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Meng Q, Zheng C, Guo L, Gao P, Liu W, Ge H, Liu T, Peng H, Lu J, Chen X. Construction and validation of a risk score system for diagnosing invasive adenocarcinoma presenting as pulmonary pure ground-glass nodules: a multi-center cohort study in China. Quant Imaging Med Surg 2024; 14:4864-4877. [PMID: 39022278 PMCID: PMC11250337 DOI: 10.21037/qims-24-170] [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: 01/27/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024]
Abstract
Background Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. Methods In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. Results The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). Conclusions An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs.
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Affiliation(s)
- Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Changbao Zheng
- Department of Radiology, The Hainan Cancer Hospital, Haikou, China
| | - Lanwei Guo
- Henan Office for Cancer Control and Research, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Pengrui Gao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Wentao Liu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Tong Liu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Hui Peng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jie Lu
- Department of Radiology, The People’s Hospital of Xingyang Country, Zhengzhou, China
| | - Xuejun Chen
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
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Chang Y, Xing H, Shang Y, Liu Y, Yu L, Dai H. Radiomics nomogram: distinguishing benign and malignant pure ground-glass nodules based on dual-layer spectral detector CT. Clin Radiol 2024:S0009-9260(24)00299-X. [PMID: 39013667 DOI: 10.1016/j.crad.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 06/06/2024] [Accepted: 06/11/2024] [Indexed: 07/18/2024]
Abstract
AIM To investigate the value of the combined model based on spectral quantitative parameters, radiomics features, imaging and clinical features to distinguish the benign and malignant pure ground-glass nodules (pGGNs). MATERIALS AND METHODS A retrospective analysis of 113 patients with single pGGNs who underwent non-contrast enhancement examination of the chest on dual-layer spectral detector CT (SDCT) with two weeks before surgery was performed in our hospital. These patients were randomized into training and testing cohorts. Regions of interest based on the conventional 120 kVp poly energetic image of SDCT were outlined. Then the optimal features were extracted and selected to construct radiomic model. A combined model combining vacuole sign, electron density (ED) value and the rad score of radiomics model was built by logistic regression analysis. A nomogram was built in a training cohort and the performance of the models was evaluated in the training and testing cohorts by receiver operating characteristic curves, calibration curves and decision curve analysis. RESULTS ED value [Odds Ratio (OR):1.100; 95% confidence interval (CI):1.027-1.166)] and vacuole sign (OR:3.343; 95% CI:0.881-12.680) were independent risk factors for the malignant pGGNs in the training cohort. A combined model was constructed using radiomics features, ED value and vacuole sign. And the AUC was 0.910 (95% CI, 0.825-0.997) and 0.850 (95% CI, 0.714-0.981) in the training and testing cohorts, respectively. CONCLUSION The combined model based on SDCT has high specificity and sensitivity for distinguishing the benign and malignant pGGNs, suggesting the model can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.
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Affiliation(s)
- Y Chang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, PR China
| | - H Xing
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, PR China
| | - Y Shang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, PR China
| | - Y Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, PR China
| | - L Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, PR China
| | - H Dai
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, PR China; Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu Province, 215006, PR China; Suzhou Key Laboratory of Intelligent Medicine and Equipment, Suzhou, Jiangsu Province, 215123, PR China.
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Xue M, Li R, Wang K, Liu W, Liu J, Li Z, Chen G, Zhang H, Tian H. Construction and validation of a predictive model of invasive adenocarcinoma in pure ground-glass nodules less than 2 cm in diameter. BMC Surg 2024; 24:56. [PMID: 38355554 PMCID: PMC10868041 DOI: 10.1186/s12893-024-02341-2] [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: 11/07/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
OBJECTIVES In this study, we aimed to develop a multiparameter prediction model to improve the diagnostic accuracy of invasive adenocarcinoma in pulmonary pure glass nodules. METHOD We included patients with pulmonary pure glass nodules who underwent lung resection and had a clear pathology between January 2020 and January 2022 at the Qilu Hospital of Shandong University. We collected data on the clinical characteristics of the patients as well as their preoperative biomarker results and computed tomography features. Thereafter, we performed univariate and multivariate logistic regression analyses to identify independent risk factors, which were then used to develop a prediction model and nomogram. We then evaluated the recognition ability of the model via receiver operating characteristic (ROC) curve analysis and assessed its calibration ability using the Hosmer-Lemeshow test and calibration curves. Further, to assess the clinical utility of the nomogram, we performed decision curve analysis. RESULT We included 563 patients, comprising 174 and 389 cases of invasive and non-invasive adenocarcinoma, respectively, and identified seven independent risk factors, namely, maximum tumor diameter, age, serum amyloid level, pleural effusion sign, bronchial sign, tumor location, and lobulation. The area under the ROC curve was 0.839 (95% CI: 0.798-0.879) for the training cohort and 0.782 (95% CI: 0.706-0.858) for the validation cohort, indicating a relatively high predictive accuracy for the nomogram. Calibration curves for the prediction model also showed good calibration for both cohorts, and decision curve analysis showed that the clinical prediction model has clinical utility. CONCLUSION The novel nomogram thus constructed for identifying invasive adenocarcinoma in patients with isolated pulmonary pure glass nodules exhibited excellent discriminatory power, calibration capacity, and clinical utility.
