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Luo W, Ren Y, Liu Y, Deng J, Huang X. Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions. Quant Imaging Med Surg 2024; 14:6123-6146. [PMID: 39144060 PMCID: PMC11320543 DOI: 10.21037/qims-24-674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 06/21/2024] [Indexed: 08/16/2024]
Abstract
Background and Objective The incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area. Methods We used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures. Key Content and Findings We examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs. Conclusions A variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.
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Affiliation(s)
- Wenting Luo
- The Second Clinical Medical College, Nanchang University, Nanchang, China
| | - Yifei Ren
- The Second Clinical Medical College, Nanchang University, Nanchang, China
| | - Yinuo Liu
- The Second Clinical Medical College, Nanchang University, Nanchang, China
| | - Jun Deng
- The Second Clinical Medical College, Nanchang University, Nanchang, China
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- Intelligent Medical Imaging of Jiangxi Key Laboratory, Nanchang, China
| | - Xiaoning Huang
- The Second Clinical Medical College, Nanchang University, Nanchang, China
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- Intelligent Medical Imaging of Jiangxi Key Laboratory, Nanchang, China
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Liu J, Qi L, Xu Q, Chen J, Cui S, Li F, Wang Y, Cheng S, Tan W, Zhou Z, Wang J. A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules. Acad Radiol 2024:S1076-6332(24)00287-3. [PMID: 38777719 DOI: 10.1016/j.acra.2024.05.002] [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: 03/25/2024] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024]
Abstract
RATIONALE AND OBJECTIVES Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images. MATERIALS AND METHODS This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. RESULTS Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860. CONCLUSION This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.
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Affiliation(s)
- Jianing Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Qian Xu
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, He Bei, China
| | - Jiaqi Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Shulei Cui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Fenglan Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yawen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Sainan Cheng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Weixiong Tan
- Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China
| | - Zhen Zhou
- Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
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Wendan W, Mengyu L, Qiufeng Z. Decreased levels of sex hormones in females with solitary pulmonary nodules were risk factors for malignancy. J Cardiothorac Surg 2024; 19:119. [PMID: 38475837 DOI: 10.1186/s13019-024-02609-x] [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: 08/09/2023] [Accepted: 03/05/2024] [Indexed: 03/14/2024] Open
Abstract
OBJECTIVE The purpose of this research was to detect the relationship between the levels of sex hormones in females with solitary pulmonary nodules (SPNs) and their potential malignancies. METHODS A total of 187 consecutive patients with pathologically confirmed SPNs by chest CT were enrolled in our study. They were divided into two groups based on the pathologic findings of SPNs after surgery: benign and malignant SPNs. Progesterone (P), estradiol (E2), and testosterone (T) levels in the two groups were measured. Meanwhile, we used binary logistic regression analysis to analyze the risk factors for SPNs. RESULTS Of these 187 patients, 73 had benign SPNs, while 114 had malignant SPNs. We found that the levels of progesterone (P), estradiol (E2), and testosterone (T) were decreased significantly in patients with malignant SPNs compared to patients with benign SPNs (all P < 0.05). Multivariate logistic regression analysis revealed that second-hand smoke, burr sign, lobulation sign, pleural traction sign, vascular convergence sign, vacuole sign, and ≥ 1 cm nodules were independent risk factors for malignant pulmonary nodules in females. CONCLUSIONS Decreased levels of sex hormones in females were associated with malignant pulmonary nodules, suggesting that they can contribute to the diagnosis of lung cancer.
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Affiliation(s)
- Wang Wendan
- Department of Internal Medicine, the Second Affiliated Hospital of Zhejiang University School of Medicine, HangZhou, Zhejiang, China
| | - Li Mengyu
- Department of Internal Medicine, the Second Affiliated Hospital of Zhejiang University School of Medicine, HangZhou, Zhejiang, China
| | - Zhang Qiufeng
- Department of Internal Medicine, the Second Affiliated Hospital of Zhejiang University School of Medicine, HangZhou, Zhejiang, China.
