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Jiang J, Lv FJ, Tao Y, Fu BJ, Li WJ, Lin RY, Chu ZG. Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT. Insights Imaging 2023; 14:146. [PMID: 37697104 PMCID: PMC10495292 DOI: 10.1186/s13244-023-01504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs. RESULTS From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001). CONCLUSION In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant. CRITICAL RELEVANCE STATEMENT The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection. KEY POINTS • The solid pleura-attached nodules (SPANs) are closely related to the pleura. • Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. • Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. • Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. • Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.
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Affiliation(s)
- Jin Jiang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yang Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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du Plessis T, Ramkilawon G, Rae WID, Botha T, Martinson NA, Dixon SAP, Kyme A, Sathekge MM. Introducing a secondary segmentation to construct a radiomics model for pulmonary tuberculosis cavities. LA RADIOLOGIA MEDICA 2023; 128:1093-1102. [PMID: 37474665 PMCID: PMC10474191 DOI: 10.1007/s11547-023-01681-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE Accurate segmentation (separating diseased portions of the lung from normal appearing lung) is a challenge in radiomic studies of non-neoplastic diseases, such as pulmonary tuberculosis (PTB). In this study, we developed a segmentation method, applicable to chest X-rays (CXR), that can eliminate the need for precise disease delineation, and that is effective for constructing radiomic models for automatic PTB cavity classification. METHODS This retrospective study used a dataset of 266 posteroanterior CXR of patients diagnosed with laboratory confirmed PTB. The lungs were segmented using a U-net-based in-house automatic segmentation model. A secondary segmentation was developed using a sliding window, superimposed on the primary lung segmentation. Pyradiomics was used for feature extraction from every window which increased the dimensionality of the data, but this allowed us to accurately capture the spread of the features across the lung. Two separate measures (standard-deviation and variance) were used to consolidate the features. Pearson's correlation analysis (with a 0.8 cut-off value) was then applied for dimensionality reduction followed by the construction of Random Forest radiomic models. RESULTS Two almost identical radiomic signatures consisting of 10 texture features each (9 were the same plus 1 other feature) were identified using the two separate consolidation measures. Two well performing random forest models were constructed from these signatures. The standard-deviation model (AUC = 0.9444 (95% CI, 0.8762; 0.9814)) performed marginally better than the variance model (AUC = 0.9288 (95% CI, 0.9046; 0.9843)). CONCLUSION The introduction of the secondary sliding window segmentation on CXR could eliminate the need for disease delineation in pulmonary radiomic studies, and it could improve the accuracy of CXR reporting currently regaining prominence as a high-volume screening tool as the developed radiomic models correctly classify cavities from normal CXR.
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Affiliation(s)
- Tamarisk du Plessis
- Department of Nuclear Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
| | - Gopika Ramkilawon
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
| | | | - Tanita Botha
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
| | - Neil Alexander Martinson
- Perinatal HIV Research Unit (PHRU), University of the Witwatersrand, Johannesburg, South Africa
- Johns Hopkins University Centre for TB Research, Baltimore, MD, USA
| | | | - Andre Kyme
- School of Biomedical Engineering, University of Sydney, Sydney, Australia
| | - Mike Michael Sathekge
- Department of Nuclear Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
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Abuladze LR, Blokhin IA, Gonchar AP, Suchilova MM, Vladzymyrskyy AV, Gombolevskiy VA, Balanyuk EA, Ni OG, Troshchansky DV, Reshetnikov RV. CT imaging of HIV-associated pulmonary disorders in COVID-19 pandemic. Clin Imaging 2023; 95:97-106. [PMID: 36706642 PMCID: PMC9846904 DOI: 10.1016/j.clinimag.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 11/30/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023]
Affiliation(s)
- Liya R. Abuladze
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation,The Vishnevsky Nаtionаl Mediсаl Reseаrсh Сenter of Surgery, 117997 Mosсow, Bol. Serpukhovskаyа str., 27, Russian Federation,Corresponding author at: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation
| | - Ivan A. Blokhin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation
| | - Anna P. Gonchar
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation
| | - Maria M. Suchilova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation
| | - Anton V. Vladzymyrskyy
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation,I.M. Sechenov First Moscow State Medical University (Sechenov University), 8, Trubetskaya str. 2, 119991 Moscow, Russian Federation
