1
|
Chang YC, Chen PT, Hsieh MS, Huang YS, Ko WC, Lin MW, Hsu HH, Chen JS, Chang YC. Discrimination of invasive lung adenocarcinoma from Lung-RADS category 2 nonsolid nodules through visual assessment: a retrospective study. Eur Radiol 2024; 34:3453-3461. [PMID: 37914975 DOI: 10.1007/s00330-023-10317-8] [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: 02/10/2023] [Revised: 09/11/2023] [Accepted: 09/24/2023] [Indexed: 11/03/2023]
Abstract
OBJECTIVES Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category. METHODS This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)-guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant. RESULTS A total of 242 NSNs (mean diameter, 7.6 ± 2.8 mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively. CONCLUSIONS The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs. CLINICAL RELEVANCE STATEMENT The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions. KEY POINTS • Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window-visible and the bone window-invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window.
Collapse
Affiliation(s)
- Yu-Chien Chang
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Po-Ting Chen
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yu-Sen Huang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Wei-Chun Ko
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hsao-Hsun Hsu
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan.
- Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan.
| |
Collapse
|
2
|
Sun JD, Sugarbaker E, Byrne SC, Gagné A, Leo R, Swanson SJ, Hammer MM. Clinical Outcomes of Resected Pure Ground-Glass, Heterogeneous Ground-Glass, and Part-Solid Pulmonary Nodules. AJR Am J Roentgenol 2024; 222:e2330504. [PMID: 38323785 PMCID: PMC11161307 DOI: 10.2214/ajr.23.30504] [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] [Indexed: 02/08/2024]
Abstract
BACKGROUND. Increased (but not definitively solid) attenuation within pure ground-glass nodules (pGGNs) may indicate invasive adenocarcinoma and the need for resection rather than surveillance. OBJECTIVE. The purpose of this study was to compare the clinical outcomes among resected pGGNs, heterogeneous ground-glass nodules (GGNs), and part-solid nodules (PSNs). METHODS. This retrospective study included 469 patients (335 female patients and 134 male patients; median age, 68 years [IQR, 62.5-73.5 years]) who, between January 2012 and December 2020, underwent resection of lung adenocarcinoma that appeared as a subsolid nodule on CT. Two radiologists, using lung windows, independently classified each nodule as a pGGN, a heterogeneous GGN, or a PSN, resolving discrepancies through discussion. A heterogeneous GGN was defined as a GGN with internal increased attenuation not quite as dense as that of pulmonary vessels, and a PSN was defined as having an internal solid component with the same attenuation as that of the pulmonary vessels. Outcomes included pathologic diagnosis of invasive adenocarcinoma, 5-year recurrence rates (locoregional or distant), and recurrence-free survival (RFS) and overall survival (OS) over 7 years, as analyzed by Kaplan-Meier and Cox proportional hazards regression analyses, with censoring of patients with incomplete follow-up. RESULTS. Interobserver agreement for nodule type, expressed as a kappa coefficient, was 0.69. Using consensus assessments, 59 nodules were pGGNs, 109 were heterogeneous GGNs, and 301 were PSNs. The frequency of invasive adenocarcinoma was 39.0% in pGGNs, 67.9% in heterogeneous GGNs, and 75.7% in PSNs (for pGGNs vs heterogeneous GGNs, p < .001; for pGGNs vs PSNs, p < .001; and for heterogeneous GGNs vs PSNs, p = .28). The 5-year recurrence rate was 0.0% in patients with pGGNs, 6.3% in those with heterogeneous GGNs, and 10.8% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .06; for pGGNs vs PSNs, p = .02; and for heterogeneous GGNs vs PSNs, p = .18). At 7 years, RFS was 97.7% in patients with pGGNs, 82.0% in those with heterogeneous GGNs, and 79.4% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .02; for pGGNs vs PSNs, p = .006; and for heterogeneous GGNs vs PSNs, p = .40); OS was 98.0% in patients with pGGNs, 84.6% in those with heterogeneous GGNs, and 82.9% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .04; for pGGNs vs PSNs, p = .01; and for heterogeneous GGNs vs PSNs, p = .50). CONCLUSION. Resected pGGNs had excellent clinical outcomes. Heterogeneous GGNs had relatively worse outcomes, more closely resembling outcomes for PSNs. CLINICAL IMPACT. The findings support surveillance for truly homogeneous pGGNs versus resection for GGNs showing internal increased attenuation even if not having a true solid component.
