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Cui X, Zheng S, Zhang W, Fan S, Wang J, Song F, Liu X, Zhu W, Ye Z. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT. Eur Radiol 2023:10.1007/s00330-023-09432-3. [PMID: 36723725 DOI: 10.1007/s00330-023-09432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/31/2022] [Accepted: 01/02/2023] [Indexed: 02/02/2023]
Abstract
OBJECTIVES This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT. METHODS A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately. RESULTS Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts. CONCLUSIONS We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment. KEY POINTS • Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Sunyi Zheng
- Artificial Intelligence and Biomedical Image Analysis Lab, School of Engineering, Westlake University, Hangzhou, People's Republic of China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, People's Republic of China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Jing Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, People's Republic of China
| | - Feipeng Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Xu Liu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Weijie Zhu
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China.
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Weir-McCall JR, Debruyn E, Harris S, Qureshi NR, Rintoul RC, Gleeson FV, Gilbert FJ. Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging. Chest 2023; 163:444-454. [PMID: 36087795 PMCID: PMC9899635 DOI: 10.1016/j.chest.2022.08.2227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 08/19/2022] [Accepted: 08/24/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy. RESEARCH QUESTION What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? STUDY DESIGN AND METHODS This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test. RESULTS Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only). INTERPRETATION An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules. TRIAL REGISTRATION ClinicalTrials.gov Identifier; No.: NCT02013063.
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Affiliation(s)
- Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge; Department of Radiology, Royal Papworth Hospital, Cambridge
| | - Elise Debruyn
- College of Medicine, University of Illinois at Chicago, Chicago, IL
| | - Scott Harris
- Faculty of Public Health Sciences and Medical Statistics, University of Southampton, Southampton
| | | | - Robert C Rintoul
- Department of Oncology, University of Cambridge; Department of Thoracic Oncology, Royal Papworth Hospital
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital and University of Oxford, Oxford, England
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge.
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Xie X, Liu K, Luo K, Xu Y, Zhang L, Wang M, Shen W, Zhou Z. Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model. Front Oncol 2023; 13:1147479. [PMID: 37213284 PMCID: PMC10196349 DOI: 10.3389/fonc.2023.1147479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/25/2023] [Indexed: 05/23/2023] Open
Abstract
Objective This study aimed to investigate the role of spectral detector computed tomography (SDCT) quantitative parameters and their derived quantitative parameters combined with lesion morphological information in the differential diagnosis of solid SPNs. Methods This retrospective study included basic clinical data and SDCT images of 132 patients with pathologically confirmed SPNs (102 and 30 patients in the malignant and benign groups, respectively). The morphological signs of SPNs were evaluated and the region of interest (ROI) was delineated from the lesion to extract and calculate the relevant SDCT quantitative parameters, and standardise the process. Differences in qualitative and quantitative parameters between the groups were statistically analysed. A receiver operating characteristic (ROC) curve was constructed to evaluate the efficacy of the corresponding parameters in the diagnosis of benign and malignant SPNs. Statistically significant clinical data, CT signs and SDCT quantitative parameters were analysed using multivariate logistic regression to determine the independent risk factors for predicting benign and malignant SPNs, and the best multi-parameter regression model was established. Inter-observer repeatability was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results Malignant SPNs differed from benign SPNs in terms of size, lesion morphology, short spicule sign, and vascular enrichment sign (P< 0.05). The SDCT quantitative parameters and their derived quantitative parameters of malignant SPNs (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, NZeff) were significantly higher than those of benign SPNs (P< 0.05). In the subgroup analysis, most parameters could distinguish between benign and adenocarcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, and NZeff), and between benign and squamous cell carcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, NEF40keV, NEF70keV, λ, and NIC). However, there were no significant differences between the parameters in the adenocarcinoma and squamous cell carcinoma groups. ROC curve analysis indicated that NIC, NEF70keV, and NEF40keV had higher diagnostic efficacy for differentiating benign and malignant SPNs (area under the curve [AUC]:0.869, 0.854, and 0.853, respectively), and NIC was the highest. Multivariate logistic regression analysis showed that size (OR=1.138, 95% CI 1.022-1.267, P=0.019), Δ70keV (OR=1.060, 95% CI 1.002-1.122, P=0.043), and NIC (OR=7.758, 95% CI 1.966-30.612, P=0.003) were independent risk factors for the prediction of benign and malignant SPNs. ROC curve analysis showed that the AUC of size, Δ70keV, NIC, and a combination of the three for differential diagnosis of benign and malignant SPNs were 0.636, 0.846, 0.869, and 0.903, respectively. The AUC for the combined parameters was the largest, and the sensitivity, specificity, and accuracy were 88.2%, 83.3% and 86.4%, respectively. The SDCT quantitative parameters and their derived quantitative parameters in this study exhibited satisfactory inter-observer repeatability (ICC: 0.811-0.997). Conclusion SDCT quantitative parameters and their derivatives can be helpful in the differential diagnosis of benign and malignant solid SPNs. The quantitative parameter, NIC, is superior to the other relevant quantitative parameters and when NIC is combined with lesion size and Δ70keV value for comprehensive diagnosis, the efficacy could be further improved.
