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Park J, Doo KW, Sung YE, Jung JI, Chang S. Computed Tomography Findings for Predicting Invasiveness of Lung Adenocarcinomas Manifesting as Pure Ground-Glass Nodules. Can Assoc Radiol J 2023; 74:137-146. [PMID: 35840350 DOI: 10.1177/08465371221110913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Purpose: To comprehensively evaluate qualitative and quantitative features for predicting invasiveness of pure ground-glass nodules (pGGNs) using multiplanar computed tomography. Methods: Ninety-three resected pGGNs (16 atypical adenomatous hyperplasia [AAH], 18 adenocarcinoma in situ [AIS], 31 minimally invasive adenocarcinoma [MIA], and 28 invasive adenocarcinoma [IA]) were retrospectively included. Two radiologists analyzed qualitative and quantitative features on three standard planes. Univariable and multivariable logistic regression analyses were performed to identify features to distinguish the pre-invasive (AAH/AIS) from the invasive (MIA/IA) group. Results: Tumor size showed high area under the curve (AUC) for predicting invasiveness (.860, .863, .874, and .893, for axial long diameter [AXLD], multiplanar long diameter, mean diameter, and volume, respectively). The AUC for AXLD (cutoff, 11 mm) was comparable to that of the volume (P = .202). The invasive group had a significantly higher number of qualitative features than the pre-invasive group, regardless of tumor size. Six out of 59 invasive nodules (10.2%) were smaller than 11 mm, and all had at least one qualitative feature. pGGNs smaller than 11 mm without any qualitative features (n = 16) were all pre-invasive. In multivariable analysis, AXLD, vessel change, and the presence or number of qualitative features were independent predictors for invasiveness. The model with AXLD and the number of qualitative features achieved the highest AUC (.902, 95% confidence interval .833-.971). Conclusion: In adenocarcinomas manifesting as pGGNs on computed tomography, AXLD and the number of qualitative features are independent risk factors for invasiveness; small pGGNs (<11 mm) without qualitative features have low probability of invasiveness.
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Affiliation(s)
- Jeaneun Park
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyung Won Doo
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Yeoun Eun Sung
- Department of Hospital Pathology, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jung Im Jung
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Suyon Chang
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
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Guo CR, Han R, Xue F, Xu L, Ren WG, Li M, Feng Z, Hu BC, Peng ZM. Expression and clinical significance of CD31, CD34, and CD105 in pulmonary ground glass nodules with different vascular manifestations on CT. Front Oncol 2022; 12:956451. [PMID: 36185269 PMCID: PMC9521677 DOI: 10.3389/fonc.2022.956451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Blood vessel passage on CT exerts a vital part in early diagnosis as well as treatment of carcinoma of the lungs. Intratumoral microvascular density (iMVD) has gradually become the focus of research on biological behavior, appearance, and evolution of malignant tumors nowadays. The aim of this paper was to verify whether there is a correlation between the iMVD and the vascular morphology of ground glass nodules (GGNs). A total of 109 patients with pulmonary GGN were classified into three groups (I,II, and III) according to the vascular morphology on CT, and their expression of CD31-, CD34-, and CD105-labeled iMVD was detected by the streptoavidin–biotin method, statistically analyzing the iMVD values of each group. The expression of CD31, CD34, and CD105 in different lung tissues was significantly different, with remarkably higher iMVD in lung cancer tissues than in adjacent normal lung tissues. In the imaging sort of types I, II, and III according to the means of vascular passage, the iMVD expression of CD31, CD34, and CD105 was significantly different between groups. These data suggest that the presence and the abnormal morphology of vessels seen within GGNs indicate the occurrence and progression of lung cancer in pathology. It offers a strong theoretical foundation for early diagnosis of carcinoma of the lungs, thus providing a more precise clinical diagnosis and prognosis of early-stage lung cancer.
