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Dong H, Wang X, Qiu Y, Lou C, Ye Y, Feng H, Ye X, Chen D. Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:10519-10530. [PMID: 37289235 DOI: 10.1007/s00432-023-04854-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/13/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT). METHODS Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram. RESULTS The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model. CONCLUSION The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.
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Affiliation(s)
- Hao Dong
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Xinbin Wang
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Yonggang Qiu
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Cuncheng Lou
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Yinfeng Ye
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Han Feng
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China
| | - Xiaodan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Institute of Medical Imaging, Shanghai, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Dihong Chen
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No. 199 Xinnan Road, Xiaoshan, Hangzhou, Zhejiang, China.
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Rea G, Sverzellati N, Bocchino M, Lieto R, Milanese G, D'Alto M, Bocchini G, Maniscalco M, Valente T, Sica G. Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology". Diagnostics (Basel) 2023; 13:2333. [PMID: 37510077 PMCID: PMC10378251 DOI: 10.3390/diagnostics13142333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.
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Affiliation(s)
- Gaetano Rea
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Nicola Sverzellati
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| | - Marialuisa Bocchino
- Department of Clinical Medicine and Surgery, Section of Respiratory Diseases, University Federico II, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Roberta Lieto
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Gianluca Milanese
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| | - Michele D'Alto
- Department of Cardiology, University "L. Vanvitelli"-Monaldi Hospital, 80131 Naples, Italy
| | - Giorgio Bocchini
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mauro Maniscalco
- Department of Pneumology Clinical and Scientific Institutes Maugeri IRCSS, 82037 Telese, Italy
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
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Murugappan M, Bourisly AK, Prakash NB, Sumithra MG, Acharya UR. Automated semantic lung segmentation in chest CT images using deep neural network. Neural Comput Appl 2023; 35:15343-15364. [PMID: 37273912 PMCID: PMC10088735 DOI: 10.1007/s00521-023-08407-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 02/13/2023] [Indexed: 06/06/2023]
Abstract
Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixel-labeled images are used to develop the deep learning model. The segmentation performance has been assessed using five performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 + network with ResNet-18 and a batch size of 8 have a higher performance for two-class segmentation. DeepLabV3 + network coupled with ResNet-50 and a batch size of 16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 + network with Xception. This present work proposes a unified DeepLabV3 + network to delineate the two and four different regions automatically using CT images for CoVID-19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis.
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Affiliation(s)
- M. Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Science, Technology, and Advanced Studies, Chennai, India
- Centre of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Ali K. Bourisly
- Department of Physiology, Kuwait University, Kuwait City, Kuwait
| | - N. B. Prakash
- Department of Electrical and Electronics and Engineering, National Engineering College, Kovilpatti, Tamil Nadu India
| | - M. G. Sumithra
- Department of Biomedical Engineering, Dr. N. G. P. Institute of Technology, Coimbatore, Tamilnadu India
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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Yong WP, Teo FS, Teo LL, Ng MC, Tan TJ, Low SY, Wong K, Ang P, Choo SP, Lee KH, Lee SC. Clinical best practices in optimal monitoring, early diagnosis, and effective management of antibody-drug conjugate-induced interstitial lung disease or pneumonitis: a multidisciplinary team approach in Singapore. Expert Opin Drug Metab Toxicol 2022; 18:805-815. [PMID: 36636012 DOI: 10.1080/17425255.2022.2162383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
INTRODUCTION Interstitial lung disease (ILD) or pneumonitis remains an important adverse event identified with treatment with antibody-drug conjugates (ADCs). Drug-induced ILD (DILD) accounts for 3%-5% of common ILD cases and is a significant problem in clinical practice. Hence, with the anticipation of the widespread use of ADCs, it will be important for guidelines and recommendations to be established to direct and standardize the management of DILD by a multidisciplinary team (MDT). AREAS COVERED A thorough literature search was conducted using PubMed to identify relevant articles related to ADCs published between 1 January 2010 and 31 November 2022. Based on the review of the literature combined with expert opinions, this review article offers an overview of incidences of ILDs associated with the use of newer anticancer therapies, specifically ADCs, and discusses local-regional best practices in optimal monitoring, early diagnosis, and management of DILD involving an MDT. EXPERT OPINION Multidisciplinary input and consensus are crucial in the accurate diagnosis of DILD. The core group of essential attendees in the MDT are oncologists, pulmonologists, thoracic radiologists, and pathologists. This allows for the integration of expertise from different specialists to achieve a 'best fit' diagnosis and management.