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Affiliation(s)
- Xiaodong Xie
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
- Department of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
| | - Kaifang Liu
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Kai Luo
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Youtao Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Lei Zhang
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Meiqin Wang
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
- *Correspondence: Meiqin Wang, ; Zhengyang Zhou, ; Wenrong Shen,
| | - Wenrong Shen
- Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
- *Correspondence: Meiqin Wang, ; Zhengyang Zhou, ; Wenrong Shen,
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- *Correspondence: Meiqin Wang, ; Zhengyang Zhou, ; Wenrong Shen,
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Yang Y, Tan M, Ma W, Duan S, Huang X, Jin L, Tang L, Li M. Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram. Clin Radiol 2022; 77:e680-e688. [PMID: 35718542 DOI: 10.1016/j.crad.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/05/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
AIM To develop and validate a radiomics nomogram for prediction of degree of differentiation in lung adenocarcinoma presenting as sub-solid or solid nodules. MATERIALS AND METHODS A total of 438 patients with histopathologically confirmed adenocarcinoma (248 non-poorly differentiated and 190 poorly differentiated) were divided into training cohort (n=235) and internal validation cohort (n=203) according to surgery sequence. Sixty patients form public TCIA dataset were selected for external validation. One thousand, two hundred and eighteen radiomics features were extracted from each volumetric region of interest and a least absolute shrinkage and selection operator logistic regression was applied to select meaningful radiomic features for building a radiomics score (Rad-score) model. A nomogram model incorporating the Rad-score and type was established after multivariable logistic regression. The discrimination efficiency, calibration efficacy, and clinical utility value of the nomogram were evaluated. RESULTS The Rad-score model could predict the differentiation degree of lung adenocarcinoma with an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78-0.89) in the internal validation cohort. The AUC of the nomogram and radiographic model was 0.86 (95% CI: 0.80-0.91), 0.78 (95% CI: 0.72-0.84) in the internal validation cohort respectively. The AUC of the nomogram in the external validation cohort was 0.73 (95% CI: 0.58-0.88). Delong's test showed that the nomogram performed better than radiographic features alone (p=0.001). CONCLUSIONS The proposed radiomics nomogram has the potential to predict the differentiation degree of lung adenocarcinoma preoperatively.
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Affiliation(s)
- Y Yang
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - M Tan
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - X Huang
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - L Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China.
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Chen G, Bai T, Wen LJ, Li Y. Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis. J Cardiothorac Surg 2022; 17:102. [PMID: 35505414 PMCID: PMC9066878 DOI: 10.1186/s13019-022-01859-x] [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: 11/25/2021] [Accepted: 04/24/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis. METHODS The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0. RESULTS In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84-0.91), 78% (95CI%: 0.74-0.80), 3.91 (95CI%: 3.42-4.46), 0.16 (95CI%: 0.12-0.21), and 3.21 (95CI%: 2.87-3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83-0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I2 = 89.07%), NLR (I2 = 87.29%), and diagnostic score (I2 = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539). CONCLUSIONS The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.