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Affiliation(s)
- Chen-ran Guo
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong University, Jinan, China
| | - Rui Han
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Feng Xue
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong University, Jinan, China
| | - Lin Xu
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Wan-gang Ren
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Meng Li
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Zhen Feng
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Ben-chuang Hu
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
| | - Zhong-min Peng
- Department of Thoracic Surgery, Shandong Provincial Hospital, Jinan, China
- Shandong University, Jinan, China
- Shandong First Medical University (Shandong Academy Of Medical Science), Jinan, China
- *Correspondence: Zhong-min Peng,
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Yan B, Chang Y, Jiang Y, Liu Y, Yuan J, Li R. A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma. Front Surg 2022; 9:973523. [PMID: 36090345 PMCID: PMC9458920 DOI: 10.3389/fsurg.2022.973523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD. Methods A total of 301 surgical LUAD patients with HRCT-confirmed GGN were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 171 invasive and 130 non-invasive LUAD patients). Results GGN features including shorter distance from pleura, larger diameter, area and mean CT attenuation, more heterogeneous uniformity of density, irregular shape, coarse margin, not defined nodule-lung interface, spiculation, pleural indentation, air bronchogram, vacuole sign, vessel changes, lobulation were observed in invasive LUAD patients compared with non-invasive LUAD patients. After adjustment by multivariate logistic regression model, GGN diameter (OR = 1.490, 95% CI, 1.326-1.674), mean CT attenuation (OR = 1.007, 95% CI, 1.004-1.011) and heterogeneous uniformity of density (OR = 3.009, 95% CI, 1.485-6.094) were independent risk factors for invasive LUAD. In addition, a predictive model integrating these three independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.919, 95% CI, 0.889-0.949). Conclusions ILAG predictive model integrating GGN diameter, mean CT attenuation and heterogeneous uniformity of density via HRCT shows great potential for early estimation of LUAD invasiveness.
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Affiliation(s)
- Bo Yan
- Clinical Research Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanyuan Chang
- Clinical Research Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifeng Jiang
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuan Liu
- Department of Statistics Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junyi Yuan
- Department of Information Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Li
- Clinical Research Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 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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Zhang T, Li X, Liu J. Prediction of the Invasiveness of Ground-Glass Nodules in Lung Adenocarcinoma by Radiomics Analysis Using High-Resolution Computed Tomography Imaging. Cancer Control 2022; 29:10732748221089408. [PMID: 35848489 PMCID: PMC9297444 DOI: 10.1177/10732748221089408] [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] [Indexed: 12/03/2022] Open
Abstract
Background Pure ground-glass nodules (pGGNs) have been considered inert tumors due to their biological behavior; however, their prognosis is not completely consistent because of differences in internal pathological component. The aim of this study was to explore whether radiomics can be used to identify the invasiveness of pGGNs. Methods The retrospective study received the relevant ethical approval. After postoperative pathological confirmation, sixty-five patients with lung adenocarcinoma pGGNs (≤30 mm) were enrolled in this study from January 2015 to October 2018. All the cases were randomly divided into training and test groups in a 7:3 ratio. In total, 385 radiomics features were obtained from HRCT images, and then least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the training group to obtain optimal features to distinguish the invasion degree of lesions. The diagnostic efficiency of the radiomics model was estimated by the area under the curve (AUC) of the receiver operating curve (ROC), and verified by the test group. Results The optimal features (“GLCMEntropy_angle135_offset1” and “Sphericity”) were selected after applying the LASSO regression to develop the proposed radiomics model. This prediction model exhibited good differentiation between pre-invasive and invasive lesions. The AUC for the test group was 0.824 (95%CI: 0.599-1.000), indicating that the radiomics model has some prediction ability. Conclusion The HRCT radiomics features can discriminate pre-invasive from invasive lung adenocarcinoma pGGNs. This non-invasive method can provide more information for surgeons before operation, and can also predict the prognosis of patients to some extent.
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Affiliation(s)
- Tianqi Zhang
- College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China.,Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China
| | - Xiuling Li
- College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China
| | - Jianhua Liu
- Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China
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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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Li X, Ren F, Wang S, He Z, Song Z, Chen J, Xu S. The Epidemiology of Ground Glass Opacity Lung Adenocarcinoma: A Network-Based Cumulative Meta-Analysis. Front Oncol 2020; 10:1059. [PMID: 32793469 PMCID: PMC7386063 DOI: 10.3389/fonc.2020.01059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/27/2020] [Indexed: 12/12/2022] Open
Abstract
Introduction: Due to the introduction of low-dose computed tomography (CT) and screening procedures, the proportion of early-stage lung cancer with ground glass opacity (GGO) manifestation is increasing in clinical practice. However, its epidemiological characteristics is still not fully investigated. Methods: We retrieved all solitary GGO adenocarcinoma lung cancer (ADLC) on the PubMed, Cochrane Library, and Embase databases until January 1, 2019 and extracted the general information to perform the meta-analysis, mainly focusing on age, gender, and smoking status. Results: A total of 8,793 solitary GGO ADLC patients from 53 studies were included in this analysis. The final pooled analysis showed that the female proportion, average diagnosis age, and non-smoking proportion of solitary GGO ADLC was 0.62 (95% CI, 0.60–0.64), 56.97 (95% CI, 54.56–59.37), and 0.72 (95% CI, 0.66–0.77), respectively. The cumulative meta-analysis and meta-trend analysis confirmed that the average age at diagnosis has been decreasing while the non-smoking proportion significantly increased in the past two decades. Conclusions: From our epidemiological analysis, it demonstrates that the clinical characteristics of GGO lung cancer patients may be out of the high-risk factors. Therefore, we propose to reconsider the risk assessment and current lung cancer screening criteria.