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Affiliation(s)
- Mengchao Xue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Rongyang Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Kun Wang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Wen Liu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Junjie Liu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Zhenyi Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Guanqing Chen
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Huiying Zhang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, 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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Zhang H, Wang D, Li W, Tian Z, Ma L, Guo J, Wang Y, Sun X, Ma X, Ma L, Zhu L. Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules. Quant Imaging Med Surg 2023; 13:5783-5795. [PMID: 37711837 PMCID: PMC10498261 DOI: 10.21037/qims-23-31] [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: 01/06/2023] [Accepted: 07/10/2023] [Indexed: 09/16/2023]
Abstract
Background The use of an artificial intelligence (AI)-based diagnostic system can significantly aid in analyzing the histogram of pulmonary nodules. The aim of our study was to evaluate the value of computed tomography (CT) histogram indicators analyzed by AI in predicting the tumor invasiveness of ground-glass nodules (GGNs) and to determine the added value of contrast-enhanced CT (CECT) compared with nonenhanced CT (NECT) in this prediction. Methods This study enrolled patients with persistent GGNs who underwent preoperative NECT and CECT scanning. AI-based histogram analysis was performed for pathologically confirmed GGNs, which was followed by screening invasiveness-related factors via univariable analysis. Multivariable logistic models were developed based on candidate CT histogram indicators measured on either NECT or CECT. Receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the models' performance. Results A total of 116 patients comprising 121 GGNs were included and divided into the precancerous lesion and adenocarcinoma groups based on invasiveness. In the AI-based histogram analysis, the mean CT value [NECT: odds ratio (OR) =1.009; 95% confidence interval (CI): 1.004-1.013; P<0.001] and solid component volume (NECT: OR =1.005; 95% CI: 1.000-1.010; P=0.032) were associated with the adenocarcinoma and used for multivariable logistic modeling. The area under ROC curve (AUC) and PR curve (AUPR) were not significantly different between the NECT model (AUC =0.765, 95% CI: 0.679-0.837; AUPR =0.907, 95% CI: 0.825-0.953) and the optimal CECT model (delayed phase: AUC =0.772, 95% CI: 0.687-0.843; AUPR =0.895, 95% CI: 0.812-0.944). No significantly different metrics were observed between the NECT and CECT models (precision: 0.707 vs. 0.742; P=0.616). Conclusions The AI diagnostic system can help in the diagnosis of GGNs. The system displayed decent performance in GGN detection and alert to malignancy. Mean CT value and solid component volume were independent predictors of tumor invasiveness. CECT provided no additional improvement in diagnostic performance as compared with NECT.
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Affiliation(s)
- Huairong Zhang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
| | - Wenling Li
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Zhaorong Tian
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Lirong Ma
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Jiaxuan Guo
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Yifan Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiao Sun
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiaobin Ma
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Li Ma
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Li Zhu
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
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Kim BG, Um SW. A narrative review of the clinical approach to subsolid pulmonary nodules. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:217. [PMID: 37007560 PMCID: PMC10061480 DOI: 10.21037/atm-22-5246] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/19/2023] [Indexed: 03/14/2023]
Abstract
Background and Objective The widespread use of chest computed tomography (CT) for lung cancer screening has led to increased detection of subsolid pulmonary nodules. The management of subsolid nodules (SSNs) is challenging since they are likely to grow slowly and a long-term follow-up is needed. In this review, we discuss the characteristics, natural history, genetic features, surveillance, and management of SSNs. Methods PubMed and Google Scholar were searched to identify relevant articles published in English between January 1998 and December 2022 using the following keywords: "subsolid nodule", "ground-glass nodule (GGN)", and "part-solid nodule (PSN)". Key Content and Findings The differential diagnosis of SSNs includes transient inflammatory lesions, focal fibrosis, and premalignant or malignant lesions. Long-term CT surveillance follow-up is needed to manage SSNs that persist for >3 months. Although most SSNs have an indolent clinical course, PSNs may have a more aggressive clinical course than pure GGNs. The proportion of growth and the time to grow is higher and shorter in PSN than pure GGN. In lung adenocarcinoma manifesting as SSNs, EGFR mutations were the major driver mutations. Guidelines are available for the management of incidentally detected and screening-detected SSNs. The size, solidity, location, and number of SSNs are important factors in determining the need for surveillance and surgical resection, as well as the interval of follow-up. Positron emission tomography/CT and brain magnetic resonance imaging (MRI) are not recommended for the diagnosis of SSNs, especially for pure GGNs. Periodic CT surveillance and lung-sparing surgery are the main strategies for the management of persistent SSNs. Nonsurgical treatment options for persistent SSNs include stereotactic body radiotherapy (SBRT) and radiofrequency ablation (RFA). For multifocal SSNs, the timing of repeated CT scans and the need for surgical treatment are decided based on the most dominant SSN(s). Conclusions The SSN is a heterogeneous disease and a personalized medicine approach is required in the future. Future studies of SSNs should focus on their natural history, optimal follow-up duration, genetic features, and surgical and nonsurgical treatments to improve the corresponding clinical management. All these efforts will lead to the personalized medicine approach for the SSNs.
