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Notsuda H, Oshio H, Onodera K, Hirama T, Watanabe Y, Watanabe T, Suzuki T, Oishi H, Niikawa H, Saito-Koyama R, Noda M, Tominaga J, Okada Y. Morphological Predictors of Primary Lung Cancer among Part-Solid Ground-Grass Nodules on High-Resolution CT. TOHOKU J EXP MED 2024; 263:35-42. [PMID: 38355111 DOI: 10.1620/tjem.2024.j016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Recent advancements in computed tomography (CT) scanning have improved the detection rates of peripheral pulmonary nodules, including those with ground-glass opacities (GGOs). This study focuses on part-solid pure ground-glass nodules (GGNs) and aims to identify imaging predictors that can reliably differentiate primary lung cancer from nodules with other diagnoses among part-solid GGNs on high-resolution CT (HRCT). A retrospective study was conducted on 609 patients who underwent surgical treatment or observation for lung nodules. Radiological findings from pre-operative HRCT scans were reviewed and several CT imaging features of part-solid GGNs were examined for their positive predictive value to identify primary lung cancer. The proportions of the nodules with a final diagnosis of primary lung cancer were significantly higher in part-solid GGNs (91.9%) compared with solid nodules (70.3%) or pure GGNs (66.7%). Among CT imaging features of part-solid GGNs that were evaluated, consolidation-to-tumor ratio (CTR) < 0.5 (98.1%), pleural indentation (96.4%), and clear tumor border (96.7%) had high positive predictive value to identify primary lung cancer. When two imaging features were combined, the combination of CTR < 0.5 and a clear tumor border was identified to have 100% positive predictive values with a sensitivity of 40.8%. Thus we conclude that part-solid GGNs with a CTR < 0.5 accompanied by a clear tumor border evaluated by HRCT are very likely to be primary lung cancers with an acceptable sensitivity. Preoperative diagnostic procedures to obtain a pathological diagnosis may potentially be omitted in patients harboring such part-solid GGNs.
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Affiliation(s)
- Hirotsugu Notsuda
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | | | - Ken Onodera
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Takashi Hirama
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Yui Watanabe
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Tatsuaki Watanabe
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Takaya Suzuki
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Hisashi Oishi
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Hiromichi Niikawa
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Ryoko Saito-Koyama
- Department of Pathology, National Hospital Organization, Sendai Medical Center
| | - Masafumi Noda
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Junya Tominaga
- Department of Diagnostic Radiology, Tohoku University Hospital
| | - Yoshinori Okada
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
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Yang Y, Xu J, Wang W, Ma M, Huang Q, Zhou C, Zhao J, Duan Y, Luo J, Jiang J, Ye L. A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma. BMC Cancer 2024; 24:438. [PMID: 38594670 PMCID: PMC11005224 DOI: 10.1186/s12885-024-12207-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.
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Affiliation(s)
- Yantao Yang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jing Xu
- Department of Dermatology and Venereal Diseases, Yan'an Hospital of Kunming City, Kunming, China
| | - Wei Wang
- Department of Thoracic and Cardiovascular Surgery, Shiyan Taihe Hospital (Hubei University of Medicine), Hubei, Shiyan, China
| | - Mingsheng Ma
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Qiubo Huang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Chen Zhou
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jie Zhao
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Yaowu Duan
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jia Luo
- Department of Pathology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiezhi Jiang
- Department of Radiology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lianhua Ye
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China.