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Affiliation(s)
- Xiongfei Li
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Fan Ren
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Shuhang Wang
- Department of Clinical Trials Center, National Cancer Center, Cancer Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Zhicheng He
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuoqing Song
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Chen
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Song Xu
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
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Qiu T, Ru X, Yin K, Yu J, Song Y, Wu J. Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis. Jpn J Radiol 2020; 38:761-770. [PMID: 32356236 DOI: 10.1007/s11604-020-00957-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 03/16/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE The aim of the study is to construct two nomograms for predicting the invasive extent of pulmonary adenocarcinoma and nodule growth in patients with pulmonary pure ground-glass nodules (pGGN). METHOD Consecutive patients with pGGNs (n = 172) were retrospectively studied at one institution, formed the development cohort in predicting IPAs' nomogram. A separate cohort of patients with pGGNs (n = 116) from another institution was used for validation. For the predicting growth nomogram, the primary cohort of patients with pGGNs (n = 80) was from the former institution. We developed the nomogram for predicting IPA using binary logistic regression model, and a Cox multivariable model for the growth nomogram. We assessed nomogram model performance by calibration and discrimination (C-index). RESULTS The variables selected in binary logistic regression model (lesion size and shape) had a significant effect on identifying IPA from preinvasive lesion. The C-index of the development and validation cohort were 0.819 (95% CI 0.753-0.874) and 0.811 (95% CI 0.728-0.878), respectively. The risk variables (lesion size, blood vessel types) were selected in the multivariable Cox model. The C-index was 0.880 in the development cohort. CONCLUSION Our nomograms are reliable prognostic methods that can predict the invasiveness of pulmonary adenocarcinomas and the growth of pure GGN in preoperative.
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Affiliation(s)
- Taichun Qiu
- Rodiology Department, People's Hospital of Deyang City, No. 173, Taishan North Rd, Deyang, Sichuan, China.,Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China
| | - Xiaoshuang Ru
- Radiology Department, Dalian Municipal Central Hospital Affiliated of Dalian Medical University, Dalian Medical University, No. 42, Xuegong Rd, Shahekou District, Dalian, China
| | - Ke Yin
- Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China
| | - Jing Yu
- Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China
| | - Yang Song
- Radiology Department, Dalian Municipal Central Hospital Affiliated of Dalian Medical University, Dalian Medical University, No. 42, Xuegong Rd, Shahekou District, Dalian, China
| | - Jianlin Wu
- Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China.
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9
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CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules. AJR Am J Roentgenol 2020; 215:351-358. [PMID: 32348187 DOI: 10.2214/ajr.19.22381] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm2; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm2), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.
