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Yuan J, Xu F, Sun Y, Ren H, Chen M, Feng S. Shared decision-making in the management of pulmonary nodules: a systematic review of quantitative and qualitative studies. BMJ Open 2024; 14:e079080. [PMID: 38991667 PMCID: PMC11243204 DOI: 10.1136/bmjopen-2023-079080] [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: 08/21/2023] [Accepted: 06/26/2024] [Indexed: 07/13/2024] Open
Abstract
OBJECTIVE The objective of this systematic review was to explore the evidence regarding shared decision-making (SDM) in the management of pulmonary nodules. DESIGN Systematic review of quantitative and qualitative studies. DATA SOURCE Studies published in English or Chinese up to April 2022 were extracted from nine databases: PubMed, PsycINFO, EMBASE, Cochrane Library, Web of Science and CINAHL, China National Knowledge Infrastructure, Wanfang Data and SinoMed Data. ELIGIBILITY CRITERIA Studies were eligible if patients or healthcare providers are faced with pulmonary nodule management options or the interventions or experiences were focused on the patient-healthcare provider relationship or health education to make, increase or support shared decisions. All types of studies were included, including quantitative and qualitative studies. Grey literature and literature that had not been peer reviewed were excluded. Poster abstracts and non-empirical publications such as editorials, letters, opinion papers and review articles were excluded. DATA EXTRACTION AND SYNTHESIS Two reviewers independently screened abstracts and full texts, assessed quality using Joanna Briggs Institute's critical appraisal tools, and extracted data from included studies. Thematic syntheses were used to identify prominent themes emerging from the data. RESULTS A total of 12 studies met the inclusion criteria, 11 of which were conducted in USA. These included six qualitative studies and six quantitative studies (including both survey and quasi-experimental designs). Three major themes with specific subthemes emerged: (1) Opportunity (uncertainty in the diagnosis and treatment of pulmonary nodules, willingness to participate in decision-making); (2) Ability (patient's lack of knowledge, physician's experience); and (3) Different worldview (misconception, distress among patients, preference for diagnosis and treatment). CONCLUSIONS Uncertainty in the management of pulmonary nodules is the opportunity to implement SDM. Patients' lack of knowledge, distress, and misunderstandings between healthcare providers and patients are both the main obstacles and the causes of the application of SDM.
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Affiliation(s)
- Jingmin Yuan
- Department of Preventive Medicine, Health Science Center, Yangtze University, Jingzhou, China
| | - Fenglin Xu
- Department of Nursing, Hubei College of Chinese Medicine, Jingzhou, China
| | - Yan Sun
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Hui Ren
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Department of Talent Highland, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Mingwei Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Sifang Feng
- Department of Pulmonary and Critical Care Medicine, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, China
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Zhang Y, Qu L, Zhang H, Wang Y, Gao G, Wang X, Zhang T. Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT. Front Med (Lausanne) 2024; 11:1403020. [PMID: 38975053 PMCID: PMC11224554 DOI: 10.3389/fmed.2024.1403020] [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: 03/18/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Background The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures. Methods We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established. Results (1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0. Conclusion We analyzed the risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.
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Affiliation(s)
- Yifan Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Lin Qu
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Haihua Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Ying Wang
- Department of Respiratory Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Guizhou Gao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Xiaodong Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Tao Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
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Copeland J, Rojas-Alexandre M, Tsai L, King F, Hata N. Characterizing the accuracy of robotic bronchoscopy in localization & targeting of small pulmonary lesions. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03152-9. [PMID: 38890223 DOI: 10.1007/s11548-024-03152-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 04/15/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE Considering the recent implementation of lung cancer screening guidelines, it is crucial that small pulmonary nodules are accurately diagnosed. There is a significant need for quick, precise, and minimally invasive biopsy methods, especially for patients with small lung lesions in the outer periphery. Robotic bronchoscopy (RB) has recently emerged as a novel solution. The purpose of this study was to evaluate the accuracy of RB compared to the existing standard, electromagnetic navigational bronchoscopy (EM-NB). METHODS A prospective, single-blinded, and randomized-controlled study was performed to compare the accuracy of RB to EM-NB in localizing and targeting pulmonary lesions in a porcine lung model. Four operators were tasked with navigating to four pulmonary targets in the outer periphery of a porcine lung, to which they were blinded, using both the RB and EM-NB systems. The dependent variable was accuracy. Accuracy was measured as a rate of success in lesion localization and targeting, the distance from the center of the pulmonary target, and by anatomic location. The independent variable was the navigation system, RB was compared to EM-NB using 1:1 randomization. RESULTS Of 75 attempts, 72 were successful in lesion localization and 60 were successful in lesion targeting. The success rate for lesion localization was 100% with RB and 91% with EM- NB. The success rate for lesion targeting was 93% with RB and 80% for EM-NB. RB demonstrated superior accuracy in reaching the distance from the center of the lesion, at 0.62 mm compared to EM-NB at 1.28 mm (p = 0.001). Accuracy was improved using RB compared to EM- NB for lesions in the LLL (p = 0.025), LUL (p < 0.001), and RUL (p < 0.001). CONCLUSION Our findings support RB as a more accurate method of navigating and localizing small peripheral pulmonary targets when compared to standard EM-NB in a porcine lung model. This may be attributed to the ability of RB to reduce substantial tissue displacement seen with standard EM-NB navigation. As the development and application of RB advances, so will the ability to accurately diagnose small peripheral lung cancer nodules, providing patients with early-stage lung cancer the best possible outcomes.
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Affiliation(s)
- Jessica Copeland
- Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Mehida Rojas-Alexandre
- Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lilian Tsai
- Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Franklin King
- Division of Thoracic Surgery, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nobuhiko Hata
- Division of Thoracic Surgery, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Masquelin AH, Cheney N, José Estépar RS, Bates JHT, Kinsey CM. LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules. Cancer Biomark 2024:CBM230444. [PMID: 38848168 DOI: 10.3233/cbm-230444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
BACKGROUND Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images. OBJECTIVE In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules. METHODS We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included. RESULTS The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively. CONCLUSION We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.
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Affiliation(s)
- Axel H Masquelin
- Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA
| | - Nick Cheney
- Computer Science, University of Vermont, Burlington, VT, USA
| | | | - Jason H T Bates
- Department of Medicine, College of Medicine, University of Vermont, Burlington, VT, USA
| | - C Matthew Kinsey
- Department of Medicine, Pulmonary and Critical Care, College of Medicine, University of Vermont, Burlington, VT, USA
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Wang X, Cui Y, Wang Y, Liu S, Meng N, Wei W, Bai Y, Shen Y, Guo J, Guo Z, Wang M. Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI. J Magn Reson Imaging 2024. [PMID: 38602245 DOI: 10.1002/jmri.29388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities. PURPOSE To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules. STUDY TYPE Prospective. POPULATION 54 patients, including 21 men and 33 women. FIELD STRENGTH/SEQUENCE Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo. ASSESSMENT Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded. STATISTICAL TESTS The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant. RESULTS A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements. DATA CONCLUSION The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xinhui Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yingying Cui
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Ying Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Shuo Liu
- Department of Medical Imaging, Xinxiang Medical University and Henan Provincial People's Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Wei Wei
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yan Bai
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yu Shen
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | | | - Zhiping Guo
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
- Health Management Center of Henan Province, Zhengzhou University People's Hospital and FuWai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
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Zahari R, Cox J, Obara B. Uncertainty-aware image classification on 3D CT lung. Comput Biol Med 2024; 172:108324. [PMID: 38508053 DOI: 10.1016/j.compbiomed.2024.108324] [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: 10/20/2023] [Revised: 03/06/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
Early detection is crucial for lung cancer to prolong the patient's survival. Existing model architectures used in such systems have shown promising results. However, they lack reliability and robustness in their predictions and the models are typically evaluated on a single dataset, making them overconfident when a new class is present. With the existence of uncertainty, uncertain images can be referred to medical experts for a second opinion. Thus, we propose an uncertainty-aware framework that includes three phases: data preprocessing and model selection and evaluation, uncertainty quantification (UQ), and uncertainty measurement and data referral for the classification of benign and malignant nodules using 3D CT images. To quantify the uncertainty, we employed three approaches; Monte Carlo Dropout (MCD), Deep Ensemble (DE), and Ensemble Monte Carlo Dropout (EMCD). We evaluated eight different deep learning models consisting of ResNet, DenseNet, and the Inception network family, all of which achieved average F1 scores above 0.832, and the highest average value of 0.845 was obtained using InceptionResNetV2. Furthermore, incorporating the UQ demonstrated significant improvement in the overall model performance. Upon evaluation of the uncertainty estimate, MCD outperforms the other UQ models except for the metric, URecall, where DE and EMCD excel, implying that they are better at identifying incorrect predictions with higher uncertainty levels, which is vital in the medical field. Finally, we show that using a threshold for data referral can greatly improve the performance further, increasing the accuracy up to 0.959.
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Affiliation(s)
- Rahimi Zahari
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Julie Cox
- County Durham and Darlington NHS Foundation Trust, County Durham, UK
| | - Boguslaw Obara
- School of Computing, Newcastle University, Newcastle upon Tyne, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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Tárnoki ÁD, Tárnoki DL, Dąbrowska M, Knetki-Wróblewska M, Frille A, Stubbs H, Blyth KG, Juul AD. New developments in the imaging of lung cancer. Breathe (Sheff) 2024; 20:230176. [PMID: 38595936 PMCID: PMC11003524 DOI: 10.1183/20734735.0176-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/25/2024] [Indexed: 04/11/2024] Open
Abstract
Radiological and nuclear medicine methods play a fundamental role in the diagnosis and staging of patients with lung cancer. Imaging is essential in the detection, characterisation, staging and follow-up of lung cancer. Due to the increasing evidence, low-dose chest computed tomography (CT) screening for the early detection of lung cancer is being introduced to the clinical routine in several countries. Radiomics and radiogenomics are emerging fields reliant on artificial intelligence to improve diagnosis and personalised risk stratification. Ultrasound- and CT-guided interventions are minimally invasive methods for the diagnosis and treatment of pulmonary malignancies. In this review, we put more emphasis on the new developments in the imaging of lung cancer.