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Song C, Sun Y, Chen Y, Shen Y, Lei H, Mao W, Wang J, Wan Y. Differential diagnosis of pulmonary nodules and prediction of invasive adenocarcinoma using extracellular vesicle DNA. Clin Transl Med 2024; 14:e1582. [PMID: 38344857 PMCID: PMC10859785 DOI: 10.1002/ctm2.1582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/07/2024] [Accepted: 01/25/2024] [Indexed: 02/15/2024] Open
Affiliation(s)
- Chenghu Song
- Department of Cardiothoracic SurgeryThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiJiangsuChina
- Department of Biomedical EngineeringThe Pq Laboratory of BiomeDx/RxBinghamton UniversityBinghamtonNew YorkUSA
| | - Yifeng Sun
- Department of SurgeryUlm University Hospital, Ulm UniversityUlmGermany
- Department of SurgeryHeidelberg University HospitalHeidelberg UniversityHeidelbergGermany
| | - Yundi Chen
- Department of Biomedical EngineeringThe Pq Laboratory of BiomeDx/RxBinghamton UniversityBinghamtonNew YorkUSA
| | - Yihang Shen
- Department of Computational BiologyCarnegie Mellon UniversityPittsburghPennsylvaniaUSA
| | - Haozhi Lei
- Department of Biomedical EngineeringThe Pq Laboratory of BiomeDx/RxBinghamton UniversityBinghamtonNew YorkUSA
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Wenjun Mao
- Department of Cardiothoracic SurgeryThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiJiangsuChina
- Department of Biomedical EngineeringThe Pq Laboratory of BiomeDx/RxBinghamton UniversityBinghamtonNew YorkUSA
| | - Jing Wang
- Yizheng Hospital of Nanjing Drum Tower Hospital GroupYizhengJiangsuChina
- Department of HematologyNanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityNanjingJiangsuChina
| | - Yuan Wan
- Department of Biomedical EngineeringThe Pq Laboratory of BiomeDx/RxBinghamton UniversityBinghamtonNew YorkUSA
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Song F, Yang Q, Gong T, Sun K, Zhang W, Liu M, Lv F. Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China. Cancer Imaging 2024; 24:15. [PMID: 38254185 PMCID: PMC10801946 DOI: 10.1186/s40644-023-00634-y] [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: 08/10/2023] [Accepted: 11/05/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system). METHODS Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021-June 30, 2022), SC Center (September 1, 2021-December 31, 2021), and SX Center (January 1, 2021-December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test. RESULTS For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P < 0.001; PSN 99.9% vs. 98.4%, P = 0.015), but the specificity (SN 51.2% > 35.1%, PSN 13.3% > 5.7%, all P < 0.001) and agreement rate (SN 81.1% > 74.5%, P < 0.001, PSN 94.6% > 92.7%, all P < 0.05) of PNI-GARS were superior to those of Lung-RADS. For GGN, the sensitivity (96.5%) and agreement rate (88.6%) of PNI-GARS were better than those of Lung-RADS (0, 18.5%, P < 0.001). For the whole sample, the sensitivity (98.5%) and agreement rate (87.0%) of PNI-GARS were better than Lung-RADS (57.5%, 56.5%, all P < 0.001), whereas the specificity was slightly lower (49.8% < 53.4%, P = 0.003). CONCLUSION PNI-GARS was superior to Lung-RADS in diagnostic performance, especially for GGN.
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Affiliation(s)
- Feipeng Song
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No.1 YouYi Road, Chongqing, 400010, China
| | - Qian Yang
- Department of Radiology, Hubei Cancer Hospital, Wuhan, China
| | - Tong Gong
- Department of Radiology, Sichuan Provincial People's Hospital, Chengdu, China
| | - Kai Sun
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Mengxi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No.1 YouYi Road, Chongqing, 400010, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No.1 YouYi Road, Chongqing, 400010, China.
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Borgheresi A, Agostini A, Pierpaoli L, Bruno A, Valeri T, Danti G, Bicci E, Gabelloni M, De Muzio F, Brunese MC, Bruno F, Palumbo P, Fusco R, Granata V, Gandolfo N, Miele V, Barile A, Giovagnoni A. Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach. Tomography 2023; 9:1153-1186. [PMID: 37368547 DOI: 10.3390/tomography9030095] [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: 05/05/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126 Ancona, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Pierpaoli
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Alessandra Bruno
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Tommaso Valeri
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health, Unit 1, 67100 L'Aquila, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health, Unit 1, 67100 L'Aquila, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126 Ancona, Italy
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Madani MH, Riess JW, Brown LM, Cooke DT, Guo HH. Imaging of lung cancer. Curr Probl Cancer 2023:100966. [PMID: 37316337 DOI: 10.1016/j.currproblcancer.2023.100966] [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: 02/21/2023] [Revised: 04/29/2023] [Accepted: 05/23/2023] [Indexed: 06/16/2023]
Abstract
Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imaging appearances. The most frequently used imaging modalities include chest radiography, computed tomography, magnetic resonance imaging, and positron emission tomography. Artificial intelligence algorithms and radiomics are emerging technologies with potential applications in lung cancer imaging.
