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Lu H, Liu K, Zhao H, Wang Y, Shi B. Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules. Sci Rep 2024; 14:4565. [PMID: 38403645 PMCID: PMC10894854 DOI: 10.1038/s41598-024-55280-6] [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: 11/01/2023] [Accepted: 02/22/2024] [Indexed: 02/27/2024] Open
Abstract
The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.
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Affiliation(s)
- Hui Lu
- School of Medical Imaging, Bengbu Medical University, Bengbu, 233030, China
| | - Kaifang Liu
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, 210000, China
| | - Huan Zhao
- School of Medical Imaging, Bengbu Medical University, Bengbu, 233030, China
| | - Yongqiang Wang
- School of Medical Imaging, Bengbu Medical University, Bengbu, 233030, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical University, Bengbu, 233030, China.
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Li H, Wang J, Li X, Zhu X, Guo S, Wang H, Yu J, Ye X, He F. Comparison of serum from lung cancer patients and from patients with benign lung nodule using FTIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 306:123596. [PMID: 37925957 DOI: 10.1016/j.saa.2023.123596] [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: 03/09/2023] [Revised: 09/13/2023] [Accepted: 10/29/2023] [Indexed: 11/07/2023]
Abstract
Lungcancer remains the leading cause of cancer related deaths in worldwide. Earlydiagnosis oflungcancer can significantly improve survival rate. However, due to its close resemblance to the malignant nodules, the possible existence of benign nodules often leads to erroneous decisions. The aim of this study was to explore whether fourier transform infrared (FTIR) spectroscopy could improve the accuracy of early diagnosis of lung cancer by distinguishing lung cancer patients' (LCP') serum from patients with benign lung nodules' (PBLN') serum. In this study, A1243+1081/A1652+1539 ratio in LCP group was increased significantly compared with that in PBLN group, indicating that the ratio could be used to distinguish the serum of LCP from that of PBLN. In addition, the ratios of A2926/A2969, A1744/A2926+2859, A2926+2859/A1652+1539 were also increased significantly in LCP group compared with that in PBLN group. These findings suggest that FTIR spectroscopy might be a potentially effective method for the early diagnosis of lung cancer.
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Affiliation(s)
- Huanyu Li
- Nanchang University Jiangxi Medical College, Nanchang 330006, China; Second Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Jun Wang
- Second Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Xiaoyun Li
- Second Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Xianhong Zhu
- Key Laboratory of Applied Organic Chemistry, Higher Institutions of Jiangxi Province, Shangrao Normal University, Shangrao 334001, China
| | - Shaomei Guo
- Second Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Hongluan Wang
- Second Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Jie Yu
- Second Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Xiaoqun Ye
- Department of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital, Nanchang University, Nanchang 330006, China.
| | - Fan He
- Neonatal Intensive Care Unit, Jiangxi Provincial Children's Hospital, Nanchang 330038, China.
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Chen C, Geng Q, Song G, Zhang Q, Wang Y, Sun D, Zeng Q, Dai Z, Wang G. A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules. Front Oncol 2023; 13:1066360. [PMID: 37007065 PMCID: PMC10064794 DOI: 10.3389/fonc.2023.1066360] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.