| | - Victor A. Gombolevskiy
- Artificial Intelligence Research Institute (AIRI), 121170, Kutuzovsky pr. 32, 1, Moscow, Russian Federation
| | - Eleonora A. Balanyuk
- Clinic of Aesthetic Medicine “Olymp Clinic”, 129090, 7, Sadovaya-Sukharevskaya str.1, Moscow, Russian Federation
| | - Oksana G. Ni
- City Clinical Hospital №40, Moscow Health Care Department, 8 Sosensky stan, Kommunarka settlement, 129301 Moscow, Russian Federation
| | - Dmitry V. Troshchansky
- City Clinical Hospital №40, Moscow Health Care Department, 8 Sosensky stan, Kommunarka settlement, 129301 Moscow, Russian Federation
| | - Roman V. Reshetnikov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, 24, Petrovka str. 1, Russian Federation
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da Costa APL, Cardoso FJB, Molfetta FAD. An in silico molecular modeling approach of halolactone derivatives as potential inhibitors for human immunodeficiency virus type-1 reverse transcriptase enzyme. J Biomol Struct Dyn 2023; 41:1715-1729. [PMID: 34996334 DOI: 10.1080/07391102.2021.2024256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Acquired Immune Deficiency Syndrome (AIDS) is an infectious disease caused by Human Immunodeficiency Virus (HIV) infection and its replication requires the Reverse Transcriptase (RT) enzyme. RT plays a key role in the HIV life cycle, making it one of the most important targets for designing new drugs. Thus, in order to increase therapeutic options against AIDS, halolactone derivatives (D-halolactone) that have been showed as potential non-nucleoside inhibitors of the RT enzyme were studied. In the present work, a series of D-halolactone were investigated by molecular modeling studies, combining Three-dimensional Quantitative Structure-Activity Relationship (3 D-QSAR), molecular docking and Molecular Dynamics (MD) techniques, to understand the molecular characteristics that promote biological activity. The internal and external validation parameters indicated that the 3 D-QSAR model has good predictive capacity and statistical significance. Contour maps provided useful information on the structural characteristics of compounds for anti-HIV-1 activity. The docking results showed that D-halolactone present good complementarity by the RT allosteric site. In MD simulations it was observed that the formation of enzyme-ligand complexes were favorable, and from the free energy decomposition it was found that Leu100, Val106, Tyr181, Try188, and Trp229 are key residues for stabilization in the enzymatic site. Thus, the results showed that the proposed models can be used to design promising HIV-1 RT inhibitors. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ana Paula Lima da Costa
- Laboratório de Modelagem Molecular, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Pará, Brazil
| | - Fábio José Bonfim Cardoso
- Laboratório de Modelagem Molecular, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Pará, Brazil
| | - Fábio Alberto de Molfetta
- Laboratório de Modelagem Molecular, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Pará, Brazil
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Nishiyama A, Kawata N, Yokota H, Hayano K, Matsuoka S, Shigeta A, Sugiura T, Tanabe N, Ishida K, Tatsumi K, Suzuki T, Uno T. Heterogeneity of Lung Density in Patients With Chronic Thromboembolic Pulmonary Hypertension (CTEPH). Acad Radiol 2022; 29:e229-e239. [PMID: 35466051 DOI: 10.1016/j.acra.2022.03.002] [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: 11/05/2021] [Revised: 02/21/2022] [Accepted: 03/01/2022] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES Pulmonary endarterectomy (PEA) is one of the most effective treatments for chronic thromboembolic pulmonary hypertension (CTEPH). Right heart catheterization (RHC) is the gold standard assessment for pulmonary circulatory dynamics. However, computed tomography (CT) is less invasive than RHC and can elucidate some of the morphological changes caused by thromboembolism. We hypothesized that CT could facilitate the evaluation of heterogeneous pulmonary perfusion. This study investigated whether CT imaging features reflect the disease severity and changes in pulmonary circulatory dynamics in patients with CTEPH before and after PEA. MATERIALS AND METHODS This retrospective study included 58 patients with CTEPH who underwent PEA. Pre-PEA and post-PEA CT images were assessed for heterogeneity using CT texture analysis (CTTA). The CT parameters were compared with the results of the RHC and other clinical indices and analyzed with receiver operating characteristic curves analysis for patients with and without residual pulmonary hypertension (PH) (post-PEA mean pulmonary artery pressure ≥ 25 mmHg). RESULTS CT measurements reflecting heterogeneity were significantly correlated with mean pulmonary artery pressure. Kurtosis, skewness, and uniformity were significantly lower, and entropy was significantly higher in patients with residual PH than patients without residual PH. Area under the curve values of pre-PEA and post-PEA entropy between patients with and without residual PH were 0.71 (95% confidence interval 0.57-0.84) and 0.75 (0.63-0.88), respectively. CONCLUSION Heterogeneity of lung density might reflect pulmonary circulatory dynamics, and CTTA for heterogeneity could be a less invasive technique for evaluation of changes in pulmonary circulatory dynamics in patients with CTEPH undergoing PEA.