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Affiliation(s)
- Ádám Domonkos Tárnoki
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
- National Tumour Biology Laboratory, Oncologic Imaging and Invasive Diagnostic Centre, National Institute of Oncology, Budapest, Hungary
| | - Dávid László Tárnoki
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
- National Tumour Biology Laboratory, Oncologic Imaging and Invasive Diagnostic Centre, National Institute of Oncology, Budapest, Hungary
| | - Marta Dąbrowska
- Department of Internal Medicine, Pulmonary Diseases and Allergy, Medical University of Warsaw, Warsaw, Poland
| | | | - Armin Frille
- Department of Respiratory Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Harrison Stubbs
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Kevin G. Blyth
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
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Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark 2024:CBM230360. [PMID: 38427470 DOI: 10.3233/cbm-230360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.
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Lin CY, Guo SM, Lien JJJ, Lin WT, Liu YS, Lai CH, Hsu IL, Chang CC, Tseng YL. Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT. LA RADIOLOGIA MEDICA 2024; 129:56-69. [PMID: 37971691 PMCID: PMC10808169 DOI: 10.1007/s11547-023-01730-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 09/21/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. MATERIALS AND METHODS The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task. RESULTS The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%. CONCLUSION Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.
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Affiliation(s)
- Chia-Ying Lin
- Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Shu-Mei Guo
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Jenn-Jier James Lien
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Wen-Tsen Lin
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Yi-Sheng Liu
- Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Chao-Han Lai
- Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - I-Lin Hsu
- Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan, R.O.C..
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan, R.O.C
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10
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Craig DJ, Crawford EL, Chen H, Grogan EL, Deppen SA, Morrison T, Antic SL, Massion PP, Willey JC. TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk. BMC Cancer 2023; 23:783. [PMID: 37612638 PMCID: PMC10464352 DOI: 10.1186/s12885-023-11266-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case-control cohort. METHODS Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA. RESULTS TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749-0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846-0.986) and this was significantly higher than that for either factor alone (P < 0.03). CONCLUSIONS These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen.
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Affiliation(s)
- Daniel J Craig
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA
| | - Erin L Crawford
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA
| | - Heidi Chen
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Eric L Grogan
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
- Tennessee Valley VA Healthcare System, 1310 24Th Avenue South, Nashville, TN, 37212, USA
| | - Steven A Deppen
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Thomas Morrison
- Accugenomics Inc, 1410 Commonwealth Dr #105, Wilmington, NC, 28403, USA
| | - Sanja L Antic
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Pierre P Massion
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - James C Willey
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA.
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11
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Pritchett MA, Sigal B, Bowling MR, Kurman JS, Pitcher T, Springmeyer SC. Assessing a biomarker's ability to reduce invasive procedures in patients with benign lung nodules: Results from the ORACLE study. PLoS One 2023; 18:e0287409. [PMID: 37432960 DOI: 10.1371/journal.pone.0287409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/05/2023] [Indexed: 07/13/2023] Open
Abstract
A blood-based integrated classifier (IC) has been clinically validated to improve accuracy in assessing probability of cancer risk (pCA) for pulmonary nodules (PN). This study evaluated the clinical utility of this biomarker for its ability to reduce invasive procedures in patients with pre-test pCA ≤ 50%. This was a propensity score matching (PSM) cohort study comparing patients in the ORACLE prospective, multicenter, observational registry to control patients treated with usual care. This study enrolled patients meeting the intended use criteria for IC testing: pCA ≤ 50%, age ≥40 years, nodule diameter 8-30 mm, and no history of lung cancer and/or active cancer (except for non-melanomatous skin cancer) within 5 years. The primary aim of this study was to evaluate invasive procedure use on benign PNs of registry patients as compared to control patients. A total of 280 IC tested, and 278 control patients met eligibility and analysis criteria and 197 were in each group after PSM (IC and control groups). Patients in the IC group were 74% less likely to undergo an invasive procedure as compared to the control group (absolute difference 14%, p <0.001) indicating that for every 7 patients tested, one unnecessary invasive procedure was avoided. Invasive procedure reduction corresponded to a reduction in risk classification, with 71 patients (36%) in the IC group classified as low risk (pCA < 5%). The proportion of IC group patients with malignant PNs sent to surveillance were not statistically different than the control group, 7.5% vs 3.5% for the IC vs. control groups, respectively (absolute difference 3.91%, p 0.075). The IC for patients with a newly discovered PN has demonstrated valuable clinical utility in a real-world setting. Use of this biomarker can change physicians' practice and reduce invasive procedures in patients with benign pulmonary nodules. Trial registration: Clinical trial registration: ClinicalTrials.gov NCT03766958.
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Affiliation(s)
- Michael A Pritchett
- Department of Pulmonary Medicine, FirstHealth of the Carolinas & Pinehurst Medical Clinic, Pinehurst, North Carolina, United States of America
| | - Barry Sigal
- Southeastern Research Center, Winston-Salem, North Carolina, United States of America
| | - Mark R Bowling
- Division of Pulmonary, Critical Care, and Sleep Medicine, Brody School of Medicine, Eastern Carolina University, Greenville, North Carolina, United States of America
| | - Jonathan S Kurman
- Division of Critical Care Medicine, Interventional Pulmonology, Pulmonary Disease, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Trevor Pitcher
- Medical Affairs, Biodesix, Inc., Boulder, Colorado, United States of America
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Lastwika KJ, Wu W, Zhang Y, Ma N, Zečević M, Pipavath SNJ, Randolph TW, Houghton AM, Nair VS, Lampe PD, Kinahan PE. Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment. Cancers (Basel) 2023; 15:3418. [PMID: 37444527 PMCID: PMC10341085 DOI: 10.3390/cancers15133418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.
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Affiliation(s)
- Kristin J. Lastwika
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Wei Wu
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Yuzheng Zhang
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - Ningxin Ma
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
| | - Mladen Zečević
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Sudhakar N. J. Pipavath
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Timothy W. Randolph
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - A. McGarry Houghton
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Viswam S. Nair
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Paul D. Lampe
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
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13
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Lee K, Liu Z, Chandran U, Kalsekar I, Laxmanan B, Higashi MK, Jun T, Ma M, Li M, Mai Y, Gilman C, Wang T, Ai L, Aggarwal P, Pan Q, Oh W, Stolovitzky G, Schadt E, Wang X. Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing. JMIR AI 2023; 2:e44537. [PMID: 38875565 PMCID: PMC11041451 DOI: 10.2196/44537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 03/31/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes. OBJECTIVE We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.
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Affiliation(s)
| | | | - Urmila Chandran
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | - Iftekhar Kalsekar
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | - Balaji Laxmanan
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | | | - Tomi Jun
- Sema4, Stamford, CT, United States
| | - Meng Ma
- Sema4, Stamford, CT, United States
| | | | - Yun Mai
- Sema4, Stamford, CT, United States
| | | | | | - Lei Ai
- Sema4, Stamford, CT, United States
| | | | - Qi Pan
- Sema4, Stamford, CT, United States
| | - William Oh
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Eric Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Lyew MA, Morris C, Smith K, Stennett M. Case report: Colonic actinomycosis - A rare cause of a locally advanced colonic tumour. Int J Surg Case Rep 2023; 105:107957. [PMID: 36907045 PMCID: PMC10025125 DOI: 10.1016/j.ijscr.2023.107957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
INTRODUCTION AND IMPORTANCE Colon cancer is a common malignancy and is often encountered initially as locally advanced disease. However, there are many benign clinical entities that may masquerade as complicated colonic malignancy. Abdominal actinomycosis is one such rare mimic. CASE PRESENTATION A 48-year-old female presented with a progressively enlarging abdominal mass with skin involvement and clinical features of partial large bowel obstruction. Computed tomography (CT) revealed a mid-transverse colonic lesion at the centre of an inflammatory phlegmon. At laparotomy, the mass was found to be adherent to the anterior abdominal wall, gastrocolic omentum, and loops of jejunum. En block resection was performed with primary anastomosis. Final histology showed no evidence of malignancy, but mural abscesses containing pathognomonic sulphur granules and actinomyces species. CLINICAL DISCUSSION Abdominal actinomycosis, particularly of the colon, is rare and exceptionally so in immunocompetent patients. However, the clinical and radiographic presentation often mimics more common conditions such as colon cancer. Accordingly, surgical resection is typically radical to clear margins, and diagnosis is made only on final histopathology. CONCLUSION Colonic actinomycosis is an uncommon infection but the diagnosis should be considered particularly in colonic masses with anterior abdominal wall involvement. Oncologic resection remains the mainstay of treatment and the diagnosis commonly made retrospectively given the rarity of the condition.
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Affiliation(s)
- Matthew-Anthony Lyew
- Department of General Surgery, Kingston Public Hospital, Kingston, Jamaica; Department of Surgery, Radiology, Anaesthesia and Intensive Care, University Hospital of the West Indies, Mona, Jamaica.
| | - Conrad Morris
- Department of General Surgery, Kingston Public Hospital, Kingston, Jamaica
| | - Kevan Smith
- Department of General Surgery, Kingston Public Hospital, Kingston, Jamaica
| | - Memory Stennett
- Department of Pathology, National Public Health Laboratory, Kingston, Jamaica
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15
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Shu Q, Wang Y, Deng M, Chen X, Liu M, Cai L. Benign lesions with 68Ga-FAPI uptake: a retrospective study. Br J Radiol 2023; 96:20220994. [PMID: 36715164 PMCID: PMC10078866 DOI: 10.1259/bjr.20220994] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/15/2022] [Accepted: 01/11/2023] [Indexed: 01/31/2023] Open
Abstract
OBJECTIVES Although FAPI, as a pan-tumor tracer, shows high expression in the malignancy imaging, FAPI uptake is also seen in some benign lesions. The purpose of this study was to retrospectively analyze the characteristics of benign lesions with FAPI uptake on 68Ga-FAPI PET/CT imaging. METHODS The electronic medical and imaging records of patients undergoing 68Ga-FAPI PET/CT imaging in the Department of Nuclear Medicine of our hospital from March 2020 to March 2022 were retrospectively analyzed. Patients with benign lesions confirmed by histopathological analysis or long-term follow-up of FAPI-positive lesions were included in the study. RESULTS A total of 44 patients (i.e., 44 benign lesions) were included in this study, including 14 women and 30 men, ranging in age from 19 to 74 years. Benign lesions involved eight systems, including liver (n = 3), tail of pancreas (n = 3), stomach (n = 3), esophagus (n = 1), lung (n = 14), and mediastinum (n = 2), sinuses (n = 1), brain (n = 2), lymph nodes (n = 5), kidneys (n = 4), bones (n = 2), muscles (n = 1), thyroid (n = 1), parathyroid gland (n = 1), and breast (n = 1). The mean SUVmax (p = 0.471) and mean TBR (p = 0.830) of benign lesions in the eight systems were not significantly different. CONCLUSION Our studies have shown that in addition to malignant tumors, certain benign lesions also show uptake of FAPI, and it is necessary for doctors to distinguish these benign lesions from true malignant tumors. ADVANCES IN KNOWLEDGE Benign lesions may also show FAPI expression, which may make the differential diagnosis of benign and malignant lesions difficult and should be alerted by physicians.