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Affiliation(s)
- Chengyu Chen
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Qun Geng
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Gesheng Song
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China
| | - Qian Zhang
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Youruo Wang
- Elite Class of 2017, Shandong First Medical University, Jinan, China
| | - Dongfeng Sun
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Gongchao Wang
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Gongchao Wang,
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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Chen H, Yu Y, Yu X, Li S, Zheng L, Zhang S, Zhuang Q, Deng Z, Chen Z. An Innovative Method: Predicting the Visibility of Radial Endobronchial Ultrasound for Peripheral Pulmonary Nodules by Virtual Bronchoscopic Navigation. Technol Cancer Res Treat 2022; 21:15330338221141790. [PMID: 36529905 PMCID: PMC9772973 DOI: 10.1177/15330338221141790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background: The diagnosis of peripheral pulmonary nodules (PPNs) still is the key and difficult point. Previous studies have demonstrated that the diagnostic yield of radial endobronchial ultrasound (rEBUS) visible nodules is significantly higher than that of invisible nodules. The traditional method of predicting the rEBUS-visibility of nodules is based on the CT-bronchus signs, but its effectiveness may be unsatisfactory. Objective: We innovate a valuable predictive model based on virtual bronchoscopic navigation to identify beforehand which PPNs are likely to be successfully visualized by rEBUS. The innovative predictor is the ratio of the size of lesions (S) to the shortest straight-line distance (D) from the terminal point of the virtual navigation path to the localization point of the nodule. Methods: This is a retrospective study. On the training dataset of 214 patients, a receiver operating characteristic curve was drawn to understand the utility of the predictive model and get the optimal cut-off points. Ninety-two cases were enrolled in the validation dataset to validate the external predictive accuracy of the predictor. Results: The optimal cut-off point of the curve was 1.84 with the Youden index of 0.65, at which point the area under the curve was 0.85 (95% CI: 0.76-0.95). The predictor has a good performance in the validation dataset with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 100%, 100%, 71%, and 87%, respectively. Conclusion: The S/D ratio is a valuable and innovative method to identify beforehand which PPNs are likely to be successfully visualized by rEBUS. If the S/D ratio of the nodule is greater than 1.84, it will be visualized by rEBUS.
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Affiliation(s)
- Hui Chen
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Yiming Yu
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Xuechan Yu
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Sha Li
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Lin Zheng
- Department of Microbiology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Shuya Zhang
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Qidong Zhuang
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China
| | - Zaichun Deng
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China,Zaichun Deng, Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, No.247, Renmin Road, Jiangbei District, Ningbo, Zhejiang Province, 315020, China.
| | - Zhongbo Chen
- Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province, China,Zhongbo Chen, Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, No.247, Renmin Road, Jiangbei District, Ningbo, Zhejiang Province, 315020, China.
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Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:2058284. [PMID: 35685674 PMCID: PMC9162868 DOI: 10.1155/2022/2058284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 04/29/2022] [Accepted: 05/11/2022] [Indexed: 12/04/2022]
Abstract
In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system components are applied to electroencephalogram (EEG) signal transmission, to which a threshold compression module and a vector reconstruction module are added to address the system power burden associated with the acquisition and transmission of large amounts of real-time EEG data in the medical IoT. In the threshold compression module, the inherent characteristics of EEG signals are analysed, and the generated EEG data are decomposed into multiple symbolic streams and compressed by applying different thresholds to improve the compression ratio while ensuring the quality of service of the application. A deep neural network-based approach is proposed for the detection and diagnosis of lung nodules. Automatic identification and measurement of simulated lung nodules and the corresponding volumes of nodules in images under different conditions are applied. The sensitivity of each AIADS in identifying lung nodules under different convolution kernel conditions, false positives (FP), false negatives (FN), relative volume errors (RVE), the miss detection rate (MDR) for different types of lung nodules, and the performance of each system in predicting the four types of nodules are calculated. In this paper, an interpretable multibranch feature convolutional neural network model is proposed for the diagnosis of benign and malignant lung nodules. It is demonstrated that the proposed model not only yields interpretable lung nodule classification results but also achieves better lung nodule classification performance with an accuracy rate of 97.8%.
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Wang X, Gao M, Xie J, Deng Y, Tu W, Yang H, Liang S, Xu P, Zhang M, Lu Y, Fu C, Li Q, Fan L, Liu S. Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2022; 12:892890. [PMID: 35747810 PMCID: PMC9209648 DOI: 10.3389/fonc.2022.892890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention. Methods Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’. Results The image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset. Conclusion The deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.