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Affiliation(s)
- Wei Peng Yong
- National University Cancer Institute, Singapore, National University Health System, Singapore
| | - Felicia Sw Teo
- University Medicine Cluster, Division of Respiratory and Critical Care Medicine, National University Hospital, National University Health System, Singapore.,The Lung Clinic, Mount Elizabeth Medical Centre, Singapore
| | - Lynette Ls Teo
- Department of Diagnostic Imaging, National University Hospital, National University Health System, Singapore
| | - Matthew Ch Ng
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Tira J Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Su Ying Low
- Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore
| | - Karmen Wong
- Icon Cancer Centre, Mount Alvernia Hospital, Singapore
| | - Peter Ang
- Oncocare Cancer Centre, Mount Elizabeth Novena Medical Centre, Singapore
| | - Su Pin Choo
- Curie Oncology, Mount Elizabeth Hospital, Singapore
| | - Kim Hua Lee
- Division of Oncology Nursing, National University Cancer Institute, Singapore, National University Health System, Singapore
| | - Soo Chin Lee
- National University Cancer Institute, Singapore, National University Health System, Singapore
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Laros SSA, Dieckens D, Blazis SP, van der Heide JA. Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population. EJNMMI Phys 2022; 9:66. [PMID: 36153446 PMCID: PMC9509500 DOI: 10.1186/s40658-022-00494-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND [18F] FDG PET-CT has an important role in the initial staging of lung cancer; however, accurate differentiation between activity in malignant and benign intrathoracic lymph nodes on PET-CT scans can be challenging. The purpose of the current study was to investigate the effect of incorporating primary tumour data and clinical features to differentiate between [18F] FDG-avid malignant and benign intrathoracic lymph nodes. METHODS We retrospectively selected lung cancer patients who underwent PET-CT for initial staging in two centres in the Netherlands. The primary tumour and suspected lymph node metastases were annotated and cross-referenced with pathology results. Lymph nodes were classified as malignant or benign. From the image data, we extracted radiomic features and trained the classifier model using the extreme gradient boost (XGB) algorithm. Various scenarios were defined by selecting different combinations of data input and clinical features. Data from centre 1 were used for training and validation of the models using the XGB algorithm. To determine the performance of the model in a different hospital, the XGB model was tested using data from centre 2. RESULTS Adding primary tumour data resulted in a significant gain in the performance of the trained classifier model. Adding the clinical information about distant metastases did not lead to significant improvement. The performance of the model in the test set (centre 2) was slightly but statistically significantly lower than in the validation set (centre 1). CONCLUSIONS Using the XGB algorithm potentially leads to an improved model for the classification of intrathoracic lymph nodes. The inclusion of primary tumour data improved the performance of the model, while additional knowledge of distant metastases did not. In patients in whom metastases are limited to lymph nodes in the thorax, this may reduce costly and invasive procedures such as endobronchial ultrasound or mediastinoscopy procedures.