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Affiliation(s)
- Akira Nishiyama
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan.
| | - Naoko Kawata
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Hajime Yokota
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Koichi Hayano
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Shin Matsuoka
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Ayako Shigeta
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Toshihiko Sugiura
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Nobuhiko Tanabe
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Keiichi Ishida
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Koichiro Tatsumi
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Takuji Suzuki
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Takashi Uno
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
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Jiang J, Lv ZM, Lv FJ, Fu BJ, Liang ZR, Chu ZG. Clinical and Computed Tomography Characteristics of Solitary Pulmonary Nodules Caused by Fungi: A Comparative Study. Infect Drug Resist 2022; 15:6019-6028. [PMID: 36267266 PMCID: PMC9576936 DOI: 10.2147/idr.s382289] [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: 07/25/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022] Open
Abstract
Purpose To clarify the clinical and computed tomography (CT) indicators in distinguishing pulmonary nodules caused by fungal infection from lung cancers. Methods From January 2013 to April 2022, 68 patients with solitary fungal nodules (64 were solid and 4 were mixed ground-glass nodules) and 140 cases with solid cancerous nodules with similar size were enrolled. Their clinical characteristics and CT manifestations of the solid nodules were summarized and compared, respectively. Results Compared with patients with lung cancers, cases were younger (51.2 ± 11.5 vs 61.3 ± 10.2 years) and non-smokers (72.1% vs 57.9%) and immunocompromised (44.1% vs 17.9%) individuals were more common in patients with fungal nodules (each P < 0.05). The air crescent sign (ACS) (34.4% vs 0%), halo sign (HS) (23.4% vs 4.3%), and satellite lesions (45.3% vs 2.9%) were more frequently detected in fungal nodules than in cancerous ones (each P < 0.05). Air bronchogram similarly occurred in fungal and cancerous nodules, whereas the natural ones were more common in the former (100% vs 16.7%, P = 0.000). However, the fungal nodules had a lower enhancement degree (29.0 ± 19.2 HU vs 40.3 ± 28.3 HU, P = 0.038) and frequency of hilar and/or mediastinal lymph node enlargement (2.9% vs 14.3%, P = 0.013) compared with the cancerous nodules. Conclusion In the younger, non-smoking and immunocompromised patients, a solitary pulmonary solid nodule with ACS, HS, satellite lesions and/or natural air bronchogram but without significant enhancement, fungal infection is a probable diagnosis.
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Affiliation(s)
- Jin Jiang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhuo-ma Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Department of Radiology, The Second People’s Hospital of Yubei District, Chongqing, People’s Republic of China
| | - Fa-jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Bin-jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhang-rui Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Correspondence: Zhi-gang Chu, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, 400016, People’s Republic of China, Tel +86 18723032809, Fax +86 23 68811487, Email
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Zhao W, Xiong Z, Tian D, Wang K, Zhao M, Lu X, Qin D, Li Z. The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses. Front Public Health 2022; 10:1018527. [PMID: 36267999 PMCID: PMC9577178 DOI: 10.3389/fpubh.2022.1018527] [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: 08/13/2022] [Accepted: 09/20/2022] [Indexed: 01/28/2023] Open
Abstract
Purpose To compare the value of contrast-enhanced CT (CECT) and non-contrast-enhanced CT (NCECT) radiomics models in differentiating tuberculosis (TB) from non-tuberculous infectious lesions (NTIL) presenting as solid pulmonary nodules or masses, and develop a combine radiomics model (RM). Materials and methods This study was a retrospective analysis of 101 lesions in 95 patients, including 49 lesions (from 45 patients) in the TB group and 52 lesions (from 50 patients) in the NTIL group. Lesions were randomly divided into training and test sets in the ratio of 7:3. Conventional imaging features were used to construct a conventional imaging model (IM). Radiomics features screening and NCECT or CECT RM construction were carried out by correlation analysis and gradient boosting decision tree, and logistic regression. Finally, conventional IM, NCECT RM, and CECT RM were used for combine RM construction. Additionally, we recruited three radiologists for independent diagnosis. The differential diagnostic performance of each model was assessed using the areas under the receiver operating characteristic curve (AUCs). Results The CECT RM (training AUC, 0.874; test AUC, 0.796) outperformed the conventional IM (training AUC, 0.792; test AUC, 0.708), the NCECT RM (training AUC, 0.835; test AUC, 0.704), and three radiologists. The diagnostic efficacy of the combine RM (training AUC, 0.922; test AUC, 0.833) was best in the training and test sets. Conclusions The diagnostic efficacy of the CECT RM was superior to that of the NCECT RM in identifying TB from NTIL presenting as solid pulmonary nodules or masses. The combine RM had the best performance and may outperform expert radiologists.
