheral vessel and aerated bronchus types. Type I peripheral vessel and type V aerated bronchus (both least involved) suggest a benign lesion, whereas type III/IV peripheral vessel and type I/II aerated bronchus (both most involved) suggest malignancy.
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Affiliation(s)
- Chenguang Zhang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China
| | - Jianke Li
- Department of Thoracic Surgery, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China
| | - Mengyue Sun
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China
| | - Shujing Li
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China.
| | - Jingyu Li
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China
| | - Quanhai Li
- Department of Cell Therapy Laboratory, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China
| | - Zhenlong Zhu
- Department of Pathology, the First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China
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Jin L, Sun Y, Li M. Use of an Anthropomorphic Chest Model to Evaluate Multiple Scanning Protocols for High-Definition and Standard-Definition Computed Tomography to Detect Small Pulmonary Nodules. Med Sci Monit 2019; 25:2195-2205. [PMID: 30907379 PMCID: PMC6442497 DOI: 10.12659/msm.913243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND This study aimed to use the LUNGMAN N1 anthropomorphic chest model to evaluate protocols for high-definition computed tomography (HDCT) and standard-definition CT (SDCT) to detect and compare small pulmonary nodules and determine the most appropriate low-dose scanning protocols. MATERIAL AND METHODS HDCT imaging used the Discovery HD750 scanner (80, 100, 120 and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA), and SDCT imaging used the Lightspeed VCT scanner (80, 120, and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA). The LUNGMAN N1 anthropomorphic chest model contained artificial pulmonary nodules (diameter: 5, 8, 10, and 12 mm). Low-dose scanning protocols were used in image acquisition. Two experienced radiologists evaluated the image quality. The combinations of voltage, tube current, image noise, and radiation dose were recorded. Consistency of the image quality between raters was assessed by kappa statistical analysis. RESULTS Seventy CT scans of pulmonary nodules (diameter, 5-12 mm) were performed. There was a high degree of consistency for image quality between the two observers (K=0.929 for 5 mm nodules; K=0.819 for overall image quality). For 8 mm nodules, 100% were detected on both SDCT and HDCT. HDCT outperformed SDCT by 5%, in terms of effective dose. There was no significant difference in image quality between the SDCT and HDCT scanners. CONCLUSIONS Using an anthropomorphic chest model, the identification and image quality using SDCT was similar to that of HDCT for small pulmonary nodules between 5-12 mm.
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Affiliation(s)
- Liang Jin
- Department of Radiology, Huadong Hospital, Affiliated to Fudan University, Shanghai, China (mainland)
| | - Yingli Sun
- Department of Radiology, Huadong Hospital, Affiliated to Fudan University, Shanghai, China (mainland)
| | - Ming Li
- Department of Radiology, Huadong Hospital, Affiliated to Fudan University, Shanghai, China (mainland)
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