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16
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Shen Z, Ouyang X, Xiao B, Cheng JZ, Shen D, Wang Q. Image synthesis with disentangled attributes for chest X-ray nodule augmentation and detection. Med Image Anal 2023; 84:102708. [PMID: 36516554 DOI: 10.1016/j.media.2022.102708] [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: 04/10/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR images. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the shape/size attributes desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including the shape, the size, and the texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarse-to-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation strategy on greatly improving nodule detection performance.
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Affiliation(s)
- Zhenrong Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xi Ouyang
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Bin Xiao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie-Zhi Cheng
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Dinggang Shen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China
| | - Qian Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China.
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17
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Yankelevitz DF, Yip R, Henschke CI. Impact of Duration of Diagnostic Workup on Prognosis for Early Lung Cancer. J Thorac Oncol 2023; 18:527-537. [PMID: 36642158 DOI: 10.1016/j.jtho.2022.12.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/18/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Growth assessment for pulmonary nodules is an important diagnostic tool; however, the impact on prognosis due to time delay for follow-up diagnostic scans needs to be considered. METHODS Using the data between 2003 and 2019 from the International Early Lung Cancer Action Program, a prospective cohort study, we determined the size-specific, 10-year Kaplan-Meier lung cancer (LC) survival rates as surrogates for cure rates. We estimated the change in LC diameter after delays of 90, 180, and 365 days using three representative LC volume doubling times (VDTs) of 60 (fast), 120 (moderate), and 240 (slow). We then estimated the decrease in the LC cure rate resulting from time between computed tomography scans to assess for growth during the diagnostic workup. RESULTS Using a regression model of the 10-year LC survival rates on LC diameter, the estimated LC cure rate of a 4.0 mm LC with fast (60-d) VDT is 96.0% (95% confidence interval [CI]: 95.2%-96.7%) initially, but it would decrease to 94.3% (95% CI: 93.2%-95.0%), 92.0% (95% CI: 90.5%-93.4%), and 83.6%(95% CI: 80.6%-86.6%) after delays of 90, 180, and 365 days, respectively. A 20.0-mm LC with the same VDTs has a lower LC cure rate of 79.9% (95% CI: 76.2%-83.5%) initially and decreases more rapidly to 71.5% (95% CI: 66.4%-76.7%), 59.8% (95% CI: 52.4%-67.1%), and 17.9% (95% CI: 3.0%-32.8%) after the same delays of 90, 180, and 365 days, respectively. CONCLUSIONS Time between scans required to measure growth of lung nodules affects prognosis with the effect being greater for fast growing and larger cancers. Quantifying the extent of change in prognosis is required to understand efficiencies of different management protocols.
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Affiliation(s)
- David F Yankelevitz
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Rowena Yip
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Claudia I Henschke
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
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Chao HS, Tsai CY, Chou CW, Shiao TH, Huang HC, Chen KC, Tsai HH, Lin CY, Chen YM. Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial. Biomedicines 2023; 11:biomedicines11010147. [PMID: 36672655 PMCID: PMC9856020 DOI: 10.3390/biomedicines11010147] [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: 11/19/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
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Affiliation(s)
- Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chiao-Yun Tsai
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chung-Wei Chou
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hsu-Chih Huang
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Kun-Chieh Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou 545301, Taiwan
| | - Hao-Hung Tsai
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- School of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chin-Yu Lin
- Institute of New Drug Development, College of Medicine, China Medical University, Taichung 40402, Taiwan
- Tsuzuki Institute for Traditional Medicine, College of Pharmacy, China Medical University, Taichung 40402, Taiwan
- Department for Biomedical Engineering, Collage of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121 (ext. 7865)
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Anaya-Isaza A, Mera-Jiménez L, Verdugo-Alejo L, Sarasti L. Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network. Eur J Radiol Open 2023; 10:100484. [PMID: 36950474 PMCID: PMC10027502 DOI: 10.1016/j.ejro.2023.100484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
Early detection and diagnosis of brain tumors are crucial to taking adequate preventive measures, as with most cancers. On the other hand, artificial intelligence (AI) has grown exponentially, even in such complex environments as medicine. Here it's proposed a framework to explore state-of-the-art deep learning architectures for brain tumor classification and detection. An own development called Cross-Transformer is also included, which consists of three scalar products that combine self-care model keys, queries, and values. Initially, we focused on the classification of three types of tumors: glioma, meningioma, and pituitary. With the Figshare brain tumor dataset was trained the InceptionResNetV2, InceptionV3, DenseNet121, Xception, ResNet50V2, VGG19, and EfficientNetB7 networks. Over 97 % of classifications were accurate in this experiment, which provided a network's performance overview. Subsequently, we focused on tumor detection using the Brain MRI Images for Brain Tumor Detection and The Cancer Genome Atlas Low-Grade Glioma database. The development encompasses learning transfer, data augmentation, as well as image acquisition sequences; T1-weighted images (T1WI), T1-weighted post-gadolinium (T1-Gd), and Fluid-Attenuated Inversion Recovery (FLAIR). Based on the results, using learning transfer and data augmentation increased accuracy by up to 6 %, with a p-value below the significance level of 0.05. As well, the FLAIR sequence was the most efficient for detection. As an alternative, our proposed model proved to be the most effective in terms of training time, using approximately half the time of the second fastest network.
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20
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Gao R, Li T, Tang Y, Xu K, Khan M, Kammer M, Antic SL, Deppen S, Huo Y, Lasko TA, Sandler KL, Maldonado F, Landman BA. Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model. Comput Biol Med 2022; 150:106113. [PMID: 36198225 PMCID: PMC10050219 DOI: 10.1016/j.compbiomed.2022.106113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/21/2022] [Accepted: 09/17/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the pretest probability of lung cancer. In this study, we apply a deep learning network to integrate multi-modal data from CT images and clinical data (including blood-based biomarkers) to improve lung cancer diagnosis. Our goal is to reduce uncertainty and to avoid morbidity, mortality, over- and undertreatment of patients with IPNs. METHOD We use a retrospective study design with cross-validation and external-validation from four different sites. We introduce a deep learning framework with a two-path structure to learn from CT images and clinical data. The proposed model can learn and predict with single modality if the multi-modal data is not complete. We use 1284 patients in the learning cohort for model development. Three external sites (with 155, 136 and 96 patients, respectively) provided patient data for external validation. We compare our model to widely applied clinical prediction models (Mayo and Brock models) and image-only methods (e.g., Liao et al. model). RESULTS Our co-learning model improves upon the performance of clinical-factor-only (Mayo and Brock models) and image-only (Liao et al.) models in both cross-validation of learning cohort (e.g. , AUC 0.787 (ours) vs. 0.707-0.719 (baselines), results reported in validation fold and external-validation using three datasets from University of Pittsburgh Medical Center (e.g., 0.918 (ours) vs. 0.828-0.886 (baselines)), Detection of Early Cancer Among Military Personnel (e.g., 0.712 (ours) vs. 0.576-0.709 (baselines)), and University of Colorado Denver (e.g., 0.847 (ours) vs. 0.679-0.746 (baselines)). In addition, our model achieves better re-classification performance (cNRI 0.04 to 0.20) in all cross- and external-validation sets compared to the Mayo model. CONCLUSIONS Lung cancer risk estimation in patients with IPNs can benefit from the co-learning of CT image and clinical data. Learning from more subjects, even though those only have a single modality, can improve the prediction accuracy. An integrated deep learning model can achieve reasonable discrimination and re-classification performance.
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Affiliation(s)
- Riqiang Gao
- Vanderbilt University, Nashville, TN, 37235, USA.
| | - Thomas Li
- Vanderbilt University, Nashville, TN, 37235, USA
| | - Yucheng Tang
- Vanderbilt University, Nashville, TN, 37235, USA
| | - Kaiwen Xu
- Vanderbilt University, Nashville, TN, 37235, USA
| | - Mirza Khan
- Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | - Michael Kammer
- Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | - Sanja L Antic
- Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | - Stephen Deppen
- Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | - Yuankai Huo
- Vanderbilt University, Nashville, TN, 37235, USA
| | - Thomas A Lasko
- Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | - Kim L Sandler
- Vanderbilt University Medical Center, Nashville, TN, 37235, USA
| | | | - Bennett A Landman
- Vanderbilt University, Nashville, TN, 37235, USA; Vanderbilt University Medical Center, Nashville, TN, 37235, USA
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Raval AA, Benn BS, Benzaquen S, Maouelainin N, Johnson M, Huang J, Lofaro LR, Ansari A, Geurink C, Kennedy GC, Bulman WA, Kurman JS. Reclassification of risk of malignancy with Percepta Genomic Sequencing Classifier following nondiagnostic bronchoscopy. Respir Med 2022; 204:106990. [DOI: 10.1016/j.rmed.2022.106990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/30/2022] [Accepted: 09/11/2022] [Indexed: 10/31/2022]
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Zhao G, Feng Q, Chen C, Zhou Z, Yu Y. Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:7400-7416. [PMID: 34822325 DOI: 10.1109/tpami.2021.3130759] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
During clinical practice, radiologists often use attributes, e.g., morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. Our method also achieves a 3.24% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data.