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Affiliation(s)
- Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Di Tian
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Kunpeng Wang
- Department of Radiology, Dalian Public Health Clinical Center, Dalian, China
| | | | - Xiwei Lu
- Department of Tuberculosis, Dalian Public Health Clinical Center, Dalian, China
| | - Dongxue Qin
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China,*Correspondence: Dongxue Qin
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China,Zhiyong Li
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8
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Zhang Y, Chu Z, Yu J, Chen X, Liu J, Xu J, Huang C, Peng L. Computed tomography-based radiomics for identifying pulmonary cryptococcosis mimicking lung cancer. Med Phys 2022; 49:5943-5952. [PMID: 35678964 DOI: 10.1002/mp.15789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 04/25/2022] [Accepted: 05/30/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Pulmonary cryptococcosis (PC) is an invasive pulmonary fungal disease, and nodule/mass-type PC may mimic lung cancer (LC) in imaging appearance. Thus, an accurate diagnosis of nodule/mass-type PC is beneficial for appropriate management. However, the differentiation of nodule/mass-type PC from LC through computed tomography (CT) is still challenging. PURPOSE To develop and externally test a CT-based radiomics model for differentiating nodule/mass-type PC from LC. METHODS In this retrospective study, patients with nodule/mass-type PC or LC who underwent non-enhanced chest CT were included: institution 1 was for the training set, and institutions 2 and 3 were for the external test set. Large quantities of radiomics features were extracted. The radiomics score (Rad-score) was calculated using the linear discriminant analysis, and a subsequent 5-fold cross-validation was performed. A combined model was developed by incorporating Rad-score and clinical factors. Finally, the models were tested with an external test set and compared using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 168 patients (45 with PC and 123 with LC) were in the training set, and 72 (36 with PC and 36 with LC) were in the external test set. Of the 81 patients with PC, 30 were immunocompromised (37%). Rad-score, comprised of 18 features, had an AUC of 0.844 after 5-fold cross-validation, which was lower than that (AUC = 0.943, P = 0.003) of the combined model integrating Rad-score, age, lobulation, pleural retraction, and patches. In the external test set, Rad-score and the combined model obtained good predictive performance (AUC = 0.824 for Rad-score, and 0.869 for the combined model). Moreover, the combined model outperformed the clinical model in the cross-validation and external test (0.943 vs. 0.810, P <0.001; 0.869 vs. 0.769, P = 0.011). CONCLUSIONS The proposed combined model exhibits a good differential diagnostic performance between nodule/mass-type PC and LC. The CT-based radiomics analysis has the potential to serve as an effective tool for the differentiation of nodule/mass-type PC from LC in clinical practice. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yongchang Zhang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, China.,Department of Radiology, Chengdu Seventh People's Hospital, Chengdu, Sichuan Province, 610213, China
| | - Zhigang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jianqun Yu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, China
| | - Xiaoyi Chen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, China
| | - Jing Liu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, China
| | - Liqing Peng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, China
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9
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Ge X, Wang L, Pan L, Ye H, Zhu X, Feng Q, Ding Z. Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning. Front Neurol 2022; 13:862973. [PMID: 35463121 PMCID: PMC9024101 DOI: 10.3389/fneur.2022.862973] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/09/2022] [Indexed: 01/01/2023] Open
Abstract
Purpose Neurovascular compression (NVC) is considered as the main factor leading to the classical trigeminal neuralgia (CTN), and a part of idiopathic TN (ITN) may be caused by NVC (ITN-nvc). This study aimed to explore the risk factors for unilateral CTN or ITN-nvc (UC-ITN), which have bilateral NVC, using machine learning (ML). Methods A total of 89 patients with UC-ITN were recruited prospectively. According to whether there was NVC on the unaffected side, patients with UC-ITN were divided into two groups. All patients underwent a magnetic resonance imaging (MRI) scan. The bilateral cisternal segment of the trigeminal nerve was manually delineated, which avoided the offending vessel (Ofv), and the features were extracted. Dimensionality reduction, feature selection, model construction, and model evaluation were performed step-by-step. Results Four textural features with greater weight were selected in patients with UC-ITN without NVC on the unaffected side. For UC-ITN patients with NVC on the unaffected side, six textural features with greater weight were selected. The textural features (rad_score) showed significant differences between the affected and unaffected sides (p < 0.05). The nomogram model had optimal diagnostic power, and the area under the curve (AUC) in the training and validation cohorts was 0.76 and 0.77, respectively. The Ofv and rad_score were the risk factors for UC-ITN according to nomogram. Conclusion Besides NVC, the texture features of trigeminal-nerve cisternal segment and Ofv were also the risk factors for UC-ITN. These findings provided a basis for further exploration of the microscopic etiology of UC-ITN.
