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Moon JW, Song YH, Kim YN, Woo JY, Son HJ, Hwang HS, Lee SH. [ 18F]FDG PET/CT is useful in discriminating invasive adenocarcinomas among pure ground-glass nodules: comparison with CT findings-a bicenter retrospective study. Ann Nucl Med 2024; 38:754-762. [PMID: 38795306 DOI: 10.1007/s12149-024-01944-2] [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: 04/17/2024] [Accepted: 05/15/2024] [Indexed: 05/27/2024]
Abstract
PURPOSE Predicting the malignancy of pure ground-glass nodules (GGNs) using CT is challenging. The optimal role of [18F]FDG PET/CT in this context has not been clarified. We compared the performance of [18F]FDG PET/CT in evaluating GGNs for predicting invasive adenocarcinomas (IACs) with CT. METHODS From June 2012 to December 2020, we retrospectively enrolled patients with pure GGNs on CT who underwent [18F]FDG PET/CT within 90 days. Overall, 38 patients with 40 ≥ 1-cm GGNs were pathologically confirmed. CT images were analyzed for size, attenuation, uniformity, shape, margin, tumor-lung interface, and internal/surrounding characteristics. Visual [18F]FDG positivity, maximum standardized uptake value (SUVmax), and tissue fraction-corrected SUVmax (SUVmaxTF) were evaluated on PET/CT. RESULTS The histopathology of the 40 GGNs were: 25 IACs (62.5%), 9 minimally invasive adenocarcinomas (MIA, 22.5%), and 6 adenocarcinomas in situ (AIS, 15.0%). No significant differences were found in CT findings according to histopathology, whereas visual [18F]FDG positivity, SUVmax, and SUVmaxTF were significantly different (P=0.001, 0.033, and 0.018, respectively). The size, visual [18F]FDG positivity, SUVmax, and SUVmaxTF showed significant diagnostic performance to predict IACs (area under the curve=0.693, 0.773, 0.717, and 0.723, respectively; P=0.029, 0.001, 0.018, and 0.013, respectively). In the multivariate logistic regression analysis, visual [18F]FDG positivity discriminated IACs among GGNs among various CT and PET findings (P=0.008). CONCLUSIONS [18F]FDG PET/CT demonstrated superior diagnostic performance compared to CT in differentiating IAC from AIS/MIA among pure GGNs, thus it has the potential to guide the proper management of patients with pure GGNs.
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Affiliation(s)
- Jung Won Moon
- Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1 Singil-Ro, Yeongdeungpo-Gu, Seoul, 07441, Republic of Korea
| | - Yun Hye Song
- Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1 Singil-Ro, Yeongdeungpo-Gu, Seoul, 07441, Republic of Korea
| | - Yoo Na Kim
- Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1 Singil-Ro, Yeongdeungpo-Gu, Seoul, 07441, Republic of Korea
| | - Ji Young Woo
- Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1 Singil-Ro, Yeongdeungpo-Gu, Seoul, 07441, Republic of Korea
| | - Hye Joo Son
- Department of Nuclear Medicine, Dankook University Medical Center, Cheonan, Chungnam, Republic of Korea
| | - Hee Sung Hwang
- Department of Nuclear Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 22 Gwanpyeong-ro 170 beon-gil, Dongan-gu,Anyang-si, Gyeonggi-do, 14068, Republic of Korea.
| | - Suk Hyun Lee
- Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1 Singil-Ro, Yeongdeungpo-Gu, Seoul, 07441, Republic of Korea.