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Liu M, Mu J, Song F, Liu X, Jing W, Lv F. Growth characteristics of early-stage (IA) lung adenocarcinoma and its value in predicting lymph node metastasis. Cancer Imaging 2023; 23:115. [PMID: 38041175 PMCID: PMC10691089 DOI: 10.1186/s40644-023-00631-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/01/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND We aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC). METHOD We reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value. RESULTS A total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p < 0.001), consolidation increase (p = 0.019), volume increase (p = 0.020), mass increase (p = 0.021), VDTt (p = 0.002), and MDTt (p = 0.004) were independent factors for LN metastasis. The ROC curves showed that the AUC for VDTt was 0.860 (95% CI, 0.778-0.943; p < 0.001) and for MDTt was 0.848 (95% CI, 0.759-0.936; p < 0.001). CONCLUSIONS Our study showed significant differences in the growth characteristics of pSN and SN, and the application of VDT and MDT could be a valid predictor LN metastasis in patients with early-stage NSCLC.
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Affiliation(s)
- Mengxi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junhao Mu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Feipeng Song
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiangling Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weiwei Jing
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Gao R, Gao Y, Zhang J, Zhu C, Zhang Y, Yan C. A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence. J Cancer Res Clin Oncol 2023; 149:15323-15333. [PMID: 37624396 DOI: 10.1007/s00432-023-05262-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
PURPOSE To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance. METHODS 187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. RESULTS The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05). CONCLUSION For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.
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Affiliation(s)
- Rongji Gao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Yinghua Gao
- Department of Pathology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Juan Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Chunyu Zhu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Yue Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China.
| | - Chengxin Yan
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China.
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Zhu H, Zhang L, Huang Z, Chen J, Sun L, Chen Y, Huang G, Chen Q, Yu H. Lung adenocarcinoma associated with cystic airspaces: Predictive value of CT features in assessing pathologic invasiveness. Eur J Radiol 2023; 165:110947. [PMID: 37392546 DOI: 10.1016/j.ejrad.2023.110947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/10/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
OBJECTIVES Lung adenocarcinoma associated with cystic airspaces (LACA) is a unique entity with limited understanding. Our aim was to evaluate the radiological characteristics of LACA and to study which criteria were predictive of invasiveness. METHODS A retrospective monocentric analysis of consecutive patients with pathologically confirmed LACA was performed. The diagnosed adenocarcinomas were classified into preinvasive (atypical adenomatous hyperplasia, adenocarcinoma in situ, or minimally invasive adenocarcinoma) and invasive adenocarcinomas. Eight clinical features and twelve CT features were evaluated. Univariable and multivariable analyses were performed to analyse the correlation between invasiveness, and CT and clinical features. The inter-observer agreement was evaluated using κ statistics and intraclass correlation coefficients. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 252 patients with 265 lesions (128 men and 124 women; mean age, 58.0 ± 11.1 years) were enrolled. Multivariable logistic regression indicated that multiple cystic airspaces (OR, 5.599; 95 % CI, 1.865-16.802), irregular shape of cystic airspace (OR, 3.236; 95 % CI, 1.073-9.761), entire tumour size (OR, 1.281; 95 % CI, 1.075-1.526), and attenuation (OR, 1.007; 95 % CI, 1.005-1.010) were independent risk factors for invasive LACA. The AUC of the logistic regression model was 0.964 (95 % CI, 0.944-0.985). CONCLUSION Multiple cystic airspaces, irregular shape of cystic airspace, entire tumour size, and attenuation were identified as independent risk factors for invasive LACA. The prediction model gives a good predictive performance, providing additional diagnostic information.