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Affiliation(s)
- Xiuhong Ge
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Lei Pan
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haiqi Ye
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaofen Zhu
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Feng
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhongxiang Ding orcid.org/0000-0001-7691-5571
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10
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Pulmonary tuberculosis diagnosis, differentiation and disease management: A review of radiomics applications. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2021. [DOI: 10.2478/pjmpe-2021-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.
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Tabatabaei M, Tasorian B, Goyal M, Moini A, Sotoudeh H. Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept. IRANIAN JOURNAL OF MEDICAL SCIENCES 2021; 46:420-427. [PMID: 34840382 PMCID: PMC8611216 DOI: 10.30476/ijms.2021.88036.1858] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/09/2021] [Accepted: 01/20/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task. METHODS In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest. RESULTS The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%). CONCLUSION In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.
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Affiliation(s)
- Mohsen Tabatabaei
- Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran
| | - Baharak Tasorian
- Internal Medicine Department, Arak University of Medical Sciences, Arak, Iran
| | - Manu Goyal
- Postdoctoral Research Associate in Medical Imaging at Dartmouth College, Hanover, NH 03755 USA
| | - Abdollatif Moini
- Department of Internal Medicine of Amir Al Momenin Hospital, Arak University of Medical Sciences, Arak, Iran
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Ouyang J, Yuan J, Chen Y, Zeng Y, Harypursat V, Lu Y, Chen H, Chen Y. The development and validation of a diagnostic scoring system to differentiate pulmonary tuberculosis from non-tuberculosis pulmonary infections in HIV-infected patients with severe immune suppression. BMC Infect Dis 2021; 21:863. [PMID: 34425768 PMCID: PMC8383349 DOI: 10.1186/s12879-021-06552-3] [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: 01/03/2021] [Accepted: 08/09/2021] [Indexed: 11/10/2022] Open
Abstract
Background It remains challenging to differentiate tuberculosis (TB) from non-TB pulmonary infections in HIV-infected patients. Herein, we developed a scoring system aimed to rapidly determine the likelihood of TB or non-TB pathology in HIV-infected patients presenting with pulmonary infections. Methods We collected and collated data of hospitalized HIV-infected patients with pulmonary infections, followed by univariate and multivariate data analyses to determine risk variables that were significantly different between HIV/TB patients and HIV/non-TB patients. Subsequently, a regression coefficient was calculated for each variable, and a score was assigned to each variable in line with its regression coefficient. The sum of the scores for each variable in our scoring model was used to predict the likelihood of TB or non-TB pulmonary infection in each patient. Finally, we tested the diagnostic accuracy of the scoring system in our retrospective cohort, as well as in a prospective cohort. Results A total of 598 HIV-infected patients were enrolled in our retrospective cohort, among whom 288 had TB and 310 had non-TB pulmonary infections. Eight variables, including fever, highest body temperature, erythrocyte sedimentation rate (ESR), cervical lymphadenopathy, hilar and/or mediastinum lymphadenopathy, pulmonary cavitation, pleural effusion, and miliary nodules, were found to be mathematically significantly different via univariate analysis and multivariate logistic regression analysis. After regression coefficient calculation followed by score assignment, a receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to be 0.902. When the total score for a patient is > 12, the sensitivity and specificity for TB prediction using our scoring system were 76.4% and 87.7% respectively in the retrospective cohort, and its diagnostic accuracy was 82.7% in the prospective cohort. Conclusions Our results demonstrate that our proposed diagnostic scoring system could be helpful in differentiating pulmonary TB from non-TB pulmonary infections in HIV-infected patients.
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Affiliation(s)
- Jing Ouyang
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China
| | - Jing Yuan
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China.,Division of Infectious Diseases, Chongqing Public Health Medical Center, Chongqing, China
| | - Yaling Chen
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China
| | - Yanming Zeng
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China
| | - Vijay Harypursat
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China
| | - Yanqiu Lu
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, No. 10 Youanmenwai Road, Fengtai, Beijing, 100069, China.
| | - Yaokai Chen
- Clinical Research Center, Chongqing Public Health Medical Center, Chongqing, China. .,Division of Infectious Diseases, Chongqing Public Health Medical Center, Chongqing, China.