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Affiliation(s)
- Mohammad H Madani
- Department of Radiology, University of California, Davis, Sacramento, CA.
| | - Jonathan W Riess
- Division of Hematology/Oncology, Department of Internal Medicine, UC Davis Medical Center, UC Davis Comprehensive Cancer Center, Sacramento, CA
| | - Lisa M Brown
- Division of General Thoracic Surgery, Department of Surgery, UC Davis Health, Sacramento, CA
| | - David T Cooke
- Division of General Thoracic Surgery, Department of Surgery, UC Davis Health, Sacramento, CA
| | - H Henry Guo
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
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Abstract
BACKGROUND Pulmonary nodule growth is often measured by volume doubling time (VDT), which may guide management. Most malignant nodules have a VDT of 20 to 400 days, with longer VDTs typically observed in indolent nodules. We assessed the utility of VDT in differentiating pulmonary carcinoids and hamartomas. METHODS A review was performed from January 2012 to October 2021 to identify patients with pathologic diagnoses and at least 2 chest computed tomography scans obtained 6 or more months apart. Visualization software was used to segment nodules and calculate diameter and volume. Volume doubling time was calculated for scans with 1-mm slices. For the remainder, estimated nodule volume doubling time (eVDT) was calculated using nodule diameter. Volume doubling times/eVDTs were placed into growth categories: less than 400 days; 400-600 days; and more than 600 days. RESULTS Sixty nodules were identified, 35 carcinoids and 25 hamartomas. Carcinoids were larger than hamartomas (median diameter, 13.5 vs 11.5 mm; P = 0.05). For carcinoid tumors, median VDT (n = 15) was 1485 days, and median eVDT (n = 32) was 1309 days; for hamartomas, median VDT (n = 8) was 2040 days and median eVDT (n = 25) was 2253 days. Carcinoid tumor eVDT was significantly shorter than hamartomas ( P = 0.03). By growth category, 1 of 25 hamartomas and 5 of 35 carcinoids had eVDT less than 400 days and 24 of 25 hamartomas and 27 of 35 carcinoids had eVDT more than 600 days. Of 4 carcinoid tumors with metastases, 2 had eVDT less than 400 days and 2 had eVDT more than 600 days. CONCLUSIONS Growth rate was not a reliable differentiator of pulmonary hamartomas and carcinoids. Slow growing carcinoids can metastasize. Radiologists should be cautious when discontinuing computed tomography follow-up based on growth rates alone.
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Affiliation(s)
- James W Ryan
- From the Brigham and Women's Hospital, Boston MA
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Lin RY, Lv ZM, Lv FJ, Fu BJ, Liang ZR, Chu ZG. Quantitative evaluation of density variability in the lesion-lung boundary zone to differentiate pulmonary subsolid nodules. Quant Imaging Med Surg 2023; 13:776-786. [PMID: 36819233 PMCID: PMC9929397 DOI: 10.21037/qims-22-510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 11/20/2022] [Indexed: 01/05/2023]
Abstract
Background Transition of the CT values from nodule to peripheral normal lung is related to pathological changes and may be a potential indicator for differential diagnosis. This study investigated the significance of the standard deviation (SD) values in the lesion-lung boundary zone when differentiating between benign and neoplastic subsolid nodules (SSNs). Methods From January 2012 to July 2021, a total of 229 neoplastic and 84 benign SSNs confirmed by pathological examination were retrospectively and nonconsecutively enrolled in this study. The diagnostic study was not registered with a clinical trial platform, and the study protocol was not published. Computed tomography (CT) values of the ground-glass component (CT1), adjacent normal lung tissue (CT2), and lesion-lung boundary zone (CT3) were measured consecutively. The SD of CT3 was recorded to assess density variability. The CT1, CT2, CT3, and SD values were compared between benign and neoplastic SSNs. Results No significant differences in CT1 and CT2 were observed between benign and neoplastic SSNs (each P value >0.05). CT3 (-736.1±51.0 vs. -792.6±73.9; P<0.001) and its SD (135.6±29.6 vs. 83.6±20.6; P<0.001) in neoplastic SSNs were significantly higher than those in benign SSNs. Moreover, the SD increased with the invasiveness degree of neoplastic SSNs (r=0.657; P<0.001). The receiver operating characteristic (ROC) curve revealed that the area under the curve was 0.927 (95% CI: 0.896-0.959) when using the SD (cutoff value =106.98) as a factor to distinguish SSNs, which increased to 0.966 (95% CI: 0.934-0.985) when including nodules with a CT1 of ≥-715 Hounsfield units (HU) only (cutoff of SD 109.9, sensitivity 0.930, and specificity 0.914). Conclusions The SD as an objective index is valuable for differentiating SSNs, especially for those with a CT1 of ≥-715 HU, which have a higher possibility of neoplasm if the SD is >109.9.
