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Azour L, Oh AS, Prosper AE, Toussie D, Villasana-Gomez G, Pourzand L. Subsolid Nodules: Significance and Current Understanding. Clin Chest Med 2024; 45:263-277. [PMID: 38816087 DOI: 10.1016/j.ccm.2024.02.003] [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] [Indexed: 06/01/2024]
Abstract
Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.
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Affiliation(s)
- Lea Azour
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA.
| | - Andrea S Oh
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Ashley E Prosper
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Danielle Toussie
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Geraldine Villasana-Gomez
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Lila Pourzand
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
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Qiu J, Li R, Wang Y, Ma X, Qu C, Liu B, Yue W, Tian H. A nomogram combining thoracic CT and tumor markers to predict the malignant grade of pulmonary nodules ≤3 cm in diameter. Front Oncol 2023; 13:1196883. [PMID: 37361581 PMCID: PMC10285407 DOI: 10.3389/fonc.2023.1196883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Background With the popularity of computed tomography (CT) of the thorax, the rate of diagnosis for patients with early-stage lung cancer has increased. However, distinguishing high-risk pulmonary nodules (HRPNs) from low-risk pulmonary nodules (LRPNs) before surgery remains challenging. Methods A retrospective analysis was performed on 1064 patients with pulmonary nodules (PNs) admitted to the Qilu Hospital of Shandong University from April to December 2021. Randomization of all eligible patients to either the training or validation cohort was performed in a 3:1 ratio. Eighty-three PNs patients who visited Qianfoshan Hospital in the Shandong Province from January through April of 2022 were included as an external validation. Univariable and multivariable logistic regression (forward stepwise regression) were used to identify independent risk factors, and a predictive model and dynamic web nomogram were constructed by integrating these risk factors. Results A total of 895 patients were included, with an incidence of HRPNs of 47.3% (423/895). Logistic regression analysis identified four independent risk factors: the size, consolidation tumor ratio, CT value of PNs, and carcinoembryonic antigen levels in blood. The area under the ROC curves was 0.895, 0.936, and 0.812 for the training, internal validation, and external validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated excellent calibration capability, and the fit of the calibration curve was good. DCA has shown the nomogram to be clinically useful. Conclusion The nomogram performed well in predicting the likelihood of HRPNs. In addition, it identified HRPNs in patients with PNs, achieved accurate treatment with HRPNs, and is expected to promote their rapid recovery.
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Affiliation(s)
- Jianhao Qiu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Rongyang Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yukai Wang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiuyuan Ma
- Department of Cardiology, Qianfoshan Hospital in the Shandong Province, Jinan, Shandong, China
| | - Chenghao Qu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Binyan Liu
- Department of Breast Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Weiming Yue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
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Cui X, Zheng S, Zhang W, Fan S, Wang J, Song F, Liu X, Zhu W, Ye Z. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT. Eur Radiol 2023:10.1007/s00330-023-09432-3. [PMID: 36723725 DOI: 10.1007/s00330-023-09432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/31/2022] [Accepted: 01/02/2023] [Indexed: 02/02/2023]
Abstract
OBJECTIVES This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT. METHODS A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately. RESULTS Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts. CONCLUSIONS We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment. KEY POINTS • Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Sunyi Zheng
- Artificial Intelligence and Biomedical Image Analysis Lab, School of Engineering, Westlake University, Hangzhou, People's Republic of China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, People's Republic of China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Jing Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, People's Republic of China
| | - Feipeng Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Xu Liu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Weijie Zhu
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China.
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Zhu Y, Yang L, Li Q, Chen B, Hao Q, Sun X, Tan J, Li W. Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations. J Evid Based Med 2022; 15:106-122. [PMID: 35794787 DOI: 10.1111/jebm.12481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting. METHODS The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs. RESULTS A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs. CONCLUSION Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.