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Affiliation(s)
- Bo-Guen Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
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Feng H, Shi G, Xu Q, Ren J, Wang L, Cai X. Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas. Insights Imaging 2023; 14:24. [PMID: 36735104 PMCID: PMC9898484 DOI: 10.1186/s13244-022-01363-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/28/2022] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVE The purpose of the study is to investigate the performance of radiomics-based analysis in prediction of pure ground-glass nodule (pGGN) lung adenocarcinomas invasiveness using thin-section computed tomography images. METHODS A total of 382 patients surgically resected single pGGN and pathologically confirmed were enrolled in the retrospective study. The pGGN cases were divided into two groups: the noninvasive group and the invasive adenocarcinoma (IAC) group. 330 patients were randomly assigned to the training and testing cohorts with a ratio of 7:3 (245 noninvasive lesions, 85 IAC lesions), while 52 patients (30 noninvasive lesions, 22 IAC lesions) were assigned to the external validation cohort. A model, radiomics model, and combined clinical-radiographic-radiomic model were built using the LASSO and multivariate backward stepwise regression analysis on the basis of the selected and radiomics features. The area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate and compare the model performance for invasiveness discrimination among the three cohorts. RESULTS Three clinical-radiographic features (including age, gender and the mean CT value) and three radiomics features were selected for model building. The combined model and radiomics model performed better than the clinical-radiographic model. The AUCs of the combined model in the training, testing, and validation cohorts were 0.856, 0.859, and 0.765, respectively. The DCA demonstrated the radiomics signatures incorporating clinical-radiographic feature was clinically useful in predicting pGGN invasiveness. CONCLUSIONS The proposed radiomics-based analysis incorporating the clinical-radiographic feature could accurately predict pGGN invasiveness, providing a noninvasive biomarker for the individualized and precise medical treatment of patients.
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Affiliation(s)
- Hui Feng
- grid.452582.cDepartment of Radiology, The Fourth Hospital of Hebei Medical University, No. 12 of Health Road, Shijiazhuang, 050011 China
| | - Gaofeng Shi
- grid.452582.cDepartment of Radiology, The Fourth Hospital of Hebei Medical University, No. 12 of Health Road, Shijiazhuang, 050011 China
| | - Qian Xu
- grid.452582.cDepartment of Radiology, The Fourth Hospital of Hebei Medical University, No. 12 of Health Road, Shijiazhuang, 050011 China
| | | | - Lijia Wang
- grid.452582.cDepartment of Radiology, The Fourth Hospital of Hebei Medical University, No. 12 of Health Road, Shijiazhuang, 050011 China
| | - Xiaojia Cai
- grid.452582.cDepartment of Radiology, The Fourth Hospital of Hebei Medical University, No. 12 of Health Road, Shijiazhuang, 050011 China
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[Research Progress of Relationship between Pleural Deformation and
Visceral Pleural Invasion in Lung Cancer Manifesting as Ground-glass Opacity]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:895-900. [PMID: 36617476 PMCID: PMC9845092 DOI: 10.3779/j.issn.1009-3419.2022.102.50] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Visceral pleural invasion (VPI) is one of the negative prognostic factors of non-small cell lung cancer (NSCLC). With the popularization of computed tomography (CT) screening for lung cancer, more and more ground-glass nodule (GGN) have been found. However, it remains unclear whether the relationship between the pleural deformation of lung cancer manifesting as ground-glass opacity (GGO) and VPI affects the effect of sub-lobectomy, which is reviewed in this paper.
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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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Zhu YQ, Liu C, Mo Y, Dong H, Huang C, Duan YN, Tang LL, Chu YY, Qin J. Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography. Br J Radiol 2022; 95:20210768. [PMID: 35262392 PMCID: PMC10996418 DOI: 10.1259/bjr.20210768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. METHODS CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital (The Third Affiliated Hospital of Sun Yat-sen University) from January 2015 to March 2021 were analyzed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest. ANOVA and least absolute shrinkage and selection operator feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic curves were used to evaluate the classification efficiency. RESULTS 129 pGGOs were included. 2107 radiomic features were extracted from each region of interest. 18 radiomic features were eventually selected for model construction. The area under the curve of the radiomics model was 0.884 [95% confidence interval (CI), 0.818-0.949] in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate. CONCLUSION The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy. ADVANCES IN KNOWLEDGE We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.
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Affiliation(s)
- Yan-qiu Zhu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Chaohui Liu
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Yan Mo
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Hao Dong
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Ya-ni Duan
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Lei-lei Tang
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Yuan-yuan Chu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Jie Qin