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Affiliation(s)
- Sara S. A. Laros
- grid.413972.a0000 0004 0396 792XDepartment of Medical Physics and Engineering, Albert Schweitzer Hospital, Afdeling Klinische Fysica - Medische Techniek, Albert Schweitzerplaats 25, 3318 AT Dordrecht, The Netherlands
| | - Dennis Dieckens
- grid.413972.a0000 0004 0396 792XDepartment of Nuclear Medicine, Albert Schweitzer Hospital, Dordrecht, The Netherlands
| | - Stephan P. Blazis
- grid.413972.a0000 0004 0396 792XDepartment of Medical Physics and Engineering, Albert Schweitzer Hospital, Afdeling Klinische Fysica - Medische Techniek, Albert Schweitzerplaats 25, 3318 AT Dordrecht, The Netherlands
| | - Johannes A. van der Heide
- grid.413972.a0000 0004 0396 792XDepartment of Nuclear Medicine, Albert Schweitzer Hospital, Dordrecht, The Netherlands ,grid.413681.90000 0004 0631 9258Department of Nuclear Medicine, Diakonessenhuis Hospital, Utrecht, The Netherlands ,grid.412301.50000 0000 8653 1507Department of Nuclear Medicine, University Hospital RWTH, Aachen, Germany
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Dong H, Yin L, Lou C, Yang J, Wang X, Qiu Y. Correlation of computed tomography quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma. Medicine (Baltimore) 2022; 101:e29373. [PMID: 35758369 PMCID: PMC9276291 DOI: 10.1097/md.0000000000029373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 04/11/2022] [Indexed: 12/24/2022] Open
Abstract
The purpose of the study is to investigate the correlation of computed tomography (CT) quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma.The study involved 141 lesions in 141 patients with early lung adenocarcinoma. According to the degree of tumor invasion, the lesions were assigned into (adenocarcinoma in situ + minimally invasive adenocarcinoma) group and invasive adenocarcinoma (IAC) group. Artificial intelligence-assisted diagnostic software was used to automatically outline the lesions and extract corresponding quantitative parameters on CT images. Statistical analysis was performed to explore the correlation of these parameters with tumor invasion and Ki-67 expression.The results of logistic regression analysis showed that the short diameter of the lesion and the average CT value were independent predictors of IAC. Receiver operating characteristic curve analysis identified the average CT value as an independent predictor of IAC with the best performance, with the area under the receiver operating characteristic curve of 0.893 (P < .001), and the threshold of -450 HU. Besides, the predicted probability of logistic regression analysis model was detected to have the area under the curve of 0.931 (P < .001). The results of Spearman correlation analysis showed that the expression level of Ki-67 had the highest correlation with the average CT value of the lesion (r = 0.403, P < .001).The short diameter of the lesion and the average CT value are independent predictors of IAC, and the average CT value is significantly positively correlated with the expression of tumor Ki-67.
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Affiliation(s)
- Hao Dong
- Department of Radiology, First People's Hospital of Xiaoshan District, Hangzhou, Zhejiang, China
| | - Lekang Yin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Cuncheng Lou
- Department of Radiology, First People's Hospital of Xiaoshan District, Hangzhou, Zhejiang, China
| | - Junjie Yang
- Department of Pathology, First People's Hospital of Xiaoshan District, Hangzhou, Zhejiang, China
| | - Xinbin Wang
- Department of Radiology, First People's Hospital of Xiaoshan District, Hangzhou, Zhejiang, China
| | - Yonggang Qiu
- Department of Radiology, First People's Hospital of Xiaoshan District, Hangzhou, Zhejiang, China
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Abstract
Die Radiologie ist von stetem Wandel geprägt und definiert sich über den technologischen Fortschritt. Künstliche Intelligenz (KI) wird die praktische Tätigkeit in der Kinder- und Jugendradiologie künftig in allen Belangen verändern. Bildakquisition, Befunderkennung und -segmentierung sowie die Erkennung von Gewebeeigenschaften und deren Kombination mit Big Data werden die Haupteinsatzgebiete in der Radiologie sein. Höhere Effektivität, Beschleunigung von Untersuchung und Befundung sowie Kosteneinsparung sind mit der Anwendung von KI verbundene Erwartungshaltungen. Ein verbessertes Patientenmanagement, Arbeitserleichterungen für medizinisch-technische Radiologieassistenten und Kinder- und Jugendradiologen sowie schnellere Untersuchungs- und Befundzeiten markieren die Meilensteine der KI-Entwicklung in der Radiologie. Von der Terminkommunikation und Gerätesteuerung bis zu Therapieempfehlung und -monitoring wird der Alltag durch Elemente der KI verändert. Kinder- und Jugendradiologen müssen daher grundlegend über KI informiert sein und mit Datenwissenschaftlern bei der Etablierung und Anwendung von KI-Elementen zusammenarbeiten.