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Kammer MN, Rowe DJ, Deppen SA, Grogan EL, Kaizer AM, Barón AE, Maldonado F. The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers. Cancer Epidemiol Biomarkers Prev 2022; 31:1752-1759. [PMID: 35732292 PMCID: PMC9491691 DOI: 10.1158/1055-9965.epi-22-0190] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 05/11/2022] [Accepted: 06/16/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect. METHODS Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets. RESULTS Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 > 0.97 for each) with fitted parameters closely aligned with professional society guidelines. CONCLUSIONS The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice. IMPACT We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.
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Affiliation(s)
| | - Dianna J Rowe
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephen A Deppen
- Vanderbilt University Medical Center, Nashville, Tennessee.,Tennessee Valley Healthcare Administration Nashville Campus, Nashville, Tennessee
| | - Eric L Grogan
- Vanderbilt University Medical Center, Nashville, Tennessee.,Tennessee Valley Healthcare Administration Nashville Campus, Nashville, Tennessee
| | - Alexander M Kaizer
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Anna E Barón
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
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24
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Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology 2022; 304:683-691. [PMID: 35608444 PMCID: PMC9434821 DOI: 10.1148/radiol.212182] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 03/02/2022] [Accepted: 03/16/2022] [Indexed: 12/25/2022]
Abstract
Background Limited data are available regarding whether computer-aided diagnosis (CAD) improves assessment of malignancy risk in indeterminate pulmonary nodules (IPNs). Purpose To evaluate the effect of an artificial intelligence-based CAD tool on clinician IPN diagnostic performance and agreement for both malignancy risk categories and management recommendations. Materials and Methods This was a retrospective multireader multicase study performed in June and July 2020 on chest CT studies of IPNs. Readers used only CT imaging data and provided an estimate of malignancy risk and a management recommendation for each case without and with CAD. The effect of CAD on average reader diagnostic performance was assessed using the Obuchowski-Rockette and Dorfman-Berbaum-Metz method to calculate estimates of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Multirater Fleiss κ statistics were used to measure interobserver agreement for malignancy risk and management recommendations. Results A total of 300 chest CT scans of IPNs with maximal diameters of 5-30 mm (50.0% malignant) were reviewed by 12 readers (six radiologists, six pulmonologists) (patient median age, 65 years; IQR, 59-71 years; 164 [55%] men). Readers' average AUC improved from 0.82 to 0.89 with CAD (P < .001). At malignancy risk thresholds of 5% and 65%, use of CAD improved average sensitivity from 94.1% to 97.9% (P = .01) and from 52.6% to 63.1% (P < .001), respectively. Average reader specificity improved from 37.4% to 42.3% (P = .03) and from 87.3% to 89.9% (P = .05), respectively. Reader interobserver agreement improved with CAD for both the less than 5% (Fleiss κ, 0.50 vs 0.71; P < .001) and more than 65% (Fleiss κ, 0.54 vs 0.71; P < .001) malignancy risk categories. Overall reader interobserver agreement for management recommendation categories (no action, CT surveillance, diagnostic procedure) also improved with CAD (Fleiss κ, 0.44 vs 0.52; P = .001). Conclusion Use of computer-aided diagnosis improved estimation of indeterminate pulmonary nodule malignancy risk on chest CT scans and improved interobserver agreement for both risk stratification and management recommendations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.
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Affiliation(s)
- Roger Y. Kim
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Jason L. Oke
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Lyndsey C. Pickup
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Reginald F. Munden
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Travis L. Dotson
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Christina R. Bellinger
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Avi Cohen
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Michael J. Simoff
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Pierre P. Massion
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Claire Filippini
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Fergus V. Gleeson
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Anil Vachani
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
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Diagnostic value of platelet-to-lymphocyte ratio in patients with solitary pulmonary nodules. KARDIOCHIRURGIA I TORAKOCHIRURGIA POLSKA = POLISH JOURNAL OF CARDIO-THORACIC SURGERY 2022; 19:117-121. [PMID: 36268479 PMCID: PMC9574589 DOI: 10.5114/kitp.2022.119758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/28/2022] [Indexed: 11/05/2022]
Abstract
Introduction Nodules detected in the lung parenchyma should be considered as malignant until proven otherwise, and the necessary tests should be performed for diagnosis. Aim To calculate the preoperative platelet-to-lymphocyte ratio (PLR) in patients with malignant lung nodules and to investigate the diagnostic value of this ratio in determining the histopathology of the nodule. Material and methods Ninety-one patients who were operated on for a malignant nodule in the lung between September 2010 and September 2020 were included in the study. The PLR was calculated by dividing the absolute platelet count by the absolute lymphocyte count. These values were compared with the histopathological diagnoses of the resected tumor tissue. Patients with primary lung malignancy were classified as group 1 (n = 54), and lung metastases of other organs were classified as group 2 (n = 37). Results The mean PLR was 127.27 ±46.82 in the first group and 183.56 ±93.49 in the second group. There was a statistically significant difference in PLR values between the two groups, and PLR was higher in group 2. There was no statistically significant difference between the two groups in terms of lymph node positivity, nodule size and SuvMax values. A moderately strong, significant and same-sided correlation was observed between nodule size and SuvMax values in the first group of patients (r = 0.48, p = 0.001) Conclusions PLR values less than 89.41 indicate that the histopathological result may be a lung-derived malignancy. However, in cases where the PLR is detected above 165.6, it would be appropriate to interpret another previously detected malignancy as metastasis to the lung.
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Marmor HN, Jackson L, Gawel S, Kammer M, Massion PP, Grogan EL, Davis GJ, Deppen SA. Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers. Clin Chim Acta 2022; 534:106-114. [PMID: 35870539 PMCID: PMC10057862 DOI: 10.1016/j.cca.2022.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/05/2022] [Accepted: 07/12/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. METHODS Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. RESULTS Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. CONCLUSIONS A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.
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Affiliation(s)
- Hannah N Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA.
| | - Laurel Jackson
- Abbott Diagnostics Division, 100 Abbott Park Road, Abbott Park, IL 60064, USA.
| | - Susan Gawel
- Abbott Diagnostics Division, 100 Abbott Park Road, Abbott Park, IL 60064, USA.
| | - Michael Kammer
- Department of Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA.
| | - Pierre P Massion
- Department of Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA; Tennessee Valley Healthcare System, Veterans Affairs, 1310 24th Avenue South, Nashville, TN 37212, USA
| | - Gerard J Davis
- Abbott Diagnostics Division, 100 Abbott Park Road, Abbott Park, IL 60064, USA.
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA; Tennessee Valley Healthcare System, Veterans Affairs, 1310 24th Avenue South, Nashville, TN 37212, USA.
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Liu F, Dai L, Wang Y, Liu M, Wang M, Zhou Z, Qi Y, Chen R, OuYang S, Fan Q. Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy. J Surg Oncol 2022; 126:1551-1559. [PMID: 35993806 DOI: 10.1002/jso.27072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/15/2022] [Accepted: 08/12/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included. METHODS A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn. RESULTS There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22. CONCLUSIONS We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.
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Affiliation(s)
- Fenghui Liu
- Department of Respiratory and Sleep Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Liping Dai
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Yulin Wang
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Man Liu
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Meng Wang
- Department of Imaging and Nuclear Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhigang Zhou
- Department of Imaging and Nuclear Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Yu Qi
- Department of Thoracic Surgery in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Ruiying Chen
- Department of Respiratory and Sleep Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Songyun OuYang
- Department of Respiratory and Sleep Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Qingxia Fan
- Department of Oncology in the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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The Chain of Adherence for Incidentally Detected Pulmonary Nodules after an Initial Radiologic Imaging Study: A Multisystem Observational Study. Ann Am Thorac Soc 2022; 19:1379-1389. [PMID: 35167780 DOI: 10.1513/annalsats.202111-1220oc] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Rationale: Millions of people are diagnosed with incidental pulmonary nodules every year. Although most nodules are benign, it is universally recommended that all patients be assessed to determine appropriate follow-up and ensure that it is obtained. Objectives: To determine the degree of concordance and adherence to 2005 Fleischner Society guidelines among radiologists, clinicians, and patients at two Veterans Affairs healthcare systems with incidental nodule tracking systems. Methods: Trained researchers abstracted data from the electronic health records of patients with incidental pulmonary nodules as identified by interpreting radiologists from 2008 to 2016. We classified radiology reports and patient follow-up into three categories. Radiologist-Fleischner adherence was the agreement between the radiologist's recommendation in the computed tomography (CT) report and the 2005 Fleischner Society guidelines. Clinician/patient-Fleischner concordance was agreement between patient follow-up and the guidelines. Clinician/patient-radiologist adherence was agreement between the radiologist's recommendation and patient follow-up. We evaluated whether the recommendation or follow-up was more (e.g., sooner) or less (e.g., later) aggressive than recommended. Results: After exclusions, 4,586 patients with 7,408 imaging tests (n = 4,586 initial chest CT scans; n = 2,717 follow-up chest CT scans; n = 105 follow-up low-dose CT scans) were included. Among radiology reports that could be classified in terms of Fleischner Society guidelines (n = 3,150), 80% had nonmissing radiologist recommendations. Among those reports, radiologist-Fleischner adherence was 86.6%, with 4.8% more aggressive and 8.6% less aggressive. Among patients whose initial scans could be classified, clinician/patient-Fleischner concordance was 46.0%, 14.5% were more aggressive, and 39.5% were less aggressive. Clinician/patient-radiologist adherence was 54.3%. Veterans whose radiology reports were adherent to Fleischner Society guidelines had a substantially higher proportion of clinician/patient-Fleischner concordance: 52.0% concordance among radiologist-Fleischner adherent versus 11.6% concordance among radiologist-Fleischner nonadherent. Conclusions: In this multi-health system observational study of incidental pulmonary nodule follow-up, we found that radiologist adherence to 2005 Fleischner Society guidelines may be necessary but not sufficient. Our results highlight the many facets of care processes that must occur to achieve guideline-concordant care.