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, 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: 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: 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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You J, Zhang G, Gao X, Chen Y, Shu Y. [Value of PET/CT Combined with CT Three-dimensional Reconstruction
in Distinguishing Different Pathological Subtypes of Early Lung Adenocarcinoma]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2021; 24:468-474. [PMID: 34120430 PMCID: PMC8317088 DOI: 10.3779/j.issn.1009-3419.2021.101.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
背景与目的 早期肺腺癌中病理亚型以贴壁为主型的浸润性腺癌(lepidic predominant invasive adenocarcinoma, LPA)与原位腺癌(adenocarcinoma in situ, AIS)、微浸润性腺癌(microinvasive adenocarcinoma, MIA)的良好预后相似,临床上也迫切需要能够区分LPA与非LPA型浸润性腺癌(non-lepidic predominant invasive adenocarcinoma, non-LPA)的手段,本研究拟通过正电子发射型计算机断层显像(positron emission computed tomography, PET)/计算机断层扫描(computed tomography, CT)的最大标准化摄取值(maximal standard uptake value, SUVmax)和CT三维重建后参数探讨术前影像学表现为部分实性结节(part-solid nodules, PSNs)的早期肺腺癌不同病理亚型间的关系。 方法 回顾性分析2016年1月-2019年1月于江苏省苏北人民医院胸外科行解剖性肺切除术且影像学表现为PSNs的早期肺腺癌患者资料,所有患者胸部增强CT和PET/CT资料完整可获取,利用Mimics软件行三维重建,获取肿瘤体积、肿瘤三维平均CT值(3-dimensional mean-CT value, 3Dm-CT)、SUVmax等数据,采用SPSS 25.0进行统计分析,GraphPad Prism 8.3.0绘制受试者工作曲线(receiver operating curve, ROC),P < 0.05为差异有统计学意义。 结果 最终共计67例患者纳入本研究,按病理亚型不同将所有患者分为两组,AIS、MIA及浸润性腺癌(invasive adenocarcinoma, IAC)中的LPA归为低危组28例(41.8%),其余non-LPA如腺泡型(acinar pattern-predominant adeno-carcinoma, APA)、乳头型(papillary pattern-predominant adenocarcinoma, PPA)、微乳头型(micropapillary pattern-predominant adeno-carcinoma, MPA)归为高危组39例(58.2%),两组间SUVmax(t=3.153, P=0.002)、肿瘤体积(t=3.331, P=0.001)、实性/磨玻璃成分体积(t=2.74, P=0.006) /(t=3.127, P=0.002)、实性/磨玻璃成分3Dm-CT(t=3.655, P < 0.001) /(t=7.082, P < 0.001) 均具有显著统计学意义。ROC曲线提示:SUVmax[曲线下面积(area under curve, AUC)=0.727]、肿瘤体积(AUC=0.740)、磨玻璃成分体积(AUC=0.725)、实性成分3Dm-CT(AUC=0.763)、磨玻璃成分3Dm-CT(AUC=0.756)预测效能最佳。将上述AUC > 0.7的协变量纳入多因素ROC曲线分析,获得联合预测因子(AUC=0.835)具有中等以上预测价值。 结论 PET/CT中SUVmax和CT三维重建参数与影像学表现为PSNs的早期肺腺癌的不同病理亚型具有显著相关性,联合SUVmax、肿瘤体积、磨玻璃成分体积和实性/磨玻璃成分3Dm-CT对鉴别表现为PSNs的早期肺腺癌的病理亚型具有一定价值。
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Affiliation(s)
- Jie You
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225001, China
| | - Guozhong Zhang
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225001, China
| | - Xianglong Gao
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225001, China
| | - Yong Chen
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225001, China
| | - Yusheng Shu
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225001, China
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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 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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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 2021; 14:100936. [PMID: 33221688 PMCID: PMC7689413 DOI: 10.1016/j.tranon.2020.100936] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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
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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Pereira T, Freitas C, Costa JL, Morgado J, Silva F, Negrão E, de Lima BF, da Silva MC, Madureira AJ, Ramos I, Hespanhol V, Cunha A, Oliveira HP. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. J Clin Med 2020; 10:E118. [PMID: 33396348 PMCID: PMC7796087 DOI: 10.3390/jcm10010118] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/28/2020] [Accepted: 12/28/2020] [Indexed: 12/16/2022] Open
Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.
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Affiliation(s)
- Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - José Luis Costa
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
- Institute for Research and Innovation in Health of the University of Porto, i3S, 4200-135 Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto, IPATIMUP, 4200-135 Porto, Portugal
| | - Joana Morgado
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
| | - Francisco Silva
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
| | - Eduardo Negrão
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Isabel Ramos
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - Venceslau Hespanhol
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - António Cunha
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Department of Engineering, University of Trás-os-Montes and Alto Douro, UTAD, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
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17
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Abstract
Most focal persistent ground glass nodules (GGNs) do not progress over 10 years. Research suggests that GGNs that do not progress, those that do, and solid lung cancers are fundamentally different diseases, although histologically they seem similar. Surveillance of GGNs to identify those that gradually progress is safe and does not risk losing a window. GGNs with 5 mm solid component or less than 10 mm consolidation (mediastinal and lung windows, respectively, on thin slice CT) are highly curable with resection. The optimal type of resection is unclear; sublobar resection is reasonable but an adequate margin is critically important.
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Affiliation(s)
- Vincent J Mase
- Department of Surgery, Division of Thoracic Surgery, Yale University School of Medicine, PO Box 208062, New Haven, CT 06520-8062, USA
| | - Frank C Detterbeck
- Department of Surgery, Division of Thoracic Surgery, Yale University School of Medicine, PO Box 208062, New Haven, CT 06520-8062, USA.
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18
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Xu F, Zhu W, Shen Y, Wang J, Xu R, Qutesh C, Song L, Gan Y, Pu C, Hu H. Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma. Front Oncol 2020; 10:872. [PMID: 32850301 PMCID: PMC7432133 DOI: 10.3389/fonc.2020.00872] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/04/2020] [Indexed: 01/15/2023] Open
Abstract
Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.