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Ye X, Fan W, Wang Z, Wang J, Wang H, Wang J, Wang C, Niu L, Fang Y, Gu S, Tian H, Liu B, Zhong L, Zhuang Y, Chi J, Sun X, Yang N, Wei Z, Li X, Li X, Li Y, Li C, Li Y, Yang X, Yang W, Yang P, Yang Z, Xiao Y, Song X, Zhang K, Chen S, Chen W, Lin Z, Lin D, Meng Z, Zhao X, Hu K, Liu C, Liu C, Gu C, Xu D, Huang Y, Huang G, Peng Z, Dong L, Jiang L, Han Y, Zeng Q, Jin Y, Lei G, Zhai B, Li H, Pan J. [Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2021; 24:305-322. [PMID: 33896152 PMCID: PMC8174112 DOI: 10.3779/j.issn.1009-3419.2021.101.14] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
局部热消融技术在肺部结节治疗领域正处在起步与发展阶段,为了肺结节热消融治疗的临床实践和规范发展,由“中国医师协会肿瘤消融治疗技术专家组”“中国医师协会介入医师分会肿瘤消融专业委员会”“中国抗癌协会肿瘤消融治疗专业委员会”“中国临床肿瘤学会消融专家委员会”组织多学科国内有关专家,讨论制定了“热消融治疗肺部亚实性结节专家共识(2021年版)”。主要内容包括:①肺部亚实性结节的临床评估;②热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症;③存在的问题和未来发展方向。
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Affiliation(s)
- Xin Ye
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Weijun Fan
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510050, China
| | - Zhongmin Wang
- Department of Interventional Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Junjie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China
| | - Hui Wang
- Interventional Center, Jilin Provincial Cancer Hospital, Changchun 170412, China
| | - Jun Wang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Chuntang Wang
- Department of Thoracic Surgery, Dezhou Second People's Hospital, Dezhou 253022, China
| | - Lizhi Niu
- Department of Oncology, Affiliated Fuda Cancer Hospital, Jinan University, Guangzhou 510665, China
| | - Yong Fang
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Shanzhi Gu
- Department of Interventional Radiology, Hunan Cancer Hospital, Changsha 410013, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Baodong Liu
- Department of Thoracic Surgery, Xuan Wu Hospital Affiliated to Capital Medical University, Beijing 100053, China
| | - Lou Zhong
- Thoracic Surgery Department, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Yiping Zhuang
- Department of Interventional Therapy, Jiangsu Cancer Hospital, Nanjing 210009, China
| | - Jiachang Chi
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Xichao Sun
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Nuo Yang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Zhigang Wei
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Xiao Li
- Department of Interventional Therapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiaoguang Li
- Minimally Invasive Tumor Therapies Center, Beijing Hospital, Beijing 100730, China
| | - Yuliang Li
- Department of Interventional Medicine, The Second Hospital of Shandong University, Jinan 250033, China
| | - Chunhai Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Yan Li
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan 250014, China
| | - Xia Yang
- Department of Oncology, Shandong Provincial Hospital Afliated to Shandong First Medical University, Jinan 250101, China
| | - Wuwei Yang
- Department of Oncology, The Fifth Medical Center, Chinese PLA General Hospital, Beijing 100071, China
| | - Po Yang
- Interventionael & Vascular Surgery, The Fourth Hospital of Harbin Medical University, Harbin 150001, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yueyong Xiao
- Department of Radiology, Chinese PLA Gneral Hospital, Beijing 100036, China
| | - Xiaoming Song
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Kaixian Zhang
- Department of Oncology, Tengzhou Central People's Hospital, Tengzhou 277500, China
| | - Shilin Chen
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Nanjing 210009, China
| | - Weisheng Chen
- Department of Thoracic Surgery, Fujian Medical University Cancer Hospital, Fujian 350011, China