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Yanagawa M. Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT. Radiology 2022; 304:692-693. [PMID: 35608448 DOI: 10.1148/radiol.220571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Masahiro Yanagawa
- From the Department of Radiology, Osaka University Graduate School of Medicine, Yamadaoka, 2-2 Suita, Osaka 565-0871, Japan
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30
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Shi F, Chen B, Cao Q, Wei Y, Zhou Q, Zhang R, Zhou Y, Yang W, Wang X, Fan R, Yang F, Chen Y, Li W, Gao Y, Shen D. Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:771-781. [PMID: 34705640 DOI: 10.1109/tmi.2021.3123572] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.
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31
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Grosu HB, Kern R, Maldonado F, Casal R, Andersen CR, Li L, Eapen G, Ost D, Jimenez C, Frangopoulos F, Sabath B, Vakil E, Schwalk A, Marcoux M, Sagar AE, Nasim F, Lin J, Salahudin M, Arain HM, Noor L, Montanez D, Stewart J, Mullon J, Michael M, Porfyridis I. Predicting malignant pleural effusion during diagnostic pleuroscopy with biopsy: A prospective multicentre study. Respirology 2022; 27:350-356. [PMID: 35178828 DOI: 10.1111/resp.14232] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/12/2022] [Accepted: 02/08/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Pleuroscopy with pleural biopsy has a high sensitivity for malignant pleural effusion (MPE). Because MPEs tend to recur, concurrent diagnosis and treatment of MPE during pleuroscopy is desired. However, proceeding directly to treatment at the time of pleuroscopy requires confidence in the on-site diagnosis. The study's primary objective was to create a predictive model to estimate the probability of MPE during pleuroscopy. METHODS A prospective observational multicentre cohort study of consecutive patients undergoing pleuroscopy was conducted. We used a logistic regression model to evaluate the probability of MPE with relation to visual assessment, rapid on-site evaluation (ROSE) of touch preparation and presence of pleural nodules/masses on computed tomography (CT). To assess the model's prediction accuracy, a bootstrapped training/testing approach was utilized to estimate the cross-validated area under the receiver operating characteristic curve. RESULTS Of the 201 patients included in the study, 103 had MPE. Logistic regression showed that higher level of malignancy on visual assessment is associated with higher odds of MPE (OR = 34.68, 95% CI = 9.17-131.14, p < 0.001). The logistic regression also showed that higher level of malignancy on ROSE of touch preparation is associated with higher odds of MPE (OR = 11.63, 95% CI = 3.85-35.16, p < 0.001). Presence of pleural nodules/masses on CT is associated with higher odds of MPE (OR = 6.61, 95% CI = 1.97-22.1, p = 0.002). A multivariable logistic regression model of final pathologic status with relation to visual assessment, ROSE of touch preparation and presence of pleural nodules/masses on CT had a cross-validated AUC of 0.94 (95% CI = 0.91-0.97). CONCLUSION A prediction model using visual assessment, ROSE of touch preparation and CT scan findings demonstrated excellent predictive accuracy for MPE. Further validation studies are needed to confirm our findings.
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Affiliation(s)
- Horiana B Grosu
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ryan Kern
- Pulmonary Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Fabien Maldonado
- Division of Allergy, Pulmonary And Critical Care Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Roberto Casal
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clark R Andersen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Georgie Eapen
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David Ost
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos Jimenez
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Bruce Sabath
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Erik Vakil
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Audra Schwalk
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mathieu Marcoux
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ala Eddin Sagar
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Faria Nasim
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Julie Lin
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Moiz Salahudin
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hasan Muhammad Arain
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laila Noor
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Diana Montanez
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - John Stewart
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - John Mullon
- Pulmonary Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Michalis Michael
- Cytopathology Department, Nicosia General Hospital, Nicosia, Cyprus
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Sethi S, Oh S, Chen A, Bellinger C, Lofaro L, Johnson M, Huang J, Bhorade SM, Bulman W, Kennedy GC. Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study. BMC Pulm Med 2022; 22:26. [PMID: 34991528 PMCID: PMC8740045 DOI: 10.1186/s12890-021-01772-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 11/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to "very high risk" with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence. METHODS Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision. RESULTS One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists' confidence in decision-making following a nondiagnostic bronchoscopy. CONCLUSIONS Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.
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Affiliation(s)
- Sonali Sethi
- Division of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, 9500 Euclid Avenue, Mail Code A90, Cleveland, OH, 44195, USA.
| | - Scott Oh
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alexander Chen
- Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Christina Bellinger
- Pulmonary, Critical Care, Allergy and Immunologic Disease, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lori Lofaro
- Veracyte, Inc., South San Francisco, CA, USA
| | | | - Jing Huang
- Veracyte, Inc., South San Francisco, CA, USA
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Qiu Z, Wu Q, Wang S, Chen Z, Lin F, Zhou Y, Jin J, Xian J, Tian J, Li W. Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging. Thorac Cancer 2022; 13:602-612. [PMID: 34994091 PMCID: PMC8841714 DOI: 10.1111/1759-7714.14305] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/17/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023] Open
Abstract
Background Early identification of the malignant propensity of pulmonary ground‐glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning‐based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs. Methods This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time‐point CT scans. We developed a deep learning‐based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models. Results The deep learning model that used integrated DL‐features from initial and follow‐up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component. Conclusions Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.
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Affiliation(s)
- Zhixin Qiu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qingxia Wu
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Zhixia Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Feng Lin
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuyan Zhou
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Jin
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jinghong Xian
- Department of Clinical Research, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Tian
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
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A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones. Noncoding RNA 2021; 7:ncrna7040080. [PMID: 34940762 PMCID: PMC8709422 DOI: 10.3390/ncrna7040080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/04/2021] [Accepted: 12/07/2021] [Indexed: 12/19/2022] Open
Abstract
The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.
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35
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Kammer MN, Lakhani DA, Balar AB, Antic SL, Kussrow AK, Webster RL, Mahapatra S, Barad U, Shah C, Atwater T, Diergaarde B, Qian J, Kaizer A, New M, Hirsch E, Feser WJ, Strong J, Rioth M, Miller YE, Balagurunathan Y, Rowe DJ, Helmey S, Chen SC, Bauza J, Deppen SA, Sandler K, Maldonado F, Spira A, Billatos E, Schabath MB, Gillies RJ, Wilson DO, Walker RC, Landman B, Chen H, Grogan EL, Barón AE, Bornhop DJ, Massion PP. Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules. Am J Respir Crit Care Med 2021; 204:1306-1316. [PMID: 34464235 PMCID: PMC8786067 DOI: 10.1164/rccm.202012-4438oc] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 08/27/2021] [Indexed: 01/06/2023] Open
Abstract
Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.
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Affiliation(s)
- Michael N. Kammer
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
- Department of Chemistry, and
| | - Dhairya A. Lakhani
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Aneri B. Balar
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Sanja L. Antic
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Amanda K. Kussrow
- Department of Chemistry, and
- Vanderbilt Institute for Chemical Biology, Nashville, Tennessee
- Vanderbilt Ingram Cancer Center, Nashville, Tennessee
| | | | - Shayan Mahapatra
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | | | | | - Thomas Atwater
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Brenda Diergaarde
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh and UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania
| | - Jun Qian
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Alexander Kaizer
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | | | - Erin Hirsch
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - William J. Feser
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Jolene Strong
- Biomedical Informatics and Personalized Medicine, and
| | - Matthew Rioth
- Medical Oncology and Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado, Aurora, Colorado
| | | | | | - Dianna J. Rowe
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Sherif Helmey
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Sheau-Chiann Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Joseph Bauza
- American College of Radiology, Philadelphia, Pennsylvania
| | - Stephen A. Deppen
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Kim Sandler
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Fabien Maldonado
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Avrum Spira
- Department of Medicine, Boston University, Boston, Massachusetts
| | - Ehab Billatos
- Department of Medicine, Boston University, Boston, Massachusetts
| | | | | | - David O. Wilson
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; and
| | | | - Bennett Landman
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Heidi Chen
- American College of Radiology, Philadelphia, Pennsylvania
| | - Eric L. Grogan
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
| | - Anna E. Barón
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Darryl J. Bornhop
- Department of Chemistry, and
- Vanderbilt Institute for Chemical Biology, Nashville, Tennessee
- Vanderbilt Ingram Cancer Center, Nashville, Tennessee
| | - Pierre P. Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine
- Vanderbilt Ingram Cancer Center, Nashville, Tennessee
- Pulmonary Section, Medical Service, Tennessee Valley Healthcare Systems Nashville Campus, Nashville, Tennessee
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Hou H, Yu S, Xu Z, Zhang H, Liu J, Zhang W. Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels. Eur J Cancer Prev 2021; 30:382-388. [PMID: 33284149 PMCID: PMC8322042 DOI: 10.1097/cej.0000000000000637] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 09/17/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels. METHODS Totally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS Differences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P < 0.05). Influencing factors for malignancy included age, gender, nodule location, spiculation, vacuole sign and CEA (all P < 0.05). The established model was as follows: Y = -5.368 + 0.055 × age + 1.012 × gender (female = 1, male = 0) + 1.302 × nodule location (right upper lobe = 1, others = 0) + 1.208 × spiculation (yes = 1, no = 0) + 2.164 × vacuole sign (yes = 1, no = 0) -0.054 × CEA. The AUC of the model with CEA was 0.818 (95% confidence interval, 0.763-0.865), with a sensitivity of 64.80% and a specificity of 84.96%, and the stability was better through internal verification. CONCLUSIONS The prediction model established in our study exhibits better accuracy and internal stability in predicting the probability of malignancy for SPNs.