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Affiliation(s)
- Huiyuan Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lian Zhang
- Shanghai University of Traditional Chinese Medicine, Shanghai, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China; Department of Radiology, Jiading Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Zike Huang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linlin Sun
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinan Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Huang
- Shanghai University of Traditional Chinese Medicine, Shanghai, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China; Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
| | - Qunhui Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Reticulation Sign on Thin-Section CT: Utility for Predicting Invasiveness of Pure Ground-Glass Nodules. AJR Am J Roentgenol 2023:1-10. [PMID: 37079277 DOI: 10.2214/ajr.22.28892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
BACKGROUND. Pure ground-glass nodules (pGGNs) may represent a diverse range of histologic entities of varying aggressiveness. OBJECTIVE. The purpose of this study was to evaluate the use of the reticulation sign on thin-section CT images for predicting the invasiveness of pGGNs. METHODS. This retrospective study included 795 patients (mean age, 53.4 ± 11.1 [SD] years; 254 men, 541 women) with a total of 876 pGGNs on thin-section CT that underwent resection between January 2015 and April 2022. Two fellowship-trained thoracic radiologists independently reviewed unenhanced CT images to assess the pGGNs for a range of features, including diameter, attenuation, location, shape, air bronchogram, bubble lucency, vascular change, lobulation, spiculation, margins, pleural indentation, and the reticulation sign (defined as multiple small linear opacities resembling a mesh or a net); differences were resolved by consensus. The relationship between the reticulation sign and lesion invasiveness on pathologic assessment was evaluated. RESULTS. On pathologic assessment, the 876 pGGNs included 163 nonneoplastic and 713 neoplastic pGGNs (323 atypical adenomatous hyperplasias [AAHs] or adenocarcinomas in situ [AISs], 250 minimally invasive adenocarcinomas [MIAs], and 140 invasive adenocarcinomas [IACs]). Interobserver agreement for the reticulation sign, expressed as kappa, was 0.870. The reticulation sign was detected in 0.0% of nonneoplastic lesions, 0.0% of AAHs/AISs, 6.8% of MIAs, and 54.3% of IACs. The reticulation sign had sensitivity of 24.0% and specificity of 100.0% for a diagnosis of MIA or IAC and sensitivity of 54.3% and specificity of 97.7% for a diagnosis of IAC. In multivariable regression analyses including all of the assessed CT features, the reticulation sign was a significant independent predictor of IAC (OR, 3.64; p = .001) but was not a significant independent predictor of MIA or IAC. CONCLUSION. The reticulation sign, when observed in a pGGN on thin-section CT, has high specificity (albeit low sensitivity) for invasiveness and is an independent predictor of IAC. CLINICAL IMPACT. Those pGGNs that show the reticulation sign should be strongly suspected to represent IAC; this suspicion may guide risk assessments and follow-up recommendations.
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Computed tomography of ground glass nodule image based on fuzzy C-means clustering algorithm to predict invasion of pulmonary adenocarcinoma. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs. Biosci Rep 2022; 42:230629. [PMID: 35005775 PMCID: PMC8766821 DOI: 10.1042/bsr20212416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 12/05/2022] Open
Abstract
Objective: To explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated. Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor–lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05). Conclusion: AI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.
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Sugawara H, Watanabe H, Kunimatsu A, Abe O, Watanabe SI, Yatabe Y, Kusumoto M. Adenocarcinoma in situ and minimally invasive adenocarcinoma in lungs of smokers: image feature differences from those in lungs of non-smokers. BMC Med Imaging 2021; 21:172. [PMID: 34798844 PMCID: PMC8603503 DOI: 10.1186/s12880-021-00705-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/09/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers. MATERIALS AND METHODS We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups. RESULTS The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively). CONCLUSION AIS and MIA in smoker's lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker's lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma.