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13
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Li Y, Wei D, Chen J, Cao S, Zhou H, Zhu Y, Wu J, Lan L, Sun W, Qian T, Ma K, Xu H, Zheng Y. Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank. IEEE J Biomed Health Inform 2020; 24:2787-2797. [PMID: 32816680 PMCID: PMC8545166 DOI: 10.1109/jbhi.2020.3018181] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/20/2020] [Accepted: 08/15/2020] [Indexed: 11/08/2022]
Abstract
Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels ('difficulty' and 'diversity') are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data 'values' of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time.
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Affiliation(s)
| | - Dong Wei
- Tencent Jarvis LabShenzhen518000China
| | | | | | | | | | | | - Lan Lan
- Department of RadiologyZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Wenbo Sun
- Department of RadiologyZhongnan Hospital of Wuhan UniversityWuhan430071China
| | | | - Kai Ma
- Tencent Jarvis LabShenzhen518000China
| | - Haibo Xu
- Department of RadiologyZhongnan Hospital of Wuhan UniversityWuhan430071China
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A radiomics-clinical nomogram for preoperative prediction of IDH1 mutation in primary glioblastoma multiforme. Clin Radiol 2020; 75:963.e7-963.e15. [PMID: 32921406 DOI: 10.1016/j.crad.2020.07.036] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/31/2020] [Indexed: 02/08/2023]
Abstract
AIM To develop and validate an individualised radiomics-clinical nomogram for the prediction of the isocitrate dehydrogenase 1 (IDH1) mutation status in primary glioblastoma multiforme (GBM) based on radiomics features and clinical variables. MATERIALS AND METHODS In a retrospective study, preoperative magnetic resonance imaging (MRI) images were obtained of 122 patients with primary glioblastoma (development cohort = 101; validation cohort = 21). Radiomics features were extracted from total tumour based on the post-contrast high-resolution three-dimensional (3D) T1-weighted MRI images. Radiomics features were selected by using a least absolute shrinkage and selection operator (LASSO) binomial regression model with nested cross-validation. Then, a radiomics-clinical nomogram was constructed by combining relevant radiomics features and clinical variables and subsequently tested by using the independent validation cohort. RESULTS A total of 105 features were quantified on the 3D MRI images of each patient, and eight were selected to construct the radiomics model for predicting IDH1 mutation status. The mean classification accuracy and mean κ value achieved with the model were 88.4±3% and 0.701±0.08, respectively. The radiomics-clinical nomogram, which combines eight radiomics features and three clinical variables (patient age, sex and tumour location), demonstrated good discrimination (C-index 0.934 [95% CI, 0.874 to 0.994]; F1 score 0.78) and performed well with the validation cohort (C-index 0.963 [95% CI, 0.957 to 0.969]; F1 score 0.91; AUC 0.956). CONCLUSIONS A radiomics-clinical nomogram was developed and proved to be valuable in the non-invasive, individualised prediction of the IDH1 mutation status in patients with primary GBM. The nomogram can be applied using clinical conditions to facilitate preoperative patient evaluation.
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Prayer F, Hofmanninger J, Weber M, Kifjak D, Willenpart A, Pan J, Röhrich S, Langs G, Prosch H. Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest study. Methods 2020; 188:98-104. [PMID: 32891727 DOI: 10.1016/j.ymeth.2020.08.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES To investigate the intra- and inter-scanner repeatability and reproducibility of CT radiomics features (RF) of fibrosing interstitial lung disease (fILD). METHODS For this prospective, IRB-approved test-retest study, CT data of sixty fILD patients were acquired. Group A (n = 30) underwent one repeated CT scan on a single scanner. Group B (n = 30) was scanned using two different CT scanners. All CT data were reconstructed using different reconstruction kernels (soft, intermediate, sharp) and slice thicknesses (one and three millimeters), resulting in twelve datasets per patient. Following ROI placement in fibrotic lung tissue, 86 RF were extracted. Intra- and inter-scanner RF repeatability and reproducibility were assessed by calculating intraclass correlation coefficients (ICCs) for corresponding kernels and slice thicknesses, and between lung-specific and non-lung-specific reconstruction parameters. Furthermore, test-retest lung volumes were compared. RESULTS Test-retest demonstrated a majority of RF is highly repeatable for all reconstruction parameter combinations. Intra-scanner reproducibility was negatively affected by reconstruction kernel changes, and further reduced by slice thickness alterations. Inter-scanner reproducibility was highly variable, reconstruction parameter-specific, and greatest if either soft kernels and three-millimeter slice thickness, or lung-specific reconstruction parameters were used for both scans. Test-retest lung volumes showed no significant difference. CONCLUSION CT RF of fILD are highly repeatable for constant reconstruction parameters in a single scanner. Intra- and inter-scanner reproducibility are severely impacted by alterations in slice thickness more than reconstruction kernel, and are reconstruction parameter-specific. These findings may facilitate CT data and RF selection and assessment in future fILD radiomics studies collecting data across scanners.