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Affiliation(s)
- Hongjun Hou
- Imaging Department, Weihai Central Hospital, Weihai, Shandong, China
| | - Shui Yu
- Imaging Department, Weihai Central Hospital, Weihai, Shandong, China
| | - Zushan Xu
- Imaging Department, Weihai Central Hospital, Weihai, Shandong, China
| | - Hongsheng Zhang
- Imaging Department, Weihai Central Hospital, Weihai, Shandong, China
| | - Jie Liu
- Imaging Department, Weihai Central Hospital, Weihai, Shandong, China
| | - Wenjun Zhang
- Imaging Department, Weihai Central Hospital, Weihai, Shandong, China
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Matsuura N, Tanaka K, Yamasaki M, Yamashita K, Makino T, Saito T, Yamamoto K, Takahashi T, Kurokawa Y, Motoori M, Kimura Y, Nakajima K, Eguchi H, Doki Y. Are Incidental Minute Pulmonary Nodules Ultimately Determined to Be Metastatic Nodules in Esophageal Cancer Patients? Oncology 2021; 99:547-554. [PMID: 34237725 DOI: 10.1159/000516629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 04/15/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE Esophageal cancer patients may simultaneously have resectable esophageal cancer and undiagnosable incidental minute solid pulmonary nodules. While the latter is rarely metastatic, only a few studies have reported on the outcomes of such nodules after surgery. In this retrospective study, we assessed the incidence of such nodules, the probability that they are ultimately metastatic nodules, and the prognosis of patients after esophagectomy according to the metastatic status of the nodules. METHODS Data of 398 patients who underwent esophagectomy for resectable esophageal cancer between January 2012 and December 2016 were collected. We reviewed computed tomography (CT) images from the first visit and searched for incidental minute pulmonary nodules <10 mm in size. We followed the outcomes of these nodules and compared the characteristics of metastatic and nonmetastatic nodules. We also assessed the prognosis of patients whose minute pulmonary nodules were metastatic. RESULTS Among the patients who underwent esophagectomy, 149 (37.4%) had one or more minute pulmonary nodules, with a total of 285 nodules. Thirteen (4.6%) of these nodules in 12 (8.1%) patients were ultimately diagnosed as being metastatic. Thirteen (8.7%) patients experienced recurrence at a different location from where the nodules were originally identified. Characteristics of the metastatic nodules were not unique in terms of size, SUVmax, or location in the lungs. Two-year and 5-year overall survival rates of patients whose nodules were metastatic were 64.2 and 32.1%, respectively. CONCLUSION The rate of minute pulmonary nodules which were ultimately metastatic was 4.6%. Our findings suggest that esophagectomy followed by the identification of minute pulmonary nodules is an acceptable strategy even if the nodules cannot be diagnosed as being metastatic on the first visit CT due to their small size.
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Affiliation(s)
- Norihiro Matsuura
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Koji Tanaka
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Makoto Yamasaki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Kotaro Yamashita
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Tomoki Makino
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Takuro Saito
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Kazuyoshi Yamamoto
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Tsuyoshi Takahashi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Yukinori Kurokawa
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Masaaki Motoori
- Department of Surgery, Osaka General Medical Center, Osaka, Japan
| | - Yutaka Kimura
- Department of Surgery, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kiyokazu Nakajima
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
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Albano D, Santore LA, Bilfinger T, Feraca M, Novotny S, Nemesure B. Clinical Implications of "Atypia" on Biopsy: Possible Precursor to Lung Cancer? ACTA ACUST UNITED AC 2021; 28:2516-2522. [PMID: 34287241 PMCID: PMC8293154 DOI: 10.3390/curroncol28040228] [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] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/29/2021] [Accepted: 07/02/2021] [Indexed: 12/03/2022]
Abstract
Background: It is common for biopsies of concerning pulmonary nodules to result in cytologic “atypia” on biopsy, which may represent a benign response or a false negative finding. This investigation evaluated time to diagnosis and factors which may predict an ultimate diagnosis of lung cancer in these patients with atypia cytology on lung nodule biopsy. Methods: This retrospective study included patients of the Stony Brook Lung Cancer Evaluation Center who had a biopsy baseline diagnosis of atypia between 2010 and 2020 and were either diagnosed with cancer or remained disease free by the end of the observation period. Cox Proportional Hazard (CPH) Models were used to assess factor effects on outcomes. Results: Among 106 patients with an initial diagnosis of atypia, 80 (75%) were diagnosed with lung cancer. Of those, over three-quarters were diagnosed within 6 months. The CPH models indicated that PET positivity (SUV ≥ 2.5) (HR = 1.74 (1.03, 2.94)), nodule size > 3.5 cm (HR = 2.83, 95% CI (1.47, 5.45)) and the presence of mixed ground glass opacities (HR = 2.15 (1.05, 4.43)) significantly increased risk of lung cancer. Conclusion: Given the high conversion rate to cancer within 6 months, at least tight monitoring, if not repeat biopsy may be warranted during this time period for patients diagnosed with atypia.
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Affiliation(s)
- Denise Albano
- Lung Cancer Evaluation Center at Stony Brook University Hospital, Stony Brook, NY 11790, USA; (T.B.); (M.F.); (B.N.)
- Correspondence:
| | - Lee Ann Santore
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11790, USA; (L.A.S.); (S.N.)
| | - Thomas Bilfinger
- Lung Cancer Evaluation Center at Stony Brook University Hospital, Stony Brook, NY 11790, USA; (T.B.); (M.F.); (B.N.)
| | - Melissa Feraca
- Lung Cancer Evaluation Center at Stony Brook University Hospital, Stony Brook, NY 11790, USA; (T.B.); (M.F.); (B.N.)
| | - Samantha Novotny
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11790, USA; (L.A.S.); (S.N.)
| | - Barbara Nemesure
- Lung Cancer Evaluation Center at Stony Brook University Hospital, Stony Brook, NY 11790, USA; (T.B.); (M.F.); (B.N.)
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Sato M, Yang SM, Tian D, Jun N, Lee JM. Managing screening-detected subsolid nodules-the Asian perspective. Transl Lung Cancer Res 2021; 10:2323-2334. [PMID: 34164280 PMCID: PMC8182721 DOI: 10.21037/tlcr-20-243] [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] [Indexed: 02/05/2023]
Abstract
The broad application of low-dose computed tomography (CT) screening has resulted in the detection of many small pulmonary nodules. In Asia, a large number of these detected nodules with a radiological ground glass pattern are reported as lung adenocarcinomas or premalignant lesions, especially among female non-smokers. In this review article, we discuss controversial issues and conditions involving these subsolid pulmonary nodules that we often face in Asia, including a lack or insufficiency of current guidelines; the roles of preoperative biopsy and imaging; the location of lesions; appropriate selection of localization techniques; the roles of dissection and sampling of frozen sections and lymph nodes; multifocal lesions; and the roles of non-surgical treatment modalities. For these complex issues, we have tried to present up-to-date evidence and our own opinions regarding the management of subsolid nodules. It is our hope that this article helps surgeons and physicians to manage the complex issues involving ground glass nodules (GGNs) in a balanced manner in their daily practice and provokes further discussion towards better guidelines and/or algorithms.
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Affiliation(s)
- Masaaki Sato
- Department of Thoracic Surgery, University of Tokyo Hospital, Tokyo, Japan
| | - Shun-Mao Yang
- Department of Thoracic Surgery, University of Tokyo Hospital, Tokyo, Japan.,Department of Thoracic Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu
| | - Dong Tian
- Department of Thoracic Surgery, University of Tokyo Hospital, Tokyo, Japan.,Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.,Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Nakajima Jun
- Department of Thoracic Surgery, University of Tokyo Hospital, Tokyo, Japan
| | - Jang-Ming Lee
- Department of Thoracic Surgery, National Taiwan University Hospital, Taipei
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40
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Dyer DS, Zelarney PT, Carr LL, Kern EO. Improvement in Follow-up Imaging With a Patient Tracking System and Computerized Registry for Lung Nodule Management. J Am Coll Radiol 2021; 18:937-946. [PMID: 33607066 DOI: 10.1016/j.jacr.2021.01.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE Despite established guidelines, radiologists' recommendations and timely follow-up of incidental lung nodules remain variable. To improve follow-up of nodules, a system using standardized language (tracker phrases) recommending time-based follow-up in chest CT reports, coupled with a computerized registry, was created. MATERIALS AND METHODS Data were obtained from the electronic health record and a facility-built electronic lung nodule registry. We evaluated two randomly selected patient cohorts with incidental nodules on chest CT reports: before intervention (September 2008 to March 2011) and after intervention (August 2011 to December 2016). Multivariable logistic regression was used to compare the cohorts for the main outcome of timely follow-up, defined as a subsequent report within 13 months of the initial report. RESULTS In all, 410 patients were included in the pretracker cohort versus 626 in the tracker cohort. Before system inception, 30% of CT reports lacked an explicit time-based recommendation for nodule follow-up. The proportion of patients with timely follow-up increased from 46% to 55%, and the proportion of those with no documented follow-up or follow-up beyond 24 months decreased from 48% to 31%. The likelihood of timely follow-up increased 41%, adjusted for high risk for lung cancer and age 65 years or older. After system inception, reports missing a tracker phrase for nodule recommendation averaged 6%, without significant interyear variation. CONCLUSIONS Standardized language added to CT reports combined with a computerized registry designed to identify and track patients with incidental lung nodules was associated with improved likelihood of follow-up imaging.
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Affiliation(s)
- Debra S Dyer
- Chair, Department of Radiology, National Jewish Health, Denver, Colorado.
| | | | - Laurie L Carr
- Past President, Medical Executive Committee; Division of Oncology, Department of Medicine, National Jewish Health, Denver, Colorado
| | - Elizabeth O Kern
- Chief, Division of Medical, Behavioral and Community Health, Department of Medicine; Past Chair, Institutional Review Board; Chair, Ethics Resource Committee, National Jewish Health, Denver, Colorado
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Lennartz S, Mager A, Große Hokamp N, Schäfer S, Zopfs D, Maintz D, Reinhardt HC, Thomas RK, Caldeira L, Persigehl T. Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules. Cancer Imaging 2021; 21:17. [PMID: 33499939 PMCID: PMC7836145 DOI: 10.1186/s40644-020-00374-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 12/18/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND The purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier. METHODS 183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology, 18F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier. RESULTS Differentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively). CONCLUSIONS First-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy compared to classification based on conventional image features only.