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Affiliation(s)
- Gang Chen
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Tian Bai
- Radiological Imaging Diagnostic Center, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, China
| | - Li-Juan Wen
- Radiological Imaging Diagnostic Center, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, China
| | - Yu Li
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China.
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Gilbert FJ, Harris S, Miles KA, Weir-McCall JR, Qureshi NR, Rintoul RC, Dizdarevic S, Pike L, Sinclair D, Shah A, Eaton R, Jones J, Clegg A, Benedetto V, Hill J, Cook A, Tzelis D, Vale L, Brindle L, Madden J, Cozens K, Little L, Eichhorst K, Moate P, McClement C, Peebles C, Banerjee A, Han S, Poon FW, Groves AM, Kurban L, Frew A, Callister MEJ, Crosbie PA, Gleeson FV, Karunasaagarar K, Kankam O, George S. Comparative accuracy and cost-effectiveness of dynamic contrast-enhanced CT and positron emission tomography in the characterisation of solitary pulmonary nodules. Thorax 2021; 77:988-996. [PMID: 34887348 DOI: 10.1136/thoraxjnl-2021-216948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 10/24/2021] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Dynamic contrast-enhanced CT (DCE-CT) and positron emission tomography/CT (PET/CT) have a high reported accuracy for the diagnosis of malignancy in solitary pulmonary nodules (SPNs). The aim of this study was to compare the accuracy and cost-effectiveness of these. METHODS In this prospective multicentre trial, 380 participants with an SPN (8-30 mm) and no recent history of malignancy underwent DCE-CT and PET/CT. All patients underwent either biopsy with histological diagnosis or completed CT follow-up. Primary outcome measures were sensitivity, specificity and overall diagnostic accuracy for PET/CT and DCE-CT. Costs and cost-effectiveness were estimated from a healthcare provider perspective using a decision-model. RESULTS 312 participants (47% female, 68.1±9.0 years) completed the study, with 61% rate of malignancy at 2 years. The sensitivity, specificity, positive predictive value and negative predictive values for DCE-CT were 95.3% (95% CI 91.3 to 97.5), 29.8% (95% CI 22.3 to 38.4), 68.2% (95% CI 62.4% to 73.5%) and 80.0% (95% CI 66.2 to 89.1), respectively, and for PET/CT were 79.1% (95% CI 72.7 to 84.2), 81.8% (95% CI 74.0 to 87.7), 87.3% (95% CI 81.5 to 91.5) and 71.2% (95% CI 63.2 to 78.1). The area under the receiver operator characteristic curve (AUROC) for DCE-CT and PET/CT was 0.62 (95% CI 0.58 to 0.67) and 0.80 (95% CI 0.76 to 0.85), respectively (p<0.001). Combined results significantly increased diagnostic accuracy over PET/CT alone (AUROC=0.90 (95% CI 0.86 to 0.93), p<0.001). DCE-CT was preferred when the willingness to pay per incremental cost per correctly treated malignancy was below £9000. Above £15 500 a combined approach was preferred. CONCLUSIONS PET/CT has a superior diagnostic accuracy to DCE-CT for the diagnosis of SPNs. Combining both techniques improves the diagnostic accuracy over either test alone and could be cost-effective. TRIAL REGISTRATION NUMBER NCT02013063.