| | - Zhengyu Lin
- Department of Intervention, The First Affiliated Hospital of Fujian Medical University, Fujian 350005, China
| | - Dianjie Lin
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Zhiqiang Meng
- Minimally Invasive Therapy Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Xiaojing Zhao
- Department of Thoracic Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Kaiwen Hu
- Department of Oncology, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100078, China
| | - Chen Liu
- Department of Interventional Therapy, Beijing Cancer Hospital, Beijing 100161, China
| | - Cheng Liu
- Department of Radiology, Shandong Medical Imaging Research Institute, Jinan 250021, China
| | - Chundong Gu
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Yong Huang
- Department of Imaging, Affiliated Cancer Hospital of Shandong First Medical University, Jinan 250117, China
| | - Guanghui Huang
- Department of Oncology, Shandong Provincial Hospital Afliated to Shandong First Medical University, Jinan 250101, China
| | - Zhongmin Peng
- Department of Thoracic Surgery , Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Liang Dong
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Lei Jiang
- Department of Radiology, The Convalescent Hospital of East China, Wuxi 214063, China
| | - Yue Han
- Department of Interventional Therapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Qingshi Zeng
- Department of Medical Imaging, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Yong Jin
- Interventionnal Therapy Department, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Guangyan Lei
- Department of Thoracic Surgery, Shanxi Provincial Cancer Hospital, Xi'an 710061, China
| | - Bo Zhai
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Hailiang Li
- Department of Interventional Radiology, Henan Cancer Hospital, Zhengzhou 450003, China
| | - Jie Pan
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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9
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Hurmuz P, Cengiz M, Esen CSB, Yedekci Y, Yildiz Z, Ozyigit G, Zorlu F, Akyol F. Factors affecting post-treatment radiation-induced lung disease in patients receiving stereotactic body radiotherapy to lung. RADIATION AND ENVIRONMENTAL BIOPHYSICS 2021; 60:87-92. [PMID: 33099668 DOI: 10.1007/s00411-020-00878-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 10/12/2020] [Indexed: 06/11/2023]
Abstract
The aim of the study is to investigate factors that may cause radiation-induced lung disease (RILD) in patients undergoing stereotactic body radiotherapy (SBRT) for lung tumors. Medical records of patients treated between May 2018 and June 2019 with SBRT were retrospectively evaluated. All patients should have a diagnosis of either primary non-small cell lung cancer (NSCLC) or less than three metastases to lung from another primary. The median treatment dose was 50 Gy in 4-5 fractions. Tumor response and RILD were evaluated in thoracic computer tomography (CT) using RECIST criteria. 82 patients with 97 lung lesions were treated. The median age was 68 years (IQR = 62-76). With a median follow-up of 7.2 months (3-18 months), three patients had grade 3 radiation pneumonitis (RP). RILD was observed in 52% of cases. Patients who had RILD had a higher risk of symptomatic RP (p = 0.007). In multivariate analyses older age, previous lung radiotherapy history, and median planning treatment volume (PTV) D95 value of ≥ 48 Gy were associated with RILD. Local recurrence (LR) was observed in 5.1% of cases. There was no difference in overall survival and LR with the presence of RILD. Older age, previous lung radiotherapy history, and median PTV D95 value of ≥ 48 Gy seems to be associated with post-SBRT RILD.