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Affiliation(s)
- Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhuo-Ma Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China;,Department of Radiology, The Second People’s Hospital of Yubei District, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhang-Rui Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Application of CT Postprocessing Reconstruction Technique in Differential Diagnosis of Benign and Malignant Solitary Pulmonary Nodules and Analysis of Risk Factors. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9739047. [PMID: 35983523 PMCID: PMC9381188 DOI: 10.1155/2022/9739047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/31/2022] [Accepted: 06/23/2022] [Indexed: 11/17/2022]
Abstract
Objective To evaluate the application of CT postprocessing reconstruction technique in differential diagnosis of benign and malignant solitary pulmonary nodules and analysis of risk factors. Methods A total of 150 solitary pulmonary nodules (SPN) patients admitted to our hospital from January 2020 to January 2022 were selected and divided into the benign SPN group (n = 64) and the malignant SPN group (n = 86) according to pathological results. All subjects underwent CT plain scan and CT postprocessing reconstruction, and the general information of the subjects was collected. The diagnostic value of CT plain scan and CT postprocessing reconstruction techniques for benign and malignant SPN was compared; and the CT signs of benign and malignant SPN were compared, and the risk factors of malignant SPN were analyzed. Results The pathological results of this study showed that there were 64 cases with benign SPN and 86 cases with malignant SPN. The sensitivity, specificity, accuracy, positive predictive rate, and negative predictive rate of CT postprocessing reconstruction technology in diagnosing malignant SPN were 73.44%, 89.53%, 82.67%, 83.39%, and 81.91%, respectively, which were higher than 56.25%, 65.12%, 61.33%, 54.55%, and 66.67% of CT plain scan, and the difference was statistically significant (P < 0.05). There were no significant differences in nodule location, nodule density, vacuole sign, vessel convergence, and pleural depression sign between the two groups (P > 0.05). There were statistically significant differences in age, nodule diameter, lobulation sign, burr sign, calcification components, and ground-glass components between the two groups (P < 0.05). Multivariate analysis showed that age ≥ 60 years, nodule diameter ≥ 15 mm, the presence of lobulation sign, burr sign, ground-glass components, and noncalcification components were independent risk factors for malignant SPN. Conclusion CT postprocessing reconstruction technique has high diagnostic value in the differentiation of benign and malignant SPN, age ≥ 60 years, nodule diameter ≥ 15 mm, lobulation signs, burr signs, ground-glass components, and noncalcification components are independent risk factors for malignant SPN.
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Wang X, Gao M, Xie J, Deng Y, Tu W, Yang H, Liang S, Xu P, Zhang M, Lu Y, Fu C, Li Q, Fan L, Liu S. Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2022; 12:892890. [PMID: 35747810 PMCID: PMC9209648 DOI: 10.3389/fonc.2022.892890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention. Methods Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’. Results The image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset. Conclusion The deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.