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Affiliation(s)
- Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Scott Harris
- Public Health Sciences and Medical Statistics, University of Southampton, Southampton, Southampton, UK
| | - Kenneth A Miles
- Institute of Nuclear Medicine, University College London, London, UK
| | - Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge, Cambridge, UK.,Department of Radiology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK
| | - Nagmi R Qureshi
- Department of Radiology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK
| | - Robert Campbell Rintoul
- Department of Thoracic Oncology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK.,Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabina Dizdarevic
- Imaging and Nuclear Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK.,Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Lucy Pike
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| | - Donald Sinclair
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| | - Andrew Shah
- Radiation Protection, East and North Hertfordshire NHS Trust, Stevenage, UK
| | - Rosemary Eaton
- Radiation Protection, East and North Hertfordshire NHS Trust, Stevenage, UK
| | - Jeremy Jones
- Centre for Innovation and Leadership in Health Sciences, University of Southampton, Southampton, UK
| | - Andrew Clegg
- Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, Applied Health Research Hub, University of Central Lancashire, Preston, UK
| | - Valerio Benedetto
- Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, Applied Health Research Hub, University of Central Lancashire, Preston, UK
| | - James Hill
- Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, Applied Health Research Hub, University of Central Lancashire, Preston, UK
| | - Andrew Cook
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Dimitrios Tzelis
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Luke Vale
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Lucy Brindle
- School of Health Sciences, University of Southampton, Southampton, UK
| | - Jackie Madden
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Kelly Cozens
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Louisa Little
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Kathrin Eichhorst
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Patricia Moate
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Chris McClement
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Charles Peebles
- Department of Radiology and Respiratory Medicine, Southampton University Hospitals NHS Foundation Trust, Southampton, UK
| | - Anindo Banerjee
- Department of Radiology and Respiratory Medicine, Southampton University Hospitals NHS Foundation Trust, Southampton, UK
| | - Sai Han
- West of Scotland PET Centre, Gartnavel General Hospital, Glasgow, UK
| | - Fat-Wui Poon
- West of Scotland PET Centre, Gartnavel General Hospital, Glasgow, UK
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London, London, UK
| | - Lutfi Kurban
- Department of Radiology, Aberdeen Royal Hospitals NHS Trust, Aberdeen, UK
| | - Anthony Frew
- Imaging and Nuclear Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | | | - Phil A Crosbie
- Division of Infection, Immunity and Respiratory Medicine, University Hospital of South Manchester, Manchester, UK
| | - Fergus Vincent Gleeson
- Department of Radiology, Churchill Hospital, Oxford, UK.,Department of Radiology, University of Oxford, Oxford, UK
| | | | - Osei Kankam
- Department of Thoracic Medicine, East Sussex Healthcare NHS Trust, Saint Leonards-on-Sea, UK
| | - Steve George
- Public Health Sciences and Medical Statistics, University of Southampton, Southampton, Southampton, UK
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Zhou C, Liu XB, Gan XJ, Li X. Calcification sign for prediction of benignity in pulmonary nodules: A meta-analysis. THE CLINICAL RESPIRATORY JOURNAL 2021; 15:1073-1080. [PMID: 34142452 DOI: 10.1111/crj.13410] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 04/13/2021] [Accepted: 06/08/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND The calcification sign assessed by computed tomography appears to be a potential marker for benignities among patients diagnosed with pulmonary nodules (PNs). The following meta-analysis has been purposefully designed to figure-out the diagnostic value of the calcification signature as a way of identifying benignities from PNs. METHODS Cochrane Library, Embase and PubMed were considered as a reference to obtain the required data from January 2000 until October 2020. Stata v12.0 was used as a standard tool for statistical assessment. RESULTS Eleven retrospective studies were assessed via this meta-analysis, which included 6136 PNs (1827 benign and 4309 malignant). The pooled diagnostic odd ratios, positive likelihood ratio (PLR), negative likelihood ratio (NLR), sensitivity and specificity were 6.79, 6.06, 0.89, 13% and 98%, respectively. The value obtained for the area under the curve was 0.65, showing moderate overall diagnostic accuracy. A significant heterogeneity was found while calculating the pooled sensitivity (I2 = 85.5%), specificity (I2 = 75.0%), PLR (I2 = 59.0%), NLR (I2 = 79.5%) and DOR (I2 = 100.0%) in the current analysis. Sub-group analyses presented better PLR and specificity values for the study with a sample size ≥ 400. Deeks' funnel plot asymmetry test detected no potential evidence of significant publication bias (p = 0.091). CONCLUSIONS Calcification signs have been identified as moderate regulators corresponding to overall diagnostic performance (via marking a distinct differentiation between malignant and benign) for PNs. However, the manifestation of the calcification sign had a good directive property for benign PNs.