Collapse
Affiliation(s)
| | | | - Suzanne C. Byrne
- Departments of Radiology (J.D.S., S.C.B., M.M.H.), Surgery (E.S., R.L., S.J.S.), and Pathology (A.G.), Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115
| | - Andréanne Gagné
- Departments of Radiology (J.D.S., S.C.B., M.M.H.), Surgery (E.S., R.L., S.J.S.), and Pathology (A.G.), Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115
| | - Rachel Leo
- Departments of Radiology (J.D.S., S.C.B., M.M.H.), Surgery (E.S., R.L., S.J.S.), and Pathology (A.G.), Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115
| | | | | |
Collapse
|
3
|
Liu SZ, Yang SH, Ye M, Fu BJ, Lv FJ, Chu ZG. Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions. Cancer Imaging 2024; 24:47. [PMID: 38566150 PMCID: PMC10985942 DOI: 10.1186/s40644-024-00694-8] [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: 11/29/2023] [Accepted: 03/29/2024] [Indexed: 04/04/2024] Open
Abstract
PURPOSE To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.
Collapse
Affiliation(s)
- Si-Zhu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
| | - Shi-Hai Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
- Department of Radiology, People's Hospital of Nanchuan district, 16# South street, Nanchuan district, 408400, Chongqing, China
| | - Min Ye
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
- Department of Radiology, The First People's Hospital of Neijiang, No.31 Tuozhong Road, Shizhong District, 641099, Neijiang, Sichuang Province, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China.
| |
Collapse
|
4
|
Lin Y, Lin G, Peng MT, Kuo CT, Wan YL, Cherng WJ. The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels. J Thorac Imaging 2024; 39:111-118. [PMID: 37982516 DOI: 10.1097/rti.0000000000000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
PURPOSE To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. PATIENTS AND METHODS Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ). RESULTS The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively). CONCLUSIONS There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.
Collapse
Affiliation(s)
- Yenpo Lin
- Department of Medical Imaging and Intervention
| | - Gigin Lin
- Department of Medical Imaging and Intervention
| | | | - Chi-Tai Kuo
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | | | - Wen-Jin Cherng
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| |
Collapse
|
5
|
Chang GC, Chiu CH, Yu CJ, Chang YC, Chang YH, Hsu KH, Wu YC, Chen CY, Hsu HH, Wu MT, Yang CT, Chong IW, Lin YC, Hsia TC, Lin MC, Su WC, Lin CB, Lee KY, Wei YF, Lan GY, Chan WP, Wang KL, Wu MH, Tsai HH, Chian CF, Lai RS, Shih JY, Wang CL, Hsu JS, Chen KC, Chen CK, Hsia JY, Peng CK, Tang EK, Hsu CL, Chou TY, Shen WC, Tsai YH, Tsai CM, Chen YM, Lee YC, Chen HY, Yu SL, Chen CJ, Wan YL, Hsiung CA, Yang PC. Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study. THE LANCET. RESPIRATORY MEDICINE 2024; 12:141-152. [PMID: 38042167 DOI: 10.1016/s2213-2600(23)00338-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/30/2023] [Accepted: 09/08/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUND In Taiwan, lung cancers occur predominantly in never-smokers, of whom nearly 60% have stage IV disease at diagnosis. We aimed to assess the efficacy of low-dose CT (LDCT) screening among never-smokers, who had other risk factors for lung cancer. METHODS The Taiwan Lung Cancer Screening in Never-Smoker Trial (TALENT) was a nationwide, multicentre, prospective cohort study done at 17 tertiary medical centres in Taiwan. Eligible individuals had negative chest radiography, were aged 55-75 years, had