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Affiliation(s)
- Xiang Wang
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Man Gao
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Jicai Xie
- Department of Radiology, The Second People’s Hospital of Yuhuan, Yuhuan, China
| | - Yanfang Deng
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Hua Yang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Shuang Liang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Panlong Xu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Mingzi Zhang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Yang Lu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - ChiCheng Fu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Qiong Li
- Department of Radiology, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
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Nakahashi K, Shiono S, Nakatsuka M, Endo M. Prediction of lymph node metastasis of clinical stage IA non-small cell lung cancer based on the tumor volume doubling time. Surg Today 2022; 52:1063-1071. [PMID: 35044521 DOI: 10.1007/s00595-022-02450-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/23/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE To investigate whether the volume doubling time is a preoperative predictor of lymph node metastasis of clinical stage IA non-small cell lung cancer (NSCLC). METHODS The subjects of this retrospective study were 204 patients who underwent lobectomy and mediastinal lymph node dissection for clinical stage IA NSCLC. We analyzed the relationship between lymph node metastasis and clinicopathological factors, including the volume doubling time. RESULTS Lymph node metastasis developed in 24 (11.8%) patients. Multivariable analysis identified consolidation diameter (Odds ratio = 1.407; 95% confidence interval, 1.007-1.966, p = 0.046) and the solid-part tumor volume doubling time (Odds ratio = 0.982; 95% confidence interval, 0.973-0.991, p < 0.001) as independent predictors of lymph node metastasis. The combination of a larger consolidation diameter (> 1.9 cm) and a shorter solid-part tumor volume doubling time (< 132 days) had sensitivity, specificity, and accuracy of 79.2%, 94.4%, and 92.6%, respectively. CONCLUSIONS The consolidation diameter and solid-part tumor volume doubling time can be useful preoperative predictors of lymph node metastasis of clinical stage IA NSCLC.
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Affiliation(s)
- Kenta Nakahashi
- Department of Thoracic Surgery, Yamagata Prefectural Central Hospital, 1800, Ooazaaoyagi, Yamagata, Yamagata, 990-2292, Japan
| | - Satoshi Shiono
- Department of Thoracic Surgery, Yamagata Prefectural Central Hospital, 1800, Ooazaaoyagi, Yamagata, Yamagata, 990-2292, Japan.
| | - Marina Nakatsuka
- Department of Thoracic Surgery, Yamagata Prefectural Central Hospital, 1800, Ooazaaoyagi, Yamagata, Yamagata, 990-2292, Japan
| | - Makoto Endo
- Department of Thoracic Surgery, Yamagata Prefectural Central Hospital, 1800, Ooazaaoyagi, Yamagata, Yamagata, 990-2292, Japan
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Frederick RP, Patel AG, Young SW, Dahiya N, Patel MD. Growth Rate of Ovarian Serous Cystadenomas and Cystadenofibromas. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:2123-2130. [PMID: 33320368 DOI: 10.1002/jum.15597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 12/02/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVES We analyzed growth rates of benign ovarian serous cystadenomas and cystadenofibromas to understand what percentage would show a volume doubling time (DT) of less than 3 years, between 3 and 5 years, or greater than 5 years. METHODS We retrospectively reviewed pathology records (January 1, 2014, to June 30, 2019) to find all surgically excised ovarian serous cystadenomas and cystadenofibromas. Imaging records were then reviewed to identify those that had been confidently identified with ultrasound imaging, magnetic resonance imaging, or computed tomography at least twice before surgical removal, with at least a 60-day interval between studies. Three orthogonal measurements were recorded on the first and last imaging studies on which the mass was detected, with volume calculations by the prolate formula (product of 3 measurements multiplied by 0.52). The volume DT was calculated and grouped into 1 of 5 categories: (1) DT of less than 1 year; (2) DT of 1 to 3 years; (3) DT of 3 to 5 years; (4) DT of 5 to 10 years; and (5) no growth (any mass with a DT >10 years or showing a decrease in volume). RESULTS A total of 102 of 536 cystadenomas and 44 of 227 cystadenofibromas met inclusion criteria. Of the 146 tumors, 40 (27.4%) had a DT of less than 1 year; 38 (26.0%) had a DT of 1 to 3 years; 22 (15.1%) had a DT of 3 to 5 years; 10 (6.8%) had a DT of 5 to 10 years; and 36 (24.7%) showed no growth. CONCLUSIONS A total of 53.4% of ovarian serous cystadenomas/cystadenofibromas have a DT of less than 3 years; 15.1% have a DT between 3 and 5 years; and 31.5% have a DT of greater than 5 years or show no growth.