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Affiliation(s)
- Cheng Zhou
- CT Department, The Third Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiao-Bei Liu
- Imaging Center, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiao-Jing Gan
- CT Department, The Third Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xing Li
- Department of Radiology, Xuzhou Infectious Hospital, Xuzhou, China
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Clinical Analysis of Video-Assisted Thoracoscopic Surgery for Resection of Solitary Pulmonary Nodules and Influencing Factors in the Diagnosis of Benign and Malignant Nodules. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2021; 2021:1490709. [PMID: 34504530 PMCID: PMC8423549 DOI: 10.1155/2021/1490709] [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: 08/05/2021] [Accepted: 08/17/2021] [Indexed: 01/13/2023]
Abstract
Purpose This is a retrospective research comparing the clinical outcomes of single-hole versus multi-hole video-assisted thoracoscopic surgical (VATS) resection for solitary pulmonary nodules (SPN) and examining the factors influencing the diagnosis of benign and malignant pulmonary nodules. Method We collected the clinical data, surgical status, outcomes, and corresponding imaging features of 317 patients with SPN who were surgically resected by VATS and diagnosed as benign or malignant by pathology in our hospital from January 2019 to December 2021. Result Among the 317 patients, 124 (39.12%) underwent single-port VATS and 193 (60.88%) underwent multiple-hole VATS. All patients were grouped according to the different surgical methods, and their postoperative indicators were statistically analyzed. The results showed that neither the single-port VATS group nor the multi-port VATS group had any serious adverse events such as death during the perioperative period. The average operation time, intraoperative blood loss, drainage tube indwelling time, and postoperative hospital stay were significantly lower in the two groups. Statistics of postoperative pathological diagnosis showed that 98 cases (30.91%) of all nodules were benign nodules and 219 cases (69.09%) were malignant nodules, and a further single-multivariate analysis showed that age, nodule maximum diameter, lobular sign, burr sign, vascular cluster sign, and pleural depression sign were independent relevant factors for the diagnosis of benign and malignant nodules. Conclusion VATS is less invasive and has fewer complications and is of great clinical value for both diagnosis and treatment of benign and malignant SPN. Age, maximum nodal diameter, lobar sign, burr sign, vascular set sign, and pleural depression sign were independent correlates affecting the diagnosis of benign and malignant SPN, which reminds that great attention should be paid to patients who are older and have risk factors on imaging, and early and timely active treatment or close follow-up should be carried out.
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Li Y, Wang T, Fu YF, Shi YB. Computed tomography-based spiculated sign for prediction of malignancy in lung nodules: A meta-analysis. CLINICAL RESPIRATORY JOURNAL 2020; 14:1113-1121. [PMID: 32790919 DOI: 10.1111/crj.13258] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 08/04/2020] [Accepted: 08/10/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Computed tomography (CT)-based spiculated sign is a risk factor for malignancy in patients with lung nodules (LNs). The present meta-analysis aimed to evaluate the diagnostic utility of CT-based spiculated sign as a means of differentiating between malignant and benign LNs. METHODS PubMed, Cochrane Library and Embase were reviewed from January 2000 to March 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis. RESULTS We identified 19 retrospective studies for inclusion in this meta-analysis. These studies compiled data pertaining to 8549 LNs (5547 malignant and 3003 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratios (DOR) were 0.51 (95% CI: 0.36-0.65), 0.84 (95% CI: 0.74-0.91), 3.15 (95% CI: 2.34-4.23), 0.59 (95% CI: 0.47-0.73) and 5.36 (95% CI: 3.93-7.31), respectively. The area under curve (AUC) was 0.76. Significant heterogeneity was detected among these studies with respect to sensitivity (I2 = 98.4%, P = .00), specificity (I2 = 95.8%, P = .00), PLR (I2 = 78.9%, P = .00), NLR (I2 = 99.3%, P = .00) and DOR (I2 = 100%, P = .00). A meta-regression analysis revealed that the country in which a study was conducted (China vs Not China) had a strong influence on reported sensitivity and specificity. No significant publication bias was detected via Deeks' funnel plot asymmetry test (P = .191). CONCLUSIONS CT-based spiculated sign can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.
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Affiliation(s)
- Yu Li
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Tao Wang
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Yu-Fei Fu
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Yi-Bing Shi
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
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