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Affiliation(s)
- Florian Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Johannes Hofmanninger
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daria Kifjak
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexander Willenpart
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jeanny Pan
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sebastian Röhrich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Röhrich S, Hofmanninger J, Prayer F, Müller H, Prosch H, Langs G. Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT. Radiol Cardiothorac Imaging 2020; 2:e190190. [PMID: 33778599 DOI: 10.1148/ryct.2020190190] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 03/10/2020] [Accepted: 04/21/2020] [Indexed: 02/06/2023]
Abstract
Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Kay in this issue.
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Affiliation(s)
- Sebastian Röhrich
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Johannes Hofmanninger
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Florian Prayer
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Henning Müller
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Helmut Prosch
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Georg Langs
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
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Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol 2020; 13:100820. [PMID: 32622312 PMCID: PMC7334418 DOI: 10.1016/j.tranon.2020.100820] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022] Open
Abstract
To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.
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Affiliation(s)
- Yaoyao Zhuo
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China
| | - Mingxiang Feng
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
| | - Shuyi Yang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China
| | - Lingxiao Zhou
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China.
| | - Di Ge
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
| | - Shaohua Lu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Lei Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China.
| | - Zhiyong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China; Headmaster's Office, Fudan University, Shanghai 200433, China; Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
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Feng B, Chen X, Chen Y, Liu K, Li K, Liu X, Yao N, Li Z, Li R, Zhang C, Ji J, Long W. Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule. Eur J Radiol 2020; 128:109022. [PMID: 32371184 DOI: 10.1016/j.ejrad.2020.109022] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 03/05/2020] [Accepted: 04/13/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). METHOD We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features. We used the least absolute shrinkage and selection operator (LASSO) logistic regression to build a radiomics signature. A clinical model was developed with clinical factors, including age, gender, and CT-based subjective findings (eg, lesion size, lesion location, lesion margin, lobulated sharp, and spiculation sign). We constructed individualized radiomics nomograms incorporating the radiomics signature and clinical factors to validate the diagnostic ability. RESULTS Three factors - radiomics signature, age, and spiculation sign - were found to be independent predictors and were used to build the radiomics nomogram, which showed better diagnostic accuracy than any single model (all net reclassification improvement p < 0.05). The area under curve yielded was 0.9660 (95% confidence interval [CI], 0.9390-0.9931), 0.9342 (95% CI, 0.8944-0.9739), and 0.9064 (95% CI, 0.8639-0.9490) for the training, internal validation, and external validation cohorts, respectively. Decision curve analysis (DCA) and stratification analysis showed the nomogram has potential for generalizability. CONCLUSION The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.
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Affiliation(s)
- Bao Feng
- The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China; School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China
| | - Xiangmeng Chen
- The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China
| | - Yehang Chen
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China
| | - Kunfeng Liu
- The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Kunwei Li
- The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Xueguo Liu
- The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Nan Yao
- The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China
| | - Zhi Li
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China
| | - Ronggang Li
- The Department of Pathology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China
| | - Chaotong Zhang
- The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China
| | - Jianbo Ji
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China
| | - Wansheng Long
- The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China.
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Frega S, Ferro A, Bonanno L, Guarneri V, Conte P, Pasello G. Lung Cancer (LC) in HIV Positive Patients: Pathogenic Features and Implications for Treatment. Int J Mol Sci 2020; 21:E1601. [PMID: 32111093 PMCID: PMC7084664 DOI: 10.3390/ijms21051601] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Revised: 02/12/2020] [Accepted: 02/21/2020] [Indexed: 12/23/2022] Open
Abstract
: The human immunodeficiency virus (HIV) infection continues to be a social and public health problem. Thanks to more and more effective antiretroviral therapy (ART), nowadays HIV-positive patients live longer, thus increasing their probability to acquire other diseases, malignancies primarily. Senescence along with immune-system impairment, HIV-related habits and other oncogenic virus co-infections increase the cancer risk of people living with HIV (PLWH); in the next future non-AIDS-defining cancers will prevail, lung cancer (LC) in particular. Tumor in PLWH might own peculiar predictive and/or prognostic features, and antineoplastic agents' activity might be subverted by drug-drug interactions (DDIs) due to concurrent ART. Moreover, PLWH immune properties and comorbidities might influence both the response and tolerability of oncologic treatments. The therapeutic algorithm of LC, rapidly and continuously changed in the last years, should be fitted in the context of a special patient population like PLWH. This is quite challenging, also because HIV-positive patients have been often excluded from participation to clinical trials, so that levels of evidence about systemic treatments are lower than evidence in HIV-uninfected individuals. With this review, we depicted the epidemiology, pathogenesis, clinical-pathological characteristics and implications for LC care in PLWH, offering a valid focus about this topic to clinicians.