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Affiliation(s)
- Pervin Hurmuz
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey.
| | - Mustafa Cengiz
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey
| | | | - Yagiz Yedekci
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey
| | - Zehra Yildiz
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey
| | - Gokhan Ozyigit
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey
| | - Faruk Zorlu
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey
| | - Fadil Akyol
- Department of Radiation Oncology, Hacettepe University Faculty of Medicine, 06100, Ankara, Turkey
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10
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Joy Mathew C, David AM, Joy Mathew CM. Artificial Intelligence and its future potential in lung cancer screening. EXCLI JOURNAL 2021; 19:1552-1562. [PMID: 33408594 PMCID: PMC7783473 DOI: 10.17179/excli2020-3095] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 12/05/2020] [Indexed: 12/18/2022]
Abstract
Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist's expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.
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Affiliation(s)
- Christopher Joy Mathew
- Acute Medicine Department, Conquest Hospital, East Sussex Healthcare NHS Trust, United Kingdom
| | - Ashwini Maria David
- Jubilee Mission Medical College and Research Institute, Kerala University of Health Sciences, Kerala, India
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11
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Zhou J, Zeng ZY, Li L. Progress of Artificial Intelligence in Gynecological Malignant Tumors. Cancer Manag Res 2020; 12:12823-12840. [PMID: 33364831 PMCID: PMC7751777 DOI: 10.2147/cmar.s279990] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is a sort of new technical science which can simulate, extend and expand human intelligence by developing theories, methods and application systems. In the last five years, the application of AI in medical research has become a hot topic in modern science and technology. Gynecological malignant tumors involves a wide range of knowledge, and AI can play an important part in these aspects, such as medical image recognition, auxiliary diagnosis, drug research and development, treatment scheme formulation and other fields. The purpose of this paper is to describe the progress of AI in gynecological malignant tumors and discuss some problems in its application. It is believed that AI improves the efficiency of diagnosis, reduces the burden of clinicians, and improves the effect of treatment and prognosis. AI will play an irreplaceable role in the field of gynecological malignant oncology and will promote the development of medicine and further promote the transformation from traditional medicine to precision medicine and preventive medicine. However, there are also some problems in the application of AI in gynecologic malignant tumors. For example, AI, inseparable from human participation, still needs to be more “humanized”, and needs to further protect patients’ privacy and health, improve legal and insurance protection, and further improve according to local ethnic conditions and national conditions. However, it is believed that with the continuous development of AI, especially ensemble classifier, and deep learning will have a profound influence on the future of medical technology, which is a powerful driving force for future medical innovation and reform.
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Affiliation(s)
- Jie Zhou
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning 530021, Guangxi, People's Republic of China.,Department of Gynecology, The Second Affiliated Hospital, University of South China, Hengyang 421001, Hunan, People's Republic of China
| | - Zhi Ying Zeng
- Department of Anesthesiology, The Second Affiliated Hospital, University of South China, Hengyang 421001, Hunan, People's Republic of China
| | - Li Li
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning 530021, Guangxi, People's Republic of China
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12
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Manava P, Galster M, Heinen H, Stebner A, Lell M. Algorithmen mit künstlicher Intelligenz. Radiologe 2020; 60:952-958. [DOI: 10.1007/s00117-020-00714-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Jutzi TB, Krieghoff-Henning EI, Holland-Letz T, Utikal JS, Hauschild A, Schadendorf D, Sondermann W, Fröhling S, Hekler A, Schmitt M, Maron RC, Brinker TJ. Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective. Front Med (Lausanne) 2020; 7:233. [PMID: 32671078 PMCID: PMC7326111 DOI: 10.3389/fmed.2020.00233] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 05/05/2020] [Indexed: 12/18/2022] Open
Abstract
Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91–0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85–0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35–0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92–0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.
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Affiliation(s)
- Tanja B Jutzi
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva I Krieghoff-Henning
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tim Holland-Letz
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Jochen Sven Utikal
- Department of Dermatology, Heidelberg University, Mannheim, Germany.,Skin Cancer Unit, German Cancer Research Center, Heidelberg, Germany
| | - Axel Hauschild
- Department of Dermatology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Wiebke Sondermann
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Stefan Fröhling
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Achim Hekler
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Max Schmitt
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roman C Maron
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J Brinker
- Division of Translational Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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