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Affiliation(s)
- Haruto Sugawara
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
| | - Hirokazu Watanabe
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan
| | - Masahiko Kusumoto
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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Hu F, Huang H, Jiang Y, Feng M, Wang H, Tang M, Zhou Y, Tan X, Liu Y, Xu C, Ding N, Bai C, Hu J, Yang D, Zhang Y. Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model. J Thorac Dis 2021; 13:5383-5394. [PMID: 34659805 PMCID: PMC8482342 DOI: 10.21037/jtd-21-786] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/06/2021] [Indexed: 11/07/2022]
Abstract
Background Patients with consistent lung pure ground-glass nodules (pGGNs) have a high incidence of lung adenocarcinoma that can be classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Regular follow-up is recommended for AIS and MIA, while surgical resection should be considered for IAC. This study sought to develop a multi-parameter prediction model to increase the diagnostic accuracy in discriminating between IAC and AIS or MIA. Methods The training data set comprised consecutive patients with lung pGGNs who underwent resection from January to December 2017 at the Zhongshan Hospital. Of the 370 resected pGGNs, 344 were pathologically confirmed to be AIS, MIA, or IAC and were included in the study. The 26 benign pGGNs were excluded. We compared differences in the clinical features (e.g., age and gender), the content of serum tumor biomarkers, the computed tomography (CT) parameters (e.g., nodule size and the maximal CT value), and the morphologic characteristics of nodules (e.g., lobulation, spiculation, pleura indentation, vacuole sign, and normal vessel penetration or abnormal vessel) between the pathological subtypes of AIS, MIA, and IAC. An abnormal vessel was defined as “vessel curve” or “vessel enlargement”. Statistical analyses were performed using the chi-square test, analysis of variance (ANOVA), and rank test. The IAC prediction model was constructed via a multivariate logistical regression. Our prediction model for lung pGGNs was further validated in a data set comprising consecutive patients from multiple medical centers in China from July to December 2018. In total, 345 resected pGGNs were pathologically diagnosed as lung adenocarcinoma in the validation data set. Results In the training data set, patients with pGGNs ≥10 mm in size had a high incidence (74.5%) of IAC. The maximal CT value of IAC [–416.1±121.2 Hounsfield unit (HU)] was much higher than that of MIA (–507.7±138.0 HU) and AIS (–602.6±93.3 HU) (P<0.001). IAC was more common in pGGNs that displayed any of the following CT manifestations: lobulation, spiculation, pleura indentation, vacuole sign, and vessel abnormality. The IAC prediction model was constructed using the parameters that were assessed as risk factors (i.e., the nodule size, maximal CT value, and CT signs). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this model for diagnosing IAC was 0.910, which was higher than that of the AUC for nodule size alone (0.891) or the AUC for the maximal CT value alone (0.807) (P<0.05, respectively). A multicenter validation data set was used to validate the performance of our prediction model in diagnosing IAC, and our model was found to have an AUC of 0.883, which was higher than that of the AUC of 0.827 for the module size alone model or the AUC of 0.791 for the maximal CT value alone model (P<0.05, respectively). Conclusions Our multi-parameter prediction model was more accurate at diagnosing IAC than models that used only nodule size or the maximal CT value alone. Thus, it is an efficient tool for identifying the IAC of malignant pGGNs and deciding if surgery is needed.
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Affiliation(s)
- Fuying Hu
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital, Tianmen, China.,Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Haihua Huang
- Department of Thoracic Surgery, Shanghai General Hospital, Jiaotong University, Shanghai, China
| | - Yunyan Jiang
- Department of Pulmonary and Critical Care Medicine, People's Hospital, Yuxi, China
| | - Minxiang Feng
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hao Wang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Min Tang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yi Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xianhua Tan
- Department of Radiology, The Fifth Hospital of Wuhan, Wuhan, China
| | - Yalan Liu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ning Ding
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chunxue Bai
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jie Hu
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dawei Yang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong Zhang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
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11
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Lam S, Tammemagi M. Contemporary issues in the implementation of lung cancer screening. Eur Respir Rev 2021; 30:30/161/200288. [PMID: 34289983 DOI: 10.1183/16000617.0288-2020] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/08/2021] [Indexed: 12/24/2022] Open
Abstract
Lung cancer screening with low-dose computed tomography can reduce death from lung cancer by 20-24% in high-risk smokers. National lung cancer screening programmes have been implemented in the USA and Korea and are being implemented in Europe, Canada and other countries. Lung cancer screening is a process, not a test. It requires an organised programmatic approach to replicate the lung cancer mortality reduction and safety of pivotal clinical trials. Cost-effectiveness of a screening programme is strongly influenced by screening sensitivity and specificity, age to stop screening, integration of smoking cessation intervention for current smokers, screening uptake, nodule management and treatment costs. Appropriate management of screen-detected lung nodules has significant implications for healthcare resource utilisation and minimising harm from radiation exposure related to imaging studies, invasive procedures and clinically significant distress. This review focuses on selected contemporary issues in the path to implement a cost-effective lung cancer screening at the population level. The future impact of emerging technologies such as deep learning and biomarkers are also discussed.