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Affiliation(s)
- Yuqi Zhu
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qianrui Li
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qiukui Hao
- The Center of Gerontology and Geriatrics, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Tan
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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Jiang Y, Xiong Z, Zhao W, Zhang J, Guo Y, Li G, Li Z. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs. Gan To Kagaku Ryoho 2022; 70:880-890. [PMID: 35301662 DOI: 10.1007/s11748-022-01801-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/03/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs. PATIENTS AND METHODS We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC). RESULTS The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness. CONCLUSION The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.
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Affiliation(s)
- Yining Jiang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Guosheng Li
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China. .,Dalian Engineering Research Centre for Artificial Intelligence in Medical Imaging, Dalian, China.
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Song X, Zhao Q, Zhang H, Xue W, Xin Z, Xie J, Zhang X. Development and Validation of a Preoperative CT-Based Nomogram to Differentiate Invasive from Non-Invasive Pulmonary Adenocarcinoma in Solitary Pulmonary Nodules. Cancer Manag Res 2022; 14:1195-1208. [PMID: 35342306 PMCID: PMC8948523 DOI: 10.2147/cmar.s357385] [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: 01/20/2022] [Accepted: 03/08/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Patients and Methods Results Conclusion
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Affiliation(s)
- Xin Song
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
- North China University of Science and Technology, Tangshan, People’s Republic of China
| | - Qingtao Zhao
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Hua Zhang
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Wenfei Xue
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Zhifei Xin
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Jianhua Xie
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
- North China University of Science and Technology, Tangshan, People’s Republic of China
| | - Xiaopeng Zhang
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
- Correspondence: Xiaopeng Zhang, Hebei General Hospital, No. 348, Heping Western Road, Xinhua District, Shijiazhuang, 050000, People’s Republic of China, Tel +8613722865878, Email
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Song L, Xing T, Zhu Z, Han W, Fan G, Li J, Du H, Song W, Jin Z, Zhang G. Hybrid Clinical-Radiomics Model for Precisely Predicting the Invasiveness of Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodule. Acad Radiol 2021; 28:e267-e277. [PMID: 32534967 DOI: 10.1016/j.acra.2020.05.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/27/2020] [Accepted: 05/04/2020] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To identify whether the radiomics features of computed tomography (CT) allowed for the preoperative discrimination of the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs) and further to develop and compare different predictive models. MATERIALS AND METHODS We retrospectively included 187 lung adenocarcinomas presenting as pGGNs (66 preinvasive lesions and 121 invasive lesions), which were randomly divided into the training and test sets (8:2). Radiomics features were extracted from non-enhanced CT images. Clinical features, including patient's demographic characteristics, smoking status, and conventional CT features that reflect tumor's morphology and surrounding information were also collected. Intraclass correlation coefficient and ℓ2.1-norm minimization were used to identify influential feature subset which was then used to build three predictive models (clinical, radiomics, and clinical-radiomics models) with the gradient boosting regression tree classifier. The performances of the predictive models were evaluated using the area under the curve (AUC). RESULTS Of the 1409 radiomics features and 27 clinical feature subtypes, 102 features were selected to construct the hybrid clinical-radiomics model, which achieved the best discriminative power (AUC = 0.934 and 0.929 in training and test set). The radiomics model showed comparable predictive performance (AUC = 0.911 and 0.901 in training and test set) compared to the clinical model (AUC = 0.911 and 0.894 in training and test set). CONCLUSION The radiomics model showed good predictive performance in discriminating invasive lesions from preinvasive lesions for lung adenocarcinomas presenting as pGGNs. Its performance can be further improved by adding clinical features.