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Affiliation(s)
- Simon Lennartz
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
- Else Kröner Forschungskolleg Clonal Evolution in Cancer, University Hospital Cologne, Weyertal 115b, 50931, Cologne, Germany
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, White 270, Boston, MA, 02114, USA
| | - Alina Mager
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | | | - David Zopfs
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Hans Christian Reinhardt
- Clinic I of Internal Medicine, University Hospital Cologne, 50931, Cologne, Germany
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, University Duisburg-Essen, German Cancer Consortium (DKTK partner site Essen), Essen, Germany
| | - Roman K Thomas
- Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, 50931, Cologne, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Thorsten Persigehl
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
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Cherian SV, Kaur S, Karanth S, Xian JZ, Estrada-Y-Martin RM. Diagnostic yield of electromagnetic navigational bronchoscopy: A safety net community-based hospital experience in the United States. Ann Thorac Med 2021; 16:102-109. [PMID: 33680130 PMCID: PMC7908899 DOI: 10.4103/atm.atm_388_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/25/2020] [Indexed: 01/05/2023] Open
Abstract
INTRODUCTION: Electromagnetic navigational bronchoscopy (ENB) is an excellent tool to diagnose peripheral pulmonary nodules, especially in the setting of emphysema and pulmonary fibrosis. However, most of these procedures are done by interventional pulmonologists and academic tertiary centers under general anesthesia. Studies evaluating the diagnostic utility of this tool in safety-net community hospitals by pulmonologists not formally trained in this technology are lacking. The objective was to evaluate the diagnostic yield of ENB done in such a setting and its associated complications. METHODS: Retrospective chart review of consecutive ENB procedures over 5 years from 2014, since its inception in our institution-a safety-net community based hospital was performed. Multiple variables were analyzed to assess their impact on diagnostic yields. RESULTS: After exclusion criteria were applied, 72 patients with 76 procedures were eventually included within our study, with an overall 1-year diagnostic yield of 80.2%. Sensitivity for malignancy was 73% and negative predictive value of 65%. Primary lung cancer was the most common diagnosis obtained, followed by tuberculosis (TB). The overall complication rates were low, with only 1 patient (1.3%) requiring hospitalization due to pneumothorax needing tube thoracostomy. No deaths or respiratory failures were noted within the cohort. The only significant variable affecting diagnostic yield was forced expiratory volume in 1 s. The presence of emphysema did not affect diagnostic yield. CONCLUSIONS: ENB is safe and feasible with a high diagnostic success rate even when performed by pulmonologists not formally trained in interventional pulmonology in low resource settings under moderate sedation.
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Affiliation(s)
- Sujith V Cherian
- Divisions of Critical Care, Pulmonary and Sleep Medicine, McGovern Medical School, University of Texas Health, Houston, TX, USA
| | - Saranjit Kaur
- Divisions of Critical Care, Pulmonary and Sleep Medicine, McGovern Medical School, University of Texas Health, Houston, TX, USA
| | - Siddharth Karanth
- Divisions of Critical Care, Pulmonary and Sleep Medicine, McGovern Medical School, University of Texas Health, Houston, TX, USA
| | - Jonathan Z Xian
- Divisions of Critical Care, Pulmonary and Sleep Medicine, McGovern Medical School, University of Texas Health, Houston, TX, USA
| | - Rosa M Estrada-Y-Martin
- Divisions of Critical Care, Pulmonary and Sleep Medicine, McGovern Medical School, University of Texas Health, Houston, TX, USA
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Orringer CE, Blaha MJ, Blankstein R, Budoff MJ, Goldberg RB, Gill EA, Maki KC, Mehta L, Jacobson TA. The National Lipid Association scientific statement on coronary artery calcium scoring to guide preventive strategies for ASCVD risk reduction. J Clin Lipidol 2021; 15:33-60. [PMID: 33419719 DOI: 10.1016/j.jacl.2020.12.005] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 12/07/2020] [Indexed: 12/21/2022]
Abstract
An Expert Panel of the National Lipid Association reviewed the evidence related to the use of coronary artery calcium (CAC) scoring in clinical practice for adults seen for primary prevention of atherosclerotic cardiovascular disease. Recommendations for optimal use of this test in adults of various races/ethnicities, ages and multiple domains of primary prevention, including those with a 10-year ASCVD risk <20%, those with diabetes or the metabolic syndrome, and those with severe hypercholesterolemia were provided. Recommendations were also made on optimal timing for repeat calcium scoring after an initial test, use of CAC scoring in those taking statins, and its role in informing the clinician patient discussion on the benefit of aspirin and anti-hypertensive drug therapy. Finally, a vision is provided for the future of coronary calcium scoring.
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Affiliation(s)
- Carl E Orringer
- University of Miami, Miller School of Medicine, Cardiovascular Division.
| | - Michael J Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Heart Disease
| | - Ron Blankstein
- Brigham and Women's Hospital, Harvard Medical School, Cardiovascular Division
| | | | - Ronald B Goldberg
- Diabetes Research Institute, University of Miami Miller School of Medicine
| | - Edward A Gill
- University of Colorado School of Medicine, Anschutz Campus
| | - Kevin C Maki
- Department of Applied Health Science, School of Public Health, and Midwest Biomedical Research, Indiana University
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Meng F, Guo Y, Li M, Lu X, Wang S, Zhang L, Zhang H. Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules. Transl Oncol 2020; 14:100936. [PMID: 33221688 PMCID: PMC7689413 DOI: 10.1016/j.tranon.2020.100936] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 12/17/2022] Open
Abstract
It is vital to distinguish indolent pulmonary adenocarcinomas from invasive pulmonary adenocarcinomas before surgery. Radiomics is a cutting-edge technology that mines quantitative features from CT images. We designed a nomogram, which incorporated clinical and CT morphological characteristics with the radiomics signature. We applied the radiomics nomogram to preoperatively predict the invasiveness of GGNs.
In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916–0.964) and validation set (AUC, 0.946; 95% CI, 0.907–0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Mingyang Li
- State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China
| | - Xiaoqian Lu
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Shuo Wang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
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Sainz Zúñiga PV, Martinez-Zayas G, Molina S, Grosu HB, Arain MH, Ost DE. Is Biopsy of Contralateral Hilar N3 Lymph Nodes With Negative PET-CT Scan Findings Necessary When Performing Endobronchial Ultrasound Staging? Chest 2020; 159:1642-1651. [PMID: 33393471 DOI: 10.1016/j.chest.2020.10.041] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 09/24/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Systematic endobronchial ultrasound (EBUS)-guided lung cancer staging starts with hilar N3 nodes, proceeding sequentially to mediastinal N3, N2, and N1 nodes, with sampling of all enlarged nodes (size, ≥ 5 mm) by EBUS. However, procedure time is limited by patient comfort when moderate sedation is used. It is unclear if EBUS staging should start with hilar N3 nodes or whether starting with mediastinal N3 nodes suffices. Knowing the probability of hilar N3 nodes with PET-CT scan negative findings harboring occult metastasis can inform this decision. RESEARCH QUESTION What proportion of patients with hilar N3 nodes showing negative PET-CT scan findings have malignancy by EBUS? STUDY DESIGN AND METHODS This retrospective observational, single-center cohort study included consecutive patients with clinical-radiographic T1-3, N0-3, M0 non-small cell lung cancer undergoing systematic EBUS staging with biopsy of hilar N3 nodes with negative PET-CT scan findings. The primary outcome was the proportion of patients with malignant hilar N3 nodes showing negative PET-CT scan findings. Based on expert opinion, a threshold probability of malignancy of less than 5% was considered sufficient to skip hilar N3 nodes. We used the binomial exact test to compare the observed proportion vs threshold probability of 5%. RESULTS Of 1,737 consecutive patients undergoing EBUS staging, 1,567 showed negative PET-CT scan findings of the hilar N3 nodes. These nodes were enlarged by EBUS and were sampled in 739 patients. Malignancy was found in the hilar N3 nodes of 5 of 739 patients (0.68%; 95% CI, 0.22%-1.57%). The proportion was significantly less than the threshold probability (P < .001). Patients with positive PET scan results of the mediastinal N3 nodes were at higher risk of having occult hilar N3 nodal metastasis (P = .003), found in 3 of 46 patients (6.5%; 95% CI, 1.4%-17.9%) with positive PET scan results of the mediastinal N3 nodes. INTERPRETATION When using moderate sedation, because time is limited, it is reasonable to start with the mediastinal N3 nodes if the hilar and mediastinal N3 nodes show negative PET scan results. Patients with positive PET scan findings of the mediastinal N3 nodes probably should undergo hilar N3 node sampling.
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Affiliation(s)
- Paula V Sainz Zúñiga
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX; Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
| | - Gabriela Martinez-Zayas
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sofia Molina
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Horiana B Grosu
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Muhammad H Arain
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - David E Ost
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
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Xie Q, Li F, Zhao S, Guo T, Li Z, Fang L, Wang S, Liu W, Gu C. GalNAc-T3 and MUC1, a combined predictor of prognosis and recurrence in solitary pulmonary adenocarcinoma initially diagnosed as malignant solitary pulmonary nodule (≤ 3 cm). Hum Cell 2020; 33:1252-1263. [PMID: 32776306 DOI: 10.1007/s13577-020-00400-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 07/13/2020] [Indexed: 12/20/2022]
Abstract
The significance of the polypeptide N-acetyl-galactosaminyl transferase-3 (GalNAc-T3) and mucin 1 (MUC1) in solitary pulmonary adenocarcinoma (SPA) initially diagnosed as malignant solitary pulmonary nodule (≤ 3 cm), especially as a combined predictor of prognosis and recurrence, was explored in this study. A retrospective analysis of 83 patients with SPA (≤ 3 cm), which revealed postoperative pathological diagnosis was lung adenocarcinoma after complete resection. Immunohistochemical staining was used to detect the expression of GalNAc-T3 and MUC1 in primary tumor specimens. The relationship between expression and various clinicopathological factors was analyzed, as well as the effects of patients' overall survival (OS) and disease-free survival (DFS). In all patients, GalNAc-T3 was highly expressed in 53 (63.9%) cases; MUC1 was highly expressed in 31 (37.3%) cases. The GalNAc-T3 expression was correlated with differentiation, pathological risk group, N stage, and TNM stage. The group with high GalNAc-T3 expression and low MUC1 expression (GalNAc-T3Hig/MUC1Low) is correlated to pathological differentiation and has a trend related to the TNM stage. The patients with better differentiation, lower pathological risk group, lower N stage, and GalNAc-T3 high expression had better overall survival, especially the GalNAc-T3Hig/MUC1Low group. Moreover, the moderate differentiation, N3 stage, and GalNAc-T3Hig/MUC1Low group were independent predictive factors for OS. Besides, patients with lower N stage, lower TNM stage, higher GalNAc-T3 expression got better disease-free survival (DFS), especially the GalNAc-T3Hig/MUC1Low group. The GalNAc-T3Hig/MUC1Low group was an independent predictive factor for DFS. In conclusion, GalNAc-T3 and MUC1 were combined predictors of prognosis and recurrence in SPA (≤ 3 cm).