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Affiliation(s)
- Xiang Wang
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Man Gao
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Jicai Xie
- Department of Radiology, The Second People’s Hospital of Yuhuan, Yuhuan, China
| | - Yanfang Deng
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Hua Yang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Shuang Liang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Panlong Xu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Mingzi Zhang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Yang Lu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - ChiCheng Fu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Qiong Li
- Department of Radiology, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
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Chen B, Li Q, Hao Q, Tan J, Yan L, Zhu Y, Hu C, Qian G, Zhang G, Chen L, Zhou C, Zhang J, Sun J, Jiang L, Zhang L, Wang Q, Zhang X, Jin Y, He Y, Song Y, Sun X, Li W. Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline. J Evid Based Med 2022; 15:142-151. [PMID: 35775869 DOI: 10.1111/jebm.12476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/19/2022] [Indexed: 02/05/2023]
Abstract
CLINICAL QUESTION The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty. CURRENT PRACTICE There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing. RECOMMENDATIONS In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk. HOW THIS GUIDELINE WAS CREATED This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure. THE EVIDENCE A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed. UNDERSTANDING THE RECOMMENDATION The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.
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Affiliation(s)
- Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
- Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Qianrui Li
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real-World Data, Chengdu, China
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qiukui Hao
- The Center of Gerontology and Geriatrics/National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Jing Tan
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real-World Data, Chengdu, China
| | - Lan Yan
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
- Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Zhu
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
- Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Chengping Hu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
| | - Guisheng Qian
- Institute of Respiratory Disease, The Second Affiliated Hospital, Army Medical University, Chongqing, China
| | - Guozhen Zhang
- Department of Radiology, Huadong Hospital Fudan University, Shanghai, China
| | - Liangan Chen
- Department of Respiratory Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Chengzhi Zhou
- State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital, Guangzhou, China
| | - Jian Zhang
- Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Air-Force Medical University, Xi'an, China
| | - Jiayuan Sun
- Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Li Jiang
- Department of Respiration, the Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Li Zhang
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
| | - Qi Wang
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Xiaoju Zhang
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yang Jin
- Department of Respiratory Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yong He
- Department of Respiratory Disease, Daping Hospital, Army Medical University, Chongqing, China
| | - Yong Song
- Department of Respiratory and Critical Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real-World Data, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
- Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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9
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Xie S, Li S, Deng H, Han Y, Liu G, Liu Q. Application Value of PET/CT and MRI in the Diagnosis and Treatment of Patients With Synchronous Multiple Pulmonary Ground-Glass Nodules. Front Oncol 2022; 12:797823. [PMID: 35280735 PMCID: PMC8905144 DOI: 10.3389/fonc.2022.797823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/21/2022] [Indexed: 11/29/2022] Open
Abstract
Background Synchronous multiple ground-glass nodules (SMGGNs) in synchronous multiple lung cancers are associated with specific imaging findings. It is difficult to distinguish whether multiple nodules are primary tumors or metastatic lesions in the lungs. The need for PET/CT and contrast-enhanced brain MRI for these patients remains unclear. This study investigated the necessity of these two imaging examinations for SMGGN patients by means of retrospective analysis. Methods SMGGN patients who were diagnosed and treated in our hospital from October 2017 to May 2020 and underwent whole-body PET/CT(Cranial excepted) and/or contrast-enhanced brain MRI+DWI were enrolled in this study. We analyzed the imaging and clinical characteristics of these patients to evaluate SMGGN patients’ need to undergo whole-body PET/CT and brain MRI examination. Results A total of 87 SMGGN patients were enrolled. 51 patients underwent whole-body PET/CT examinations and did not show signs of primary tumors in other organs, metastatic foci in other organs, or metastasis to surrounding lymph nodes. 87 patients underwent whole-brain MRI, which did not reveal brain metastases but did detect an old cerebral infarction in 23 patients and a new cerebral infarction in one patient. 87 patients underwent surgical treatment in which 219 nodules were removed. All nodules were diagnosed as adenocarcinoma or atypical adenomatous hyperplasia. No lymph node metastasis was noted. Conclusion For SMGGN patients, PET/CT and enhanced cranial MRI are unnecessary for SMGGNs patients, but from the perspective of perioperative patient safety, preoperative MRI+DWI examination is recommended for SMGGNs patients.