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Affiliation(s)
- Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Shen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University, Ningbo, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Rui Xu
- DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China.,DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian, China
| | - Chooah Qutesh
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijiang Song
- Department of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Yang HH, Lv YL, Fan XH, Ai ZY, Xu XC, Ye B, Hu DZ. Factors distinguishing invasive from pre-invasive adenocarcinoma presenting as pure ground glass pulmonary nodules. Radiat Oncol 2020; 15:186. [PMID: 32736567 PMCID: PMC7393870 DOI: 10.1186/s13014-020-01628-x] [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: 05/09/2020] [Accepted: 07/21/2020] [Indexed: 04/18/2023] Open
Abstract
Background To investigate predictors of pathological invasiveness and prognosis of lung adenocarcinoma in patients with pure ground-glass nodules (pGGNs). Methods Clinical and computed tomography (CT) features of invasive adenocarcinomas (IACs) and pre-IACs were retrospectively compared in 641 consecutive patients with pGGNs and confirmed lung adenocarcinomas who had undergone postoperative CT follow-up. Potential predictors of prognosis were investigated in these patients. Results Of 659 pGGNs in 641 patients, 258 (39.1%) were adenocarcinomas in situ, 265 (40.2%) were minimally invasive adenocarcinomas, and 136 (20.6%) were IACs. Respective optimal cutoffs for age, serum carcinoembryonic antigen concentration, maximal diameter, mean diameter, and CT density for distinguishing pre-IACs from IACs were 53 years, 2.19 ng/mL, 10.78 mm, 10.09 mm, and − 582.28 Hounsfield units (HU). Univariable analysis indicated that sex, age, maximal diameter, mean diameter, CT density, and spiculation were significant predictors of lung IAC. In multivariable analysis age, maximal diameter, and CT density were significant predictors of lung IAC. During a median follow-up of 41 months no pGGN IACs recurred. Conclusions pGGNs may be lung IACs, especially in patients aged > 55 years with lesions that are > 1 cm in diameter and exhibit CT density > − 600 HU. pGGN IACs of < 3 cm in diameter have good post-resection prognoses.
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Affiliation(s)
- Huan-Huan Yang
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.,Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Yi-Lv Lv
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China
| | - Xing-Hai Fan
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.,Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Zhi-Yong Ai
- Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Xiu-Chun Xu
- Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Bo Ye
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.
| | - Ding-Zhong Hu
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.
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20
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Zhu T, Wang Y, Zhou S, Zhang N, Xia L. A Comparative Study of Chest Computed Tomography Features in Young and Older Adults With Corona Virus Disease (COVID-19). J Thorac Imaging 2020; 35:W97-W101. [PMID: 32235187 PMCID: PMC7253040 DOI: 10.1097/rti.0000000000000513] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE To analyze the most common computed tomography (CT) findings of pneumonia caused by new coronavirus in younger patients (60 and younger) and older adults (older than 60). MATERIALS AND METHODS The chest CT images of 72 symptomatic patients with corona virus disease (COVID-19) were analyzed retrospectively, including 44 younger patients (47.5±8.7 y old) and 28 older patients (68.4±6.0 y old). CT findings including density (pure ground-glass opacities, ground-glass opacities with consolidation, consolidation), the number of lobes involved, lesion distribution, and the main accompanying signs were analyzed and compared. RESULTS Characteristic CT findings included the lobes of bilateral lung extensively involved, ground-glass opacity and ground-glass opacity with consolidation in the peripheral area, sometimes accompanied by interlobular septal thickening, and subpleural line and pleural thickening. Compared with the younger group, the proportion of extensive involvement of lung lobes was higher in the elderly group (71.4% vs. 36.4%, P=0.009), and subpleural line and pleural thickening were more likely to occur (50.0% vs. 25.0%, and 71.4% vs. 40.9%, P=0.030 and 0.011, respectively). CONCLUSION Elderly and younger patients with corona virus disease have some common CT features, but older patients are more likely to have extensive lung lobe involvement, and subpleural line and pleural thickening. These differentiated characteristics may be related to the progress and prognosis of the disease.
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Affiliation(s)
- Tingting Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Yujin Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Shuchang Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Na Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
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21
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Sun Y, Li C, Jin L, Gao P, Zhao W, Ma W, Tan M, Wu W, Duan S, Shan Y, Li M. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. Eur Radiol 2020; 30:3650-3659. [PMID: 32162003 PMCID: PMC7305264 DOI: 10.1007/s00330-020-06776-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/14/2019] [Accepted: 02/24/2020] [Indexed: 12/18/2022]
Abstract
Objectives To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic–radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. Results Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic–radiomics model (AUC 0.77; 95% CI, 0.69–0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62–0.81) and Rad-score (AUC 0.72; 95% CI, 0.63–0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. Conclusions The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. Key Points • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic–radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up. Electronic supplementary material The online version of this article (10.1007/s00330-020-06776-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Cheng Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Wei Zhao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China.,Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Weilan Wu
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | | | - Yuqing Shan
- Department of Radiology, The People's Hospital of Rizhao, Rizhao City, 276800, China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China. .,Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Shanghai, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
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22
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A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma. Clin Lung Cancer 2020; 21:314-325.e4. [PMID: 32273256 DOI: 10.1016/j.cllc.2020.01.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/24/2019] [Accepted: 01/20/2020] [Indexed: 01/12/2023]
Abstract
OBJECTIVES To develop an imaging reporting system for the classification of 3 adenocarcinoma subtypes of computed tomography (CT)-detected subsolid pulmonary nodules (SSNs) in clinical patients. METHODS Between November 2011 and October 2017, 437 pathologically confirmed SSNs were retrospectively identified. SSNs were randomly divided 2:1 into a training group (291 cases) and a testing group (146 cases). CT-imaging characteristics were analyzed using multinomial univariable and multivariable logistic regression analysis to identify discriminating factors for the 3 adenocarcinoma subtypes (pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma). These factors were used to develop a classification and regression tree model. Finally, an SSN Imaging Reporting System (SSN-IRS) was constructed based on the optimized classification model. For validation, the classification performance was evaluated in the testing group. RESULTS Of the CT-derived characteristics of SSNs, qualitative density (nonsolid or part-solid), core (non-core or core), semantic features (pleural indentation, vacuole sign, vascular invasion), and diameter of solid component (≤6 mm or >6 mm), were the most important factors for the SSN-IRS. The total sensitivity, specificity, and diagnostic accuracy of the SSN-IRS was 89.0% (95% confidence interval [CI], 84.8%-92.4%), 74.6% (95% CI, 70.8%-78.1%), and 79.4% (95% CI, 76.5%-82.0%) in the training group and 84.9% (95% CI, 78.1%-90.3%), 68.5% (95% CI, 62.8%-73.8%), and 74.0% (95% CI, 69.6%-78.0%) in the testing group, respectively. CONCLUSIONS The SSN-IRS can classify 3 adenocarcinoma subtypes using CT-based characteristics of subsolid pulmonary nodules. This classification tool can help clinicians to make follow-up recommendations or decisions for surgery in clinical patients with SSNs.