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Affiliation(s)
- Qiang Xie
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Fengzhou Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Shilei Zhao
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Tao Guo
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Zhuoshi Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Lei Fang
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Shiqing Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Wenzhi Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China
| | - Chundong Gu
- Department of Thoracic Surgery, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, 116011, Liaoning, People's Republic of China.
- Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, People's Republic of China.
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Wahidi MM, Shojaee S, Lamb CR, Ost D, Maldonado F, Eapen G, Caroff DA, Stevens MP, Ouellette DR, Lilly C, Gardner DD, Glisinski K, Pennington K, Alalawi R. The Use of Bronchoscopy During the Coronavirus Disease 2019 Pandemic: CHEST/AABIP Guideline and Expert Panel Report. Chest 2020; 158:1268-1281. [PMID: 32361152 PMCID: PMC7252059 DOI: 10.1016/j.chest.2020.04.036] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 04/19/2020] [Accepted: 04/29/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) has swept the globe and is causing significant morbidity and mortality. Given that the virus is transmitted via droplets, open airway procedures such as bronchoscopy pose a significant risk to health-care workers (HCWs). The goal of this guideline was to examine the current evidence on the role of bronchoscopy during the COVID-19 pandemic and the optimal protection of patients and HCWs. STUDY DESIGN AND METHODS A group of approved panelists developed key clinical questions by using the Population, Intervention, Comparator, and Outcome (PICO) format that addressed specific topics on bronchoscopy related to COVID-19 infection and transmission. MEDLINE (via PubMed) was systematically searched for relevant literature and references were screened for inclusion. Validated evaluation tools were used to assess the quality of studies and to grade the level of evidence to support each recommendation. When evidence did not exist, suggestions were developed based on consensus using the modified Delphi process. RESULTS The systematic review and critical analysis of the literature based on six PICO questions resulted in six statements: one evidence-based graded recommendation and 5 ungraded consensus-based statements. INTERPRETATION The evidence on the role of bronchoscopy during the COVID-19 pandemic is sparse. To maximize protection of patients and HCWs, bronchoscopy should be used sparingly in the evaluation and management of patients with suspected or confirmed COVID-19 infections. In an area where community transmission of COVID-19 infection is present, bronchoscopy should be deferred for nonurgent indications, and if necessary to perform, HCWs should wear personal protective equipment while performing the procedure even on asymptomatic patients.
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Affiliation(s)
- Momen M Wahidi
- Department of Medicine, Division of Pulmonary, Allergy and Critical Care, Duke University School of Medicine, Durham, NC.
| | - Samira Shojaee
- Department of Medicine, Divisions of Pulmonary and Critical Care and Infectious Disease, Virginia Commonwealth University, Richmond, VA
| | - Carla R Lamb
- Department of Medicine, Divisions of Pulmonary and Critical Care and Infectious Disease, Lahey Hospital and Medical Center, Burlington, MA
| | - David Ost
- Department of Medicine, Division of Pulmonary and Critical Care, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Fabien Maldonado
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care, Vanderbilt University, Nashville, TN
| | - George Eapen
- Department of Medicine, Division of Pulmonary and Critical Care, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Daniel A Caroff
- Department of Medicine, Divisions of Pulmonary and Critical Care and Infectious Disease, Lahey Hospital and Medical Center, Burlington, MA
| | - Michael P Stevens
- Department of Medicine, Divisions of Pulmonary and Critical Care and Infectious Disease, Virginia Commonwealth University, Richmond, VA
| | - Daniel R Ouellette
- Department of Medicine, Division of Pulmonary and Critical Care, Henry Ford Health System, Detroit, MI
| | - Craig Lilly
- Department of Medicine, Division of Pulmonary and Critical Care, University of Massachusetts, Worcester, MA
| | - Donna D Gardner
- Department of Respiratory Care, Texas State University, Round Rock, TX
| | - Kristen Glisinski
- Department of Medicine, Division of Pulmonary, Allergy and Critical Care, Duke University School of Medicine, Durham, NC
| | - Kelly Pennington
- Department of Medicine, Division of Pulmonary and Critical Care, Mayo Clinic, Rochester, MN
| | - Raed Alalawi
- Department of Medicine, Division of Pulmonary and Critical Care, University of Arizona, Phoenix, AZ
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Hu X, Ye W, Li Z, Chen C, Cheng S, Lv X, Weng W, Li J, Weng Q, Pang P, Xu M, Chen M, Ji J. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis. Br J Radiol 2020; 93:20190762. [PMID: 32686958 DOI: 10.1259/bjr.20190762] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVES To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis. METHODS A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets. RESULTS Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively. CONCLUSION The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm). ADVANCES IN KNOWLEDGE Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.
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Affiliation(s)
- Xianghua Hu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Weichuan Ye
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Zhongxue Li
- Department of Radiology, Fuyuan Hospital of Yiwu, Jinhua 321000, China
| | - Chunmiao Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Shimiao Cheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Xiuling Lv
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Wei Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Jie Li
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | | | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
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49
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Affiliation(s)
- David E Ost
- Department of Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
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50
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Atkins NK, Marjara J, Kaifi JT, Kunin JR, Saboo SS, Davis RM, Bhat AP. Role of Computed Tomography-guided Biopsies in the Era of Electromagnetic Navigational Bronchoscopy: A Retrospective Study of Factors Predicting Diagnostic Yield in Electromagnetic Navigational Bronchoscopy and Computed Tomography Biopsies. J Clin Imaging Sci 2020; 10:33. [PMID: 32547836 PMCID: PMC7294316 DOI: 10.25259/jcis_53_2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 05/20/2020] [Indexed: 12/26/2022] Open
Abstract
Objectives: Over 25% of the high-risk population screened for lung cancer have an abnormal computed tomography (CT) scan. Conventionally, these lesions have been biopsied with CT guidance with a high diagnostic yield. Electromagnetic navigational bronchoscopy (ENB) with transbronchial biopsy has emerged as a technology that improves the diagnostic sensitivity of conventional bronchoscopic biopsy. It has been used to biopsy lung lesions, due to the low risk of pneumothorax. It is, however, a new technology that is expensive and its role in the diagnosis of the solitary pulmonary nodule (SPN) is yet to be determined. The purpose of this study was to evaluate the diagnostic yield of CT-guided biopsy (CTB) following non-diagnostic ENB biopsy and identify characteristics of the lesion that predicts a low diagnostic yield with ENB, to ensure appropriate use of ENB in the evaluation of SPN. Materials and Methods: One hundred and thirty-five lung lesions were biopsied with ENB from January 2017 to August 2019. Biopsies were considered diagnostic if pathology confirmed malignancy or inflammation in the appropriate clinical and imaging setting. We evaluated lesions for several characteristics including size, lobe, and central/peripheral distribution. The diagnostic yield of CTB in patients who failed ENB biopsies was also evaluated. Logistic regression was used to identify factors likely to predict a non-diagnostic ENB biopsy. Result: Overall, ENB biopsies were performed in 135 patients with solitary lung lesions. ENB biopsies were diagnostic in 52% (70/135) of the patients. In 23 patients with solitary lung lesions, CTBs were performed following a non-diagnostic ENB biopsy. The CTBs were diagnostic in 87% of the patients (20/23). ENB biopsies of lesions <21.5 mm were non-diagnostic in 71% of cases (42/59); 14 of these patients with non-diagnostic ENB biopsies had CTBs, and 86% of them were diagnostic (12/14). ENB biopsies of lesions in the lower lobes were non- diagnostic in 59% of cases (35/59); 12 of these patients with non-diagnostic ENB biopsies had CTBs, and 83% were diagnostic (10/12). ENB biopsies of lesions in the outer 2/3 were non-diagnostic in 57% of cases (50/87); 21 of these patients with non-diagnostic ENB biopsies had CTBs, and 86% were diagnostic (18/21). Conclusion: CTBs have a high diagnostic yield even following non-diagnostic ENB biopsies. Lesions <21.5 mm, in the outer 2/3 of the lung, and in the lower lung have the lowest likelihood of a diagnostic yield with ENB biopsies. Although CTBs have a slightly higher pneumothorax rate, these lesions would be more successfully diagnosed with CTB as opposed to ENB biopsy, in the process expediting the diagnosis and saving valuable medical resources.
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Affiliation(s)
- Naomi K Atkins
- Departments of Radiology, University of Missouri, Columbia, Missouri, United States
| | - Jasraj Marjara
- Departments of Radiology, University of Missouri, Columbia, Missouri, United States
| | - Jussuf T Kaifi
- Departments of Cardiothoracic Surgery, University of Missouri, Columbia, Missouri, United States
| | - Jeffrey R Kunin
- Departments of Radiology, University of Missouri, Columbia, Missouri, United States
| | - Sachin S Saboo
- Department of Radiology, University of Texas Health Science Center, San Antonio, Texas, United States
| | - Ryan M Davis
- Departments of Radiology, University of Missouri, Columbia, Missouri, United States
| | - Ambarish P Bhat
- Departments of Radiology, University of Missouri, Columbia, Missouri, United States
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