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Affiliation(s)
- Stefano Frega
- Medical Oncology 2, Istituto Oncologico Veneto IOV- IRCCS, 35, 128 Padova, Italy; (S.F.); (A.F.); (L.B.); (V.G.); (P.C.)
| | - Alessandra Ferro
- Medical Oncology 2, Istituto Oncologico Veneto IOV- IRCCS, 35, 128 Padova, Italy; (S.F.); (A.F.); (L.B.); (V.G.); (P.C.)
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35, 128 Padova, Italy
| | - Laura Bonanno
- Medical Oncology 2, Istituto Oncologico Veneto IOV- IRCCS, 35, 128 Padova, Italy; (S.F.); (A.F.); (L.B.); (V.G.); (P.C.)
| | - Valentina Guarneri
- Medical Oncology 2, Istituto Oncologico Veneto IOV- IRCCS, 35, 128 Padova, Italy; (S.F.); (A.F.); (L.B.); (V.G.); (P.C.)
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35, 128 Padova, Italy
| | - PierFranco Conte
- Medical Oncology 2, Istituto Oncologico Veneto IOV- IRCCS, 35, 128 Padova, Italy; (S.F.); (A.F.); (L.B.); (V.G.); (P.C.)
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35, 128 Padova, Italy
| | - Giulia Pasello
- Medical Oncology 2, Istituto Oncologico Veneto IOV- IRCCS, 35, 128 Padova, Italy; (S.F.); (A.F.); (L.B.); (V.G.); (P.C.)
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Wang H, Ren Y, Chang Z, Liu Z. The increased recurrence rate of liver abscess caused by extended-spectrum β-lactamase-producing Klebsiella pneumoniae. Eur J Clin Microbiol Infect Dis 2020; 39:1315-1320. [PMID: 32062726 DOI: 10.1007/s10096-020-03848-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 02/10/2020] [Indexed: 12/12/2022]
Abstract
The pathogenic bacterium Klebsiella pneumoniae (KP) is the major causative agent of pyogenic liver abscess (PLA). But reports about the prognosis of KP-caused PLA (KPLA) are rare. This study aimed to ascertain the recurrence rate of KPLA after initial treatment and its contributing factors. A total of 110 patients who had first-time episodes of KPLA were included into the study. The average follow-up time was 3.65 ± 2.18 years. Twenty (18.18%) KPLA patients experienced recurrence. Those in the recurrence group had a significantly greater incidence of extended-spectrum β-lactamase (ESBL) production compared with the non-recurrence group (30.0% vs 8.89%, P = 0.018). Diabetes, biliary tract disease, and history of malignancy were not associated with recurrence (all P > 0.05). No difference in the CT characteristics of KPLA (including abscess size, location, whether multilocular, gas production of KPLA, and thrombophlebitis) was found between the two groups. Multivariate regression analysis showed that ESBL production (OR, 6.3; 95% CI, 1.02-38.59; P = 0.04) was an independent risk factor for the recurrence of KPLA. Our findings emphasize that KPLA has a high recurrence rate and ESBL production is an independent risk factor for recurrent KPLA.
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Affiliation(s)
- Hairui Wang
- Department of Radiology, Shengjing Hospital of China Medical University, NO. 36, Sanhao Street, Heping District, Shenyang, 110004, China
| | - Yue Ren
- Department of Radiology, Shengjing Hospital of China Medical University, NO. 36, Sanhao Street, Heping District, Shenyang, 110004, China
| | - Zhihui Chang
- Department of Radiology, Shengjing Hospital of China Medical University, NO. 36, Sanhao Street, Heping District, Shenyang, 110004, China.
| | - Zhaoyu Liu
- Department of Radiology, Shengjing Hospital of China Medical University, NO. 36, Sanhao Street, Heping District, Shenyang, 110004, China
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