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Affiliation(s)
- Ross P Frederick
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Anika G Patel
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Scott W Young
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Nirvikar Dahiya
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Maitray D Patel
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
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Jiang B, Zhang Y, Zhang L, H de Bock G, Vliegenthart R, Xie X. Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks. Eur Radiol 2021; 31:7303-7315. [PMID: 33847813 DOI: 10.1007/s00330-021-07901-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/03/2021] [Accepted: 03/16/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES The interpretability of convolutional neural networks (CNNs) for classifying subsolid nodules (SSNs) is insufficient for clinicians. Our purpose was to develop CNN models to classify SSNs on CT images and to investigate image features associated with the CNN classification. METHODS CT images containing SSNs with a diameter of ≤ 3 cm were retrospectively collected. We trained and validated CNNs by a 5-fold cross-validation method for classifying SSNs into three categories (benign and preinvasive lesions [PL], minimally invasive adenocarcinoma [MIA], and invasive adenocarcinoma [IA]) that were histologically confirmed or followed up for 6.4 years. The mechanism of CNNs on human-recognizable CT image features was investigated and visualized by gradient-weighted class activation map (Grad-CAM), separated activation channels and areas, and DeepDream algorithm. RESULTS The accuracy was 93% for classifying 586 SSNs from 569 patients into three categories (346 benign and PL, 144 MIA, and 96 IA in 5-fold cross-validation). The Grad-CAM successfully located the entire region of image features that determined the final classification. Activated areas in the benign and PL group were primarily smooth margins (p < 0.001) and ground-glass components (p = 0.033), whereas in the IA group, the activated areas were mainly part-solid (p < 0.001) and solid components (p < 0.001), lobulated shapes (p < 0.001), and air bronchograms (p < 0.001). However, the activated areas for MIA were variable. The DeepDream algorithm showed the image features in a human-recognizable pattern that the CNN learned from a training dataset. CONCLUSION This study provides medical evidence to interpret the mechanism of CNNs that helps support the clinical application of artificial intelligence. KEY POINTS • CNN achieved high accuracy (93%) in classifying subsolid nodules on CT images into three categories: benign and preinvasive lesions, MIA, and IA. • The gradient-weighted class activation map (Grad-CAM) located the entire region of image features that determined the final classification, and the visualization of the separated activated areas was consistent with radiologists' expertise for diagnosing subsolid nodules. • DeepDream showed the image features that CNN learned from a training dataset in a human-recognizable pattern.
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Affiliation(s)
- Beibei Jiang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Yaping Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Geertruida H de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China.
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Fernandes S, Williams G, Williams E, Ehrlich K, Stone J, Finlayson N, Bradley M, Thomson RR, Akram AR, Dhaliwal K. Solitary pulmonary nodule imaging approaches and the role of optical fibre-based technologies. Eur Respir J 2021; 57:2002537. [PMID: 33060152 PMCID: PMC8174723 DOI: 10.1183/13993003.02537-2020] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 09/29/2020] [Indexed: 12/18/2022]
Abstract
Solitary pulmonary nodules (SPNs) are a clinical challenge, given there is no single clinical sign or radiological feature that definitively identifies a benign from a malignant SPN. The early detection of lung cancer has a huge impact on survival outcome. Consequently, there is great interest in the prompt diagnosis, and treatment of malignant SPNs. Current diagnostic pathways involve endobronchial/transthoracic tissue biopsies or radiological surveillance, which can be associated with suboptimal diagnostic yield, healthcare costs and patient anxiety. Cutting-edge technologies are needed to disrupt and improve, existing care pathways. Optical fibre-based techniques, which can be delivered via the working channel of a bronchoscope or via transthoracic needle, may deliver advanced diagnostic capabilities in patients with SPNs. Optical endomicroscopy, an autofluorescence-based imaging technique, demonstrates abnormal alveolar structure in SPNs in vivo Alternative optical fingerprinting approaches, such as time-resolved fluorescence spectroscopy and fluorescence-lifetime imaging microscopy, have shown promise in discriminating lung cancer from surrounding healthy tissue. Whilst fibre-based Raman spectroscopy has enabled real-time characterisation of SPNs in vivo Fibre-based technologies have the potential to enable in situ characterisation and real-time microscopic imaging of SPNs, which could aid immediate treatment decisions in patients with SPNs. This review discusses advances in current imaging modalities for evaluating SPNs, including computed tomography (CT) and positron emission tomography-CT. It explores the emergence of optical fibre-based technologies, and discusses their potential role in patients with SPNs and suspected lung cancer.