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10
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Jia M, Yu S, Cao L, Sun PL, Gao H. Clinicopathologic Features and Genetic Alterations in Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma of the Lung: Long-Term Follow-Up Study of 121 Asian Patients. Ann Surg Oncol 2020; 27:3052-3063. [PMID: 32048092 DOI: 10.1245/s10434-020-08241-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Indexed: 11/18/2022]
Abstract
BACKGROUND Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are both small tumors with good prognosis after surgical resection, and most of them present as ground glass opacities (GGOs) on computed tomography (CT) screening. However, the differences in clinicopathologic features and genetic alterations between AIS and MIA are poorly elaborated, and few studies have evaluated the prognosis of MIA with different invasive components. Meanwhile, the histological features of lung lesions presenting as unchanged pure GGOs are barely understood. METHODS Clinicopathologic features and genetic alterations of AIS (n = 59) and MIA (n = 62) presenting as GGOs were analyzed. Long-term preoperative observation (ranging from 2 to 1967 days) and postoperative follow-up (ranging from 0 to 92 months) was conducted. RESULTS The tumor size and consolidation/tumor ratio were significantly larger in the MIA cohort than those in the AIS cohort both on CT and microscopy images. Immunohistochemically, the expression of p53, Ki67, and cyclin D1 was higher in MIA than in AIS. The EGFR mutation rate was significantly higher in MIA, while other genetic alterations showed no differences. Six MIA cases showed recurrence or metachronous adenocarcinoma and all the cases with a predominant micropapillary invasive pattern demonstrated this feature. CONCLUSIONS The current CT measurements may be helpful in distinguishing AIS from MIA, but show limited utility in predicting the histology of unchanged pure GGOs. The invasive pattern may have an influence on the postoperative process of MIA; therefore, further studies are needed to evaluate the current diagnostic criteria and treatment strategy for MIA.
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Affiliation(s)
- Meng Jia
- Department of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, China
| | - Shili Yu
- Department of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, China
| | - Lanqing Cao
- Department of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, China
| | - Ping-Li Sun
- Department of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, China.
| | - Hongwen Gao
- Department of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, China.
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Meng Q, Ren P, Gao P, Dou X, Chen X, Guo L, Song Y. Effectiveness and Feasibility of Complementary Lung-RADS Version 1.1 in Risk Stratification for pGGN in LDCT Lung Cancer Screening in a Chinese Population. Cancer Manag Res 2020; 12:189-198. [PMID: 32021435 PMCID: PMC6957006 DOI: 10.2147/cmar.s232269] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 12/30/2019] [Indexed: 12/18/2022] Open
Abstract
Purpose To evaluate the effectiveness of using a modified lung imaging reporting and data system (Lung-RADS) for risk stratification of pure ground-glass nodules (pGGNs) in low-dose computed tomography (LDCT) for lung cancer (LC) screenings in China. Patients and Methods Eight subjects with nine pGGNs originating from a Cancer Screening Program were enrolled as training set and 32 asymptomatic subjects with 35 pGGNs were selected as validation set from November 2013 to October 2018. The complementary Lung-RADS categories were set based on the GGN-vessel relationship (GVR). The correlations between GGN-vessel relationships and pathology were evaluated, and the diagnostic value of complementary Lung-RADS version 1.1 in discriminating malignant pGGNs were analyzed. Results The inter-reader agreements for Lung-RADS 1.1 (intraclass correlation coefficient (ICC= 0.999) and complementary Lung-RADS 1.1 (ICC= 0.971) displayed good reliability. The combined incidence of invasive adenocarcinoma in type III and IV was more than that of benign and preinvasive diseases (30% vs 75%, P=0.013). Type II GVR between two benign (66.7%), seven preinvasive (53.8%), and six invasive (21.4%) GGN cases was statistically significant (χ2=5.415, P=0.019). GGN pathological groups and GVR had a significant correlation (r=0.584, P=0.00). Compared to Lung-RADS 1.1, complementary Lung-RADS 1.1 had better performance in the training set, with its sensitivity increased from 33.3% to 88.9%, accuracy increased from 44.4% to 88.9%, false-negative proportion (FNP) decreased from 66.7% to 11.1%, and the sensitivity to predict malignant nodules increased from 13.8% to 93.1%, accuracy increased from 28.6% to 80.0%, and FNP decreased from 86.2% to 6.9% in validation set. The detection rate of preinvasive disease and adenocarcinoma was increased from 12.5% to 90.6% and that of missed diagnosis decreased from 87.5% to 9.4% in the validation set, P=0.004. Conclusion Complementary Lung-RADS 1.1 is superior to Lung-RADS 1.1 and would be beneficial for LC screening of LDCT in China.
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Affiliation(s)
- Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Pengfei Ren
- Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Pengrui Gao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xinmin Dou
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xuejun Chen
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Lanwei Guo
- Department of Cancer Prevention Office, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yongping Song
- Department of Hematology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
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Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules. Sci Rep 2019; 9:6009. [PMID: 30979926 PMCID: PMC6461662 DOI: 10.1038/s41598-019-42340-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 03/27/2019] [Indexed: 02/07/2023] Open
Abstract
109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.
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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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