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Affiliation(s)
- Shaonan Xie
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shaoteng Li
- Department of Diagnostic Radiology, The People's Hospital of Xingtai, Xingtai, China
| | - Huiyan Deng
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yaqing Han
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangjie Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Qingyi Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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Bak SH, Kim C, Kim CH, Ohno Y, Lee HY. Magnetic resonance imaging for lung cancer: a state-of-the-art review. PRECISION AND FUTURE MEDICINE 2022. [DOI: 10.23838/pfm.2021.00170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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11
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Zhang R, Sun H, Chen B, Xu R, Li W. Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. J Thorac Dis 2021; 13:4156-4168. [PMID: 34422345 PMCID: PMC8339772 DOI: 10.21037/jtd-21-80] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
Background Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. Methods This study enrolled 5–20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model. Results Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model. Conclusions Predictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Renjie Xu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:9668836. [PMID: 34377105 PMCID: PMC8318753 DOI: 10.1155/2021/9668836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/12/2021] [Indexed: 12/02/2022]
Abstract
This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.
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Zhang R, Tian P, Chen B, Zhou Y, Li W. Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes. Cancer Manag Res 2020; 12:8057-8066. [PMID: 32943938 PMCID: PMC7481308 DOI: 10.2147/cmar.s256719] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/13/2020] [Indexed: 02/05/2023] Open
Abstract
Objective Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size. Materials and Methods This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models. Results The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size. Conclusion We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Panwen Tian
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Department of Lung Cancer Treatment Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yongzhao Zhou
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
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Liu B, Ye X. Computed tomography-guided percutaneous microwave ablation: A novel perspective to treat multiple pulmonary ground-glass opacities. Thorac Cancer 2020; 11:2385-2388. [PMID: 32748566 PMCID: PMC7471011 DOI: 10.1111/1759-7714.13601] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 07/15/2020] [Indexed: 11/29/2022] Open
Affiliation(s)
- Baodong Liu
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xin Ye
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
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Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C, Miele V, Neri E, Grassi R. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey. Radiol Med 2020; 126:63-71. [PMID: 32350797 DOI: 10.1007/s11547-020-01205-y] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 04/16/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To report the results of a nationwide online survey on artificial intelligence (AI) among radiologist members of the Italian Society of Medical and Interventional Radiology (SIRM). METHODS AND MATERIALS All members were invited to the survey as an initiative by the Imaging Informatics Chapter of SIRM. The survey consisted of 13 questions about the participants' demographic information, perceived advantages and issues related to AI implementation in radiological practice, and their overall opinion about AI. RESULTS In total, 1032 radiologists (equaling 9.5% of active SIRM members for the year 2019) joined the survey. Perceived AI advantages included a lower diagnostic error rate (750/1027, 73.0%) and optimization of radiologists' work (697/1027, 67.9%). The risk of a poorer professional reputation of radiologists compared with non-radiologists (617/1024, 60.3%), and increased costs and workload due to AI system maintenance and data analysis (399/1024, 39.0%) were seen as potential issues. Most radiologists stated that specific policies should regulate the use of AI (933/1032, 90.4%) and were not afraid of losing their job due to it (917/1032, 88.9%). Overall, 77.0% of respondents (794/1032) were favorable to the adoption of AI, whereas 18.0% (186/1032) were uncertain and 5.0% (52/1032) were unfavorable. CONCLUSIONS Radiologists had a mostly positive attitude toward the implementation of AI in their working practice. They were not concerned that AI will replace them, but rather that it might diminish their professional reputation.
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Affiliation(s)
- Francesca Coppola
- Department of Specialized, Diagnostic and Experimental Medicine (DIMES), S. Orsola Hospital, University of Bologna, Bologna, Italy
| | - Lorenzo Faggioni
- Diagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Via Roma, 67, 56126, Pisa, Italy.
| | - Daniele Regge
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
| | - Andrea Giovagnoni
- Radiology Department, Università Politecnica delle Marche, Ancona, Italy
| | - Rita Golfieri
- Department of Specialized, Diagnostic and Experimental Medicine (DIMES), S. Orsola Hospital, University of Bologna, Bologna, Italy
| | | | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Emanuele Neri
- Diagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Via Roma, 67, 56126, Pisa, Italy
| | - Roberto Grassi
- Department of Precision Medicine, University of Campania, Naples, Italy
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