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Affiliation(s)
- Stephen Lam
- British Columbia Cancer Agency, Vancouver, BC, Canada.,University of British Columbia, Vancouver, BC, Canada
| | - Martin Tammemagi
- Dept of Health Sciences, Brock University, St Catharines, ON, Canada
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12
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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: 1] [Impact Index Per Article: 0.3] [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 2020; 14:100936. [PMID: 33221688 PMCID: PMC7689413 DOI: 10.1016/j.tranon.2020.100936] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 12/17/2022] Open
Abstract
It is vital to distinguish indolent pulmonary adenocarcinomas from invasive pulmonary adenocarcinomas before surgery. Radiomics is a cutting-edge technology that mines quantitative features from CT images. We designed a nomogram, which incorporated clinical and CT morphological characteristics with the radiomics signature. We applied the radiomics nomogram to preoperatively predict the invasiveness of GGNs.
In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916–0.964) and validation set (AUC, 0.946; 95% CI, 0.907–0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Mingyang Li
- State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China
| | - Xiaoqian Lu
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Shuo Wang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
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14
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Lam S, Bryant H, Donahoe L, Domingo A, Earle C, Finley C, Gonzalez AV, Hergott C, Hung RJ, Ireland AM, Lovas M, Manos D, Mayo J, Maziak DE, McInnis M, Myers R, Nicholson E, Politis C, Schmidt H, Sekhon HS, Soprovich M, Stewart A, Tammemagi M, Taylor JL, Tsao MS, Warkentin MT, Yasufuku K. Management of screen-detected lung nodules: A Canadian partnership against cancer guidance document. CANADIAN JOURNAL OF RESPIRATORY CRITICAL CARE AND SLEEP MEDICINE 2020. [DOI: 10.1080/24745332.2020.1819175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Stephen Lam
- British Columbia Cancer Agency & the University of British Columbia, Vancouver, British Columbia, Canada
| | - Heather Bryant
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Laura Donahoe
- Division of Thoracic Surgery, Department of Surgery, University Health Network, Toronto, Ontario, Canada
| | - Ashleigh Domingo
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Craig Earle
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Christian Finley
- Department of Thoracic Surgery, St. Joseph's Healthcare, McMaster University, Hamilton, Ontario, Canada
| | - Anne V. Gonzalez
- Division of Respiratory Medicine, McGill University, Montreal, Quebec, Canada
| | - Christopher Hergott
- Division of Respiratory Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Rayjean J. Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
| | - Anne Marie Ireland
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Michael Lovas
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Daria Manos
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - John Mayo
- Department of Radiology, Vancouver Coastal Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Donna E. Maziak
- Surgical Oncology Division of Thoracic Surgery, Ottawa Hospital, Ottawa, Ontario, Canada
| | - Micheal McInnis
- Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada
| | - Renelle Myers
- British Columbia Cancer Agency & the University of British Columbia, Vancouver, British Columbia, Canada
| | - Erika Nicholson
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Christopher Politis
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Heidi Schmidt
- University Health Network and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Harman S. Sekhon
- Department of Pathology and Laboratory Medicine, University of Ottawa, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Marie Soprovich
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Archie Stewart
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Martin Tammemagi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Jana L. Taylor
- Department of Radiology, McGill University, Montreal, Quebec, Canada
| | - Ming-Sound Tsao
- Department of Laboratory Medicine and Pathobiology, University Health Network and Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Matthew T. Warkentin
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
| | - Kazuhiro Yasufuku
- Division of Thoracic Surgery, Department of Surgery and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
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