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23
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Li X, Zhang W, Yu Y, Zhang G, Zhou L, Wu Z, Liu B. CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction. BMC Cancer 2020; 20:60. [PMID: 31992239 PMCID: PMC6986053 DOI: 10.1186/s12885-020-6556-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 01/20/2020] [Indexed: 01/03/2023] Open
Abstract
Background The value of the CT features and quantitative analysis of lung subsolid nodules (SSNs) in the prediction of the pathological grading of lung adenocarcinoma is discussed. Methods Clinical data and CT images of 207 cases (216 lesions) with CT manifestations of an SSNs lung adenocarcinoma confirmed by surgery pathology were retrospectively analysed. The pathological results were divided into three groups, including atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). Then, the quantitative and qualitative data of these nodules were compared and analysed. Results The mean size, maximum diameter, mean CT value and maximum CT value of the nodules were significantly different among the three groups of AAH/AIS, MIA and IAC and were different between the paired groups (AAH/AIS and MIA or MIA and IAC) (P < 0.05). The critical values of the above indicators between AAH/AIS and MIA were 10.05 mm, 11.16 mm, − 548.00 HU and − 419.74 HU. The critical values of the above indicators between MIA and IAC were 14.42 mm, 16.48 mm, − 364.59 HU and − 16.98 HU. The binary logistic regression analysis of the features with the statistical significance showed that the regression model between AAH/AIS and MIA is logit(p) = − 0.93 + 0.216X1 + 0.004X4. The regression model between MIA and IAC is logit(p) = − 1.242–1.428X5(1) − 1.458X6(1) + 1.146X7(1) + 0.272X2 + 0.005X3. The areas under the curve (AUC) obtained by plotting the receiver operating characteristic curve (ROC) using the regression probabilities of regression models I and II were 0.815 and 0.931. Conclusions Preoperative prediction of pathological classification of CT image features has important guiding value for clinical management. Correct diagnosis results can effectively improve the patient survival rate. Through comprehensive analysis of the CT features and qualitative data of SSNs, the diagnostic accuracy of SSNs can be effectively improved. The logistic regression model established in this study can better predict the pathological classification of SSNs lung adenocarcinoma on CT, and the predictive value is significantly higher than the independent use of each quantitative factor.
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Affiliation(s)
- Xiaohu Li
- Department of Radiology, the First Affiliated Hosptial of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Wei Zhang
- Department of Radiology, The Lu'an affiliated hospital, Anhui Medical University, No.21wanxi Road, Luan, Anhui, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hosptial of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China.
| | - Guihong Zhang
- Department of Pathology, the First Affiliated Hosptial of Anhui Medical University, No.218 Jixi Road, Hefei, Anhui, China
| | - Lifen Zhou
- Department of Radiology, the First Affiliated Hosptial of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Zongshan Wu
- Department of Radiology, The Lu'an affiliated hospital, Anhui Medical University, No.21wanxi Road, Luan, Anhui, China
| | - Bin Liu
- Department of Radiology, the First Affiliated Hosptial of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China.
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24
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Hu T, Wang S, E X, Yuan Y, Huang L, Wang J, Shi D, Li Y, Peng W, Tong T. CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules. Front Oncol 2019; 9:1241. [PMID: 31803619 PMCID: PMC6877751 DOI: 10.3389/fonc.2019.01241] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 10/29/2019] [Indexed: 12/23/2022] Open
Abstract
Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis. Methods: Our study enrolled 196 CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction. Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC = 0.919) and validation dataset (AUC = 0.895), and good calibration. Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.
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Affiliation(s)
- TingDan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - ShengPing Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiangyu E
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ye Yuan
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Lv Huang
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - JiaZhou Wang
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - DeBing Shi
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuan Li
- Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - WeiJun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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25
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刘 宝. [Diagnosis and Treatment of Pulmonary Ground-glass Nodules]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2019; 22:449-456. [PMID: 31315784 PMCID: PMC6712268 DOI: 10.3779/j.issn.1009-3419.2019.07.07] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 03/06/2019] [Accepted: 03/10/2019] [Indexed: 12/19/2022]
Abstract
Recent widespread use of high resolution computed tomography (HRCT) for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass nodule (GGN). However, it had been proved that GGN was well associated with lung cancer in previous studies. Therefore, the classification, imaging characteristics, pathological type, follow-up, suggested managements and other clinical concerns of GGN were reviewed in this paper.