never smoked or had smoked fewer than 10 pack-years and stopped smoking for more than 15 years (self-report), and had one of the following risk factors: a family history of lung cancer; passive smoke exposure; a history of pulmonary tuberculosis or chronic obstructive pulmonary disorders; a cooking index of 110 or higher; or cooking without using ventilation. Eligible participants underwent LDCT at baseline, then annually for 2 years, and then every 2 years up to 6 years thereafter, with follow-up assessments at each LDCT scan (ie, total follow-up of 8 years). A positive scan was defined as a solid or part-solid nodule larger than 6 mm in mean diameter or a pure ground-glass nodule larger than 5 mm in mean diameter. Lung cancer was diagnosed through invasive procedures, such as image-guided aspiration or biopsy or surgery. Here, we report the results of 1-year follow-up after LDCT screening at baseline. The primary outcome was lung cancer detection rate. The p value for detection rates was estimated by the χ2 test. Univariate and multivariable logistic regression analyses were used to assess the association between lung cancer incidence and each risk factor. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of LDCT screening were also assessed. This study is registered with ClinicalTrials.gov, NCT02611570, and is ongoing. FINDINGS Between Dec 1, 2015, and July 31, 2019, 12 011 participants (8868 females) were enrolled, of whom 6009 had a family history of lung cancer. Among 12 011 LDCT scans done at baseline, 2094 (17·4%) were positive. Lung cancer was diagnosed in 318 (2·6%) of 12 011 participants (257 [2·1%] participants had invasive lung cancer and 61 [0·5%] had adenocarcinomas in situ). 317 of 318 participants had adenocarcinoma and 246 (77·4%) of 318 had stage I disease. The prevalence of invasive lung cancer was higher among participants with a family history of lung cancer (161 [2·7%] of 6009 participants) than in those without (96 [1·6%] of 6002 participants). In participants with a family history of lung cancer, the detection rate of invasive lung cancer increased significantly with age, whereas the detection rate of adenocarcinoma in situ remained stable. In multivariable analysis, female sex, a family history of lung cancer, and age older than 60 years were associated with an increased risk of lung cancer and invasive lung cancer; passive smoke exposure, cumulative exposure to cooking, cooking without ventilation, and a previous history of chronic lung diseases were not associated with lung cancer, even after stratification by family history of lung cancer. In participants with a family history of lung cancer, the higher the number of first-degree relatives affected, the higher the risk of lung cancer; participants whose mother or sibling had lung cancer were also at an increased risk. A positive LDCT scan had 92·1% sensitivity, 84·6% specificity, a PPV of 14·0%, and a NPV of 99·7% for lung cancer diagnosis. INTERPRETATION TALENT had a high invasive lung cancer detection rate at 1 year after baseline LDCT scan. Overdiagnosis could have occurred, especially in participants diagnosed with adenocarcinoma in situ. In individuals who do not smoke, our findings suggest that a family history of lung cancer among first-degree relatives significantly increases the risk of lung cancer as well as the rate of invasive lung cancer with increasing age. Further research on risk factors for lung cancer in this population is needed, particularly for those without a family history of lung cancer. FUNDING Ministry of Health and Welfare of Taiwan.