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Affiliation(s)
- Lan Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Tongtong Xing
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhenchen Zhu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China; 4+4 MD Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Han
- Department of Epidemiology and Health Statistics, Institute of Basic Medicine Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China
| | - Guangda Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Ji Li
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Huayang Du
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Guanglei Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, 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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A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules. Clin Radiol 2020; 76:143-151. [PMID: 33187676 DOI: 10.1016/j.crad.2020.10.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/08/2020] [Indexed: 12/17/2022]
Abstract
AIM To establish a machine-learning model to differentiate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS This retrospective study enrolled 136 patients with histopathologically diagnosed with AIS, MIA, and IAC. All pGGNs were divided randomly into a training and a testing dataset at a ratio of 7 : 3. Radiomics features were extracted based on the unenhanced computed tomography (CT) images derived from the last preoperative CT examination of each patient. The F-test and least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select the most valuable features to establish a support vector machine (SVM) model. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUROC), and the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance of radiologists and the SVM model. RESULTS Six significant radiomics features were selected to develop the SVM model and showed excellent ability to differentiate AIS/MIA from IAC in both the training dataset (AUROC=0.950, 95% confidence interval [CI]: 0.886-0.984) and the testing dataset (AUROC=0.945, 95% CI: 0.826-0.992). Compared with two radiologists, the proposed model possessed significant advantages with higher accuracy (90.24% versus 75.61% and 80.49%), sensitivity (91.67% versus 50% and 75%), and specificity (89.66% versus 86.21% and 82.76%). CONCLUSION A machine-learning model based on radiomics features exhibits superior diagnostic performance in differentiating AIS/MIA from IAC appearing as pGGNs.
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de Margerie-Mellon C, Gill RR, Salazar P, Oikonomou A, Nguyen ET, Heidinger BH, Medina MA, VanderLaan PA, Bankier AA. Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models. Sci Rep 2020; 10:14585. [PMID: 32883973 PMCID: PMC7471897 DOI: 10.1038/s41598-020-70316-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 07/13/2020] [Indexed: 01/22/2023] Open
Abstract
The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.
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Affiliation(s)
| | - Ritu R Gill
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Elsie T Nguyen
- Department of Medical Imaging, Toronto General Hospital, University of Toronto, Toronto, Canada
| | - Benedikt H Heidinger
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Mayra A Medina
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Paul A VanderLaan
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Alexander A Bankier
- Department of Radiology, UMass Memorial Medical Center, University of Massachusetts Medical School, Worcester, MA, USA
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Li X, Ren F, Wang S, He Z, Song Z, Chen J, Xu S. The Epidemiology of Ground Glass Opacity Lung Adenocarcinoma: A Network-Based Cumulative Meta-Analysis. Front Oncol 2020; 10:1059. [PMID: 32793469 PMCID: PMC7386063 DOI: 10.3389/fonc.2020.01059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/27/2020] [Indexed: 12/12/2022] Open
Abstract
Introduction: Due to the introduction of low-dose computed tomography (CT) and screening procedures, the proportion of early-stage lung cancer with ground glass opacity (GGO) manifestation is increasing in clinical practice. However, its epidemiological characteristics is still not fully investigated. Methods: We retrieved all solitary GGO adenocarcinoma lung cancer (ADLC) on the PubMed, Cochrane Library, and Embase databases until January 1, 2019 and extracted the general information to perform the meta-analysis, mainly focusing on age, gender, and smoking status. Results: A total of 8,793 solitary GGO ADLC patients from 53 studies were included in this analysis. The final pooled analysis showed that the female proportion, average diagnosis age, and non-smoking proportion of solitary GGO ADLC was 0.62 (95% CI, 0.60–0.64), 56.97 (95% CI, 54.56–59.37), and 0.72 (95% CI, 0.66–0.77), respectively. The cumulative meta-analysis and meta-trend analysis confirmed that the average age at diagnosis has been decreasing while the non-smoking proportion significantly increased in the past two decades. Conclusions: From our epidemiological analysis, it demonstrates that the clinical characteristics of GGO lung cancer patients may be out of the high-risk factors. Therefore, we propose to reconsider the risk assessment and current lung cancer screening criteria.
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Affiliation(s)
- Xiongfei Li
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Fan Ren
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Shuhang Wang
- Department of Clinical Trials Center, National Cancer Center, Cancer Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Zhicheng He
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuoqing Song
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Chen
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Song Xu
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
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12
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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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