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Affiliation(s)
- 宝东 刘
- />100053 北京,首都医科大学宣武医院胸外科Department of Toracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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26
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Jiang G, Chen C, Zhu Y, Xie D, Dai J, Jin K, Shen Y, Wang H, Li H, Zhang L, Gao S, Chen K, Zhang L, Zhou X, Shi J, Wang H, Xie B, Jiang L, Fan J, Zhao D, Chen Q, Duan L, He W, Zhou Y, Liu H, Zhao X, Zhang P, Qin X. [Shanghai Pulmonary Hospital Experts Consensus on the Management of Ground-Glass Nodules Suspected as Lung Adenocarcinoma (Version 1)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2018; 21:147-159. [PMID: 29587930 PMCID: PMC5973030 DOI: 10.3779/j.issn.1009-3419.2018.03.05] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
随着胸部计算机断层扫描(computed tomography, CT)检查,尤其是低剂量薄层CT筛查项目在中国的广泛开展,越来越多的无症状肺部磨玻璃结节(ground-glass nodules, GGNs)被发现。虽然国内及国际上已发布了一系列针对肺部GGNs的指南,但是这些指南的撰写者多来自呼吸、肿瘤及影像专业,可能缺乏对现代微创胸外科的充分认识,造成外科手术在肺部GGNs诊治中的作用不明确,甚至被低估;而且,肺部肿瘤相关的各学科对于早期肺癌,尤其是浸润前病变的处理也缺乏统一规范。因此,基于国内外现有文献及上海市肺科医院多年积累的经验,上海市肺科医院撰写了此诊疗共识。本共识推荐对于疑似肺腺癌的GGNs进行多学科评估,依据诊断,选择合理的处置方式。对于疑似原位腺癌,推荐进行胸部薄层CT随访,或在特定情况下进行不超过肺段切除的限制性肺切除;对于疑似微浸润腺癌,推荐进行限制性肺切除或肺叶切除;对于疑似浸润性腺癌,建议依据病灶是否含有磨玻璃成分、位置、大小、个数及患者躯体情况选择合理的手术方式;而肺多发结节的处理原则推荐为主病灶优先,兼顾次要病灶,综合选择治疗方案。
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Affiliation(s)
- Gening Jiang
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Chang Chen
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Yuming Zhu
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Dong Xie
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Jie Dai
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Kaiqi Jin
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Yingran Shen
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Haifeng Wang
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Hui Li
- Capital Medical University Affiliated Beijing Chao-Yang Hospital, Beijing 100020 , China
| | - Lanjun Zhang
- Sun Yat-sen University Cancer Center, Guangzhou 510060 , China
| | - Shugeng Gao
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Keneng Chen
- Peking University Cancer Hospital and Institute, Beijing 100142 , China
| | - Lei Zhang
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Xiao Zhou
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Jingyun Shi
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Hao Wang
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Boxiong Xie
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Lei Jiang
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Jiang Fan
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Deping Zhao
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Qiankun Chen
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Liang Duan
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Wenxin He
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Yiming Zhou
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Hongcheng Liu
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Xiaogang Zhao
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Peng Zhang
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
| | - Xiong Qin
- Tongji University affiliated Shanghai Pulmonary Hospital, Shanghai 200043 , China
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Milanese G, Sverzellati N, Pastorino U, Silva M. Adenocarcinoma in pure ground glass nodules: histological evidence of invasion and open debate on optimal management. J Thorac Dis 2017; 9:2862-2867. [PMID: 29221257 DOI: 10.21037/jtd.2017.08.120] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Gianluca Milanese
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ugo Pastorino
- Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mario Silva
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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28
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Heidinger BH, Anderson KR, Nemec U, Costa DB, Gangadharan SP, VanderLaan PA, Bankier AA. Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodules: Correlating CT Size, Volume, Density, and Roundness with Histopathologic Invasion and Size. J Thorac Oncol 2017; 12:1288-1298. [DOI: 10.1016/j.jtho.2017.05.017] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 05/14/2017] [Accepted: 05/15/2017] [Indexed: 12/15/2022]
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Nemec U, Heidinger BH, Anderson KR, Westmore MS, VanderLaan PA, Bankier AA. Software-based risk stratification of pulmonary adenocarcinomas manifesting as pure ground glass nodules on computed tomography. Eur Radiol 2017; 28:235-242. [PMID: 28710575 PMCID: PMC5717124 DOI: 10.1007/s00330-017-4937-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 05/08/2017] [Accepted: 06/08/2017] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To assess the performance of the "Computer-Aided Nodule Assessment and Risk Yield" (CANARY) software in the differentiation and risk assessment of histological subtypes of lung adenocarcinomas manifesting as pure ground glass nodules on computed tomography (CT). METHODS 64 surgically resected and histologically proven adenocarcinomas manifesting as pure ground-glass nodules on CT were assessed using CANARY software, which classifies voxel-densities into three risk components (low, intermediate, and high risk). Differences in risk components between histological adenocarcinoma subtypes were analysed. To determine the optimal threshold reflecting the presence of an invasive focus, sensitivity, specificity, negative predictive value, and positive predictive value were calculated. RESULTS 28/64 (44%) were adenocarcinomas in situ (AIS); 26/64 (41%) were minimally invasive adenocarcinomas (MIA); and 10/64 (16%) were invasive ACs (IAC). The software showed significant differences in risk components between histological subtypes (P<0.001-0.003). A relative volume of 45% or less of low-risk components was associated with histological invasiveness (specificity 100%, positive predictive value 100%). CONCLUSIONS CANARY-based risk assessment of ACs manifesting as pure ground glass nodules on CT allows the differentiation of their histological subtypes. A threshold of 45% of low-risk components reflects invasiveness in these groups. KEY POINTS • CANARY-based risk assessment allows the differentiation of their histological subtypes. • 45% or less of low-risk component reflects histological invasiveness. • CANARY has potential role in suspected adenocarcinomas manifesting as pure ground-glass nodules.