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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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Choi Y, Kim SH, Kim KH, Choi Y, Park SG, Sohn I, Kim HS, Um SW, Lee HY. Clinical T category for lung cancer staging: A pragmatic approach for real-world practice. Thorac Cancer 2020; 11:3555-3565. [PMID: 33075213 PMCID: PMC7705618 DOI: 10.1111/1759-7714.13701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/27/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND To determine which components should be measured and which window settings are appropriate for computerized tomography (CT) size measurements of lung adenocarcinoma (ADC) and to explore interobserver agreement and accuracy according to the eighth edition of TNM staging. METHODS A total of 165 patients with surgically resected lung ADC earlier than stage 3A were included in this study. One radiologist and two pulmonologists independently measured the total and solid sizes of components of tumors on different window settings and assessed solidity. CT measurements were compared with pathologic size measurements. RESULTS In categorizing solidity, 25% of the cases showed discordant results among observers. Measuring the total size of a lung adenocarcinoma predicted pathologic invasive components to a degree similar to measuring the solid component. Lung windows were more accurate (intraclass correlation [ICC] = 0.65-0.81) than mediastinal windows (ICC = 0.20-0.72) at predicting pathologic invasive components, especially in a part-solid nodule. Interobserver agreements for measurement of solid components were good with little significant difference (lung windows, ICC = 0.89; mediastinal windows, ICC = 0.91). A high level of interobserver agreement was seen between the radiologist and pulmonologists and between residents (from the division of pulmonology and critical care) versus a fellow (from the division of pulmonology and critical care) on different windows. CONCLUSIONS A considerable percentage (25%) of discrepancies was encountered in categorizing the solidity of lesions, which may decrease the accuracy of measurements. Lung window settings may be superior to mediastinal windows for measuring lung ADCs, with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. KEY POINTS SIGNIFICANT FINDINGS OF THE STUDY: Lung window settings are better for evaluating part-solid lung adenocarcinoma (ADC), with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. The considerable percentage (25%) of discrepancies in categorizing solidity of the lesions may also have decreased the accuracy of measurements. WHAT THIS STUDY ADDS For accurate measurement and categorization of lung ADC, robust quantitative analysis is needed rather than a simple visual assessment.
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Affiliation(s)
- Yeonu Choi
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sun-Hyung Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ki Hwan Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yeonseok Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung Goo Park
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Insuk Sohn
- Statistics and Data Center, Samsung Medical Center, Seoul, Korea
| | | | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ho Yun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Xu F, Zhu W, Shen Y, Wang J, Xu R, Qutesh C, Song L, Gan Y, Pu C, Hu H. Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma. Front Oncol 2020; 10:872. [PMID: 32850301 PMCID: PMC7432133 DOI: 10.3389/fonc.2020.00872] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/04/2020] [Indexed: 01/15/2023] Open
Abstract
Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.
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Affiliation(s)
- Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Shen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University, Ningbo, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Rui Xu
- DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China.,DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian, China
| | - Chooah Qutesh
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijiang Song
- Department of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Quantitative CT Analysis for Predicting the Behavior of Part-Solid Nodules with Solid Components Less than 6 mm: Size, Density and Shape Descriptors. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9163428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Persistent part-solid nodules (PSNs) with a solid component <6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component ≥6 mm. However, not all PSNs with a small solid component behave in the same way: some nodules exhibit an indolent course, whereas others exhibit more aggressive behavior. Thus, predicting the future behavior of this subtype of PSN remains a complex and fascinating diagnostic challenge. The main purpose of this study was to apply open-source software to investigate which quantitative computed tomography (CT) features may be useful for predicting the behavior of a select group of PSNs. We retrospectively selected 50 patients with a single PSN with a solid component <6 mm and diameter <15 mm. Computerized analysis was performed using ImageJ software for each PSN and various quantitative features were calculated from the baseline CT images. The area, perimeter, mean Feret diameter, linear mass density, circularity and solidity were significantly related to nodule growth (p ≤ 0.031). Therefore, quantitative CT analysis was helpful for predicting the future behavior of a select group of PSNs with a solid component <6 mm and diameter <15 mm.
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Ledda RE, Milanese G, Gnetti L, Borghesi A, Sverzellati N, Silva M. Spread through air spaces in lung adenocarcinoma: is radiology reliable yet? J Thorac Dis 2019; 11:S256-S261. [PMID: 30997191 DOI: 10.21037/jtd.2019.01.96] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Roberta E Ledda
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Letizia Gnetti
- Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Andrea Borghesi
- Department of Radiology, University and Spedali Civili of Brescia, Brescia, Italy
| | - Nicola Sverzellati
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mario Silva
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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Heidinger BH, Anderson KR, Nemec U, Costa DB, Gangadharan SP, VanderLaan PA, Bankier AA. Morphologic characteristics of pulmonary adenocarcinomas manifesting as pure ground-glass nodules on CT. J Thorac Dis 2017; 9:E1148-E1150. [PMID: 29313855 DOI: 10.21037/jtd.2017.11.25] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Benedikt H Heidinger
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Kevin R Anderson
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ursula Nemec
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Daniel B Costa
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Sidhu P Gangadharan
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Paul A VanderLaan
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alexander A Bankier
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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