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Affiliation(s)
- Susan Fernandes
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
| | - Gareth Williams
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
| | - Elvira Williams
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
| | - Katjana Ehrlich
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
| | - James Stone
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
- Centre for Photonics and Photonic Materials, Dept of Physics, The University of Bath, Bath, UK
| | - Neil Finlayson
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
- Institute for Integrated Micro and Nano Systems, School of Engineering, The University of Edinburgh, Edinburgh, UK
| | - Mark Bradley
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
- EaStCHEM, School of Chemistry, The University of Edinburgh, Edinburgh, UK
| | - Robert R. Thomson
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
- Institute of Photonics and Quantum Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK
| | - Ahsan R. Akram
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
| | - Kevin Dhaliwal
- Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK
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Setojima Y, Shimada Y, Tanaka T, Shigefuku S, Makino Y, Maehara S, Hagiwara M, Masuno R, Yamada T, Kakihana M, Kajiwara N, Ohira T, Ikeda N. Prognostic impact of solid-part tumour volume doubling time in patients with radiological part-solid or solid lung cancer. Eur J Cardiothorac Surg 2020; 57:763-770. [PMID: 31746987 DOI: 10.1093/ejcts/ezz305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 09/26/2019] [Accepted: 10/02/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVES The measurement of part-solid and whole tumour sizes in patients with non-small-cell lung cancer (NSCLC) using computed tomography (CT) has been widely accepted for assessing clinical outcomes. Although the volume doubling time (VDT) of a tumour is useful for distinguishing high-risk nodules from low-risk ones, it remains to be clarified whether separate calculation of whole-tumour VDT and solid-part tumour VDT (SVDT) greatly affects the survival rate of patients with radiologically node-negative part-solid or solid NSCLC. METHODS The study included 258 patients with NSCLC who had radiologically node-negative, part-solid or solid tumours and who had at least 2 preoperative CT scans taken more than 30 days apart followed by radical lobectomy and systemic lymph node dissection between January 2012 and December 2015. Univariable and multivariable analyses of recurrence-free survival were performed using the Cox proportional hazards regression model. RESULTS The mean whole-tumour VDT and SVDT were 375 and 458 days, respectively. Multivariable analyses demonstrated that whole-tumour VDT (P = 0.003), SVDT (P < 0.001), solid-part tumour size, whole-tumour size and comorbidities significantly affected the recurrence-free survival. Using the receiver operating characteristic curve, the cut-off value of the SVDT for recurrence was 215 days, and the 5-year recurrence-free survival rates for patients with SVDT >215 days and those with SVDT <215 days were 85.7% and 43.0%, respectively (P < 0.001). CONCLUSION The calculation of SVDT in patients with node-negative, part-solid or solid NSCLC is highly useful for predicting postoperative survival outcomes.
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Affiliation(s)
- Yusuke Setojima
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Yoshihisa Shimada
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Takehiko Tanaka
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | | | - Yojiro Makino
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Sachio Maehara
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Masaru Hagiwara
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Ryuichi Masuno
- Department of Radiology, Tokyo Medical University Hospital, Tokyo, Japan
| | - Takafumi Yamada
- Department of Radiology, Tokyo Medical University Hospital, Tokyo, Japan
| | | | - Naohiro Kajiwara
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Tatsuo Ohira
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
| | - Norihiko Ikeda
- Department of Surgery, Tokyo Medical University Hospital, Tokyo, Japan
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Eberhard M, Stocker D, Milanese G, Martini K, Nguyen-Kim TDL, Wurnig MC, Frauenfelder T, Baumueller S. Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study. J Thorac Dis 2019; 11:3515-3524. [PMID: 31559058 DOI: 10.21037/jtd.2019.08.12] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs. Methods CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDIvol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP). Results Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP. Conclusions Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDIvol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDIvol: 0.10 mGy).
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Affiliation(s)
- Matthias Eberhard
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Daniel Stocker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Gianluca Milanese
- Division of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Katharina Martini
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Thi Dan Linh Nguyen-Kim
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Stephan Baumueller
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
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