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Affiliation(s)
- Ursula Nemec
- Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Medical University of Vienna, Waehringer Guertel 18-20, Vienna, Austria.
| | - Benedikt H Heidinger
- Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Kevin R Anderson
- Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Paul A VanderLaan
- Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alexander A Bankier
- Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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30
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Rusu M, Rajiah P, Gilkeson R, Yang M, Donatelli C, Thawani R, Jacono FJ, Linden P, Madabhushi A. Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. Eur Radiol 2017; 27:4209-4217. [PMID: 28386717 DOI: 10.1007/s00330-017-4813-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2016] [Revised: 01/25/2017] [Accepted: 03/14/2017] [Indexed: 12/01/2022]
Abstract
OBJECTIVE To develop an approach for radiology-pathology fusion of ex vivo histology of surgically excised pulmonary nodules with pre-operative CT, to radiologically map spatial extent of the invasive adenocarcinomatous component of the nodule. METHODS Six subjects (age: 75 ± 11 years) with pre-operative CT and surgically excised ground-glass nodules (size: 22.5 ± 5.1 mm) with a significant invasive adenocarcinomatous component (>5 mm) were included. The pathologist outlined disease extent on digitized histology specimens; two radiologists and a pulmonary critical care physician delineated the entire nodule on CT (in-plane resolution: <0.8 mm, inter-slice distance: 1-5 mm). We introduced a novel reconstruction approach to localize histology slices in 3D relative to each other while using CT scan as spatial constraint. This enabled the spatial mapping of the extent of tumour invasion from histology onto CT. RESULTS Good overlap of the 3D reconstructed histology and the nodule outlined on CT was observed (65.9 ± 5.2%). Reduction in 3D misalignment of corresponding anatomical landmarks on histology and CT was observed (1.97 ± 0.42 mm). Moreover, the CT attenuation (HU) distributions were different when comparing invasive and in situ regions. CONCLUSION This proof-of-concept study suggests that our fusion method can enable the spatial mapping of the invasive adenocarcinomatous component from 2D histology slices onto in vivo CT. KEY POINTS • 3D reconstructions are generated from 2D histology specimens of ground glass nodules. • The reconstruction methodology used pre-operative in vivo CT as 3D spatial constraint. • The methodology maps adenocarcinoma extent from digitized histology onto in vivo CT. • The methodology potentially facilitates the discovery of CT signature of invasive adenocarcinoma.
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Affiliation(s)
- Mirabela Rusu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. .,GE Global Research, Niskayuna, NY, 12309, USA.
| | - Prabhakar Rajiah
- UT Southwestern Medical Center, Dallas, TX, USA.,University Hospitals, Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, USA
| | - Robert Gilkeson
- University Hospitals, Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, USA
| | - Michael Yang
- University Hospitals, Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, USA
| | - Christopher Donatelli
- University Hospitals, Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, USA
| | - Rajat Thawani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Frank J Jacono
- University Hospitals, Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, USA.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Philip Linden
- University Hospitals, Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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31
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MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, Mehta AC, Ohno Y, Powell CA, Prokop M, Rubin GD, Schaefer-Prokop CM, Travis WD, Van Schil PE, Bankier AA. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology 2017; 284:228-243. [PMID: 28240562 DOI: 10.1148/radiol.2017161659] [Citation(s) in RCA: 1331] [Impact Index Per Article: 190.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The Fleischner Society Guidelines for management of solid nodules were published in 2005, and separate guidelines for subsolid nodules were issued in 2013. Since then, new information has become available; therefore, the guidelines have been revised to reflect current thinking on nodule management. The revised guidelines incorporate several substantive changes that reflect current thinking on the management of small nodules. The minimum threshold size for routine follow-up has been increased, and recommended follow-up intervals are now given as a range rather than as a precise time period to give radiologists, clinicians, and patients greater discretion to accommodate individual risk factors and preferences. The guidelines for solid and subsolid nodules have been combined in one simplified table, and specific recommendations have been included for multiple nodules. These guidelines represent the consensus of the Fleischner Society, and as such, they incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. Changes from the previous guidelines issued by the Fleischner Society are based on new data and accumulated experience. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 13, 2017.
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Affiliation(s)
- Heber MacMahon
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - David P Naidich
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Jin Mo Goo
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Kyung Soo Lee
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Ann N C Leung
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - John R Mayo
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Atul C Mehta
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Yoshiharu Ohno
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Charles A Powell
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Mathias Prokop
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Geoffrey D Rubin
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Cornelia M Schaefer-Prokop
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - William D Travis
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Paul E Van Schil
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
| | - Alexander A Bankier
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (H.M.); Department of Radiology, New York University Langone Medical Center, New York, NY (D.P.N.); Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (K.S.L.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.N.C.L.); Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, British Columbia, Canada (J.R.M.); Department of Medicine, Cleveland Clinic, Cleveland, Ohio (A.C.M.); Department of Radiology, Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan (Y.O.); Pulmonary and Critical Care Medicine, ICAHN School of Medicine at Mount Sinai, New York, NY (C.A.P.); Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Duke University School of Medicine, Durham, NC (G.D.R.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C.M.S.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY (W.D.T.); Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium (P.E.V.S.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B)
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