Collapse
Affiliation(s)
- Gee-Chen Chang
- Department of Internal Medicine, Division of Pulmonary Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan; School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan; Institute of Biomedical Sciences, National Chung Hsing University, Taichung, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Internal Medicine, Division of Chest Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chao-Hua Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Taipei Cancer Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chong-Jen Yu
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; National Taiwan University Hospital, Hsinchu, Taiwan
| | - Yeun-Chung Chang
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
| | - Ya-Hsuan Chang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli, Taiwan
| | - Kuo-Hsuan Hsu
- Division of Critical Care and Respiratory Therapy, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yu-Chung Wu
- Department of Surgery, Division of Thoracic Surgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Department of Surgery, Division of Thoracic Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chih-Yi Chen
- Department of Surgery, Division of Thoracic Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan; Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Hsian-He Hsu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ming-Ting Wu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Cheng-Ta Yang
- Department of Thoracic Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Inn-Wen Chong
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; College of Medicine, Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yu-Ching Lin
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Respiratory and Critical Care Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan; Department of Respiratory Care, Chang Gung University of Science and Technology, Taoyuan, Taiwan
| | - Te-Chun Hsia
- Department of Respiratory Therapy, China Medical University, Taichung, Taiwan; Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Meng-Chih Lin
- Division of Pulmonary and Critical Care Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University, Kaohsiung, Taiwan; Chang Gung Respirology Center of Excellence, Kaohsiung, Taiwan
| | - Wu-Chou Su
- Department of Oncology, National Cheng Kung University Hospital, Tainan, Taiwan; College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Bin Lin
- Department of Internal Medicine, Division of Chest Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Kang-Yun Lee
- Department of Pulmonary Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Internal Medicine, Division of Thoracic Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Feng Wei
- Department of Internal Medicine, E-Da Cancer Hospital, Kaohsiung, Taiwan; School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Gong-Yau Lan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
| | - Wing P Chan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Kao-Lun Wang
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Mei-Han Wu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Medical Imaging, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Hao-Hung Tsai
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan; School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chih-Feng Chian
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ruay-Sheng Lai
- Department of Internal Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Jin-Yuan Shih
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chi-Liang Wang
- Department of Thoracic Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Respiratory Therapy, Chang Gung University, Taoyuan, Taiwan
| | - Jui-Sheng Hsu
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Radiology, School of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Kun-Chieh Chen
- Department of Internal Medicine, Division of Pulmonary Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan; School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Department of Internal Medicine, Division of Chest Medicine, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Applied Chemistry, National Chi Nan University, Nantou, Taiwan
| | - Chun-Ku Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Cardiopulmonary Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Jiun-Yi Hsia
- Department of Surgery, Division of Thoracic Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan; School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chung-Kan Peng
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Department of Medical Planning, Medical Affairs Bureau Ministry of National Defense, Taipei, Taiwan
| | - En-Kuei Tang
- Department of Surgery, Division of Thoracic Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan
| | - Chia-Lin Hsu
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Teh-Ying Chou
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Pathology, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chih Shen
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung, Taiwan; Department of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan
| | - Ying-Huang Tsai
- Department of Respiratory Therapy, Chang Gung University, Taoyuan, Taiwan; Department of Pulmonary and Critical Care, Xiamen Chang Gung Hospital, Xiamen, China
| | - Chun-Ming Tsai
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan; Cathay General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Chin Lee
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Pulmonary Medicine, West Garden Hospital, Taipei, Taiwan
| | - Hsuan-Yu Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Sung-Liang Yu
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Pan-Chyr Yang
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| |
Collapse
|
6
|
Gao S, Wu Y, Li H, Zhou Y, Du C, Yang R, Wu Y. The predictive value of computed tomography value on high-resolution images in differentiating invasive from indolent lung adenocarcinoma. Transl Cancer Res 2024; 13:268-277. [PMID: 38410205 PMCID: PMC10894351 DOI: 10.21037/tcr-23-1548] [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: 08/27/2023] [Accepted: 11/17/2023] [Indexed: 02/28/2024]
Abstract
Background Invasive adenocarcinoma (IA) has a worse prognosis and different clinical management strategies compared to indolent lung adenocarcinoma including adenocarcinoma in situ (AIS) and minimally IA (MIA). The purpose of this study was to evaluate the predictive value of computed tomography (CT) value in differentiating invasive from indolent lung adenocarcinoma. Methods The pathological diagnoses and imaging data of confirmed lung adenocarcinomas manifested as lung nodules with homogeneous internal density which were surgically resected between August 2021 and July 2022 were retrospectively analyzed. Differences in CT values between invasive and indolent lung adenocarcinomas were compared in the primary cohort (n=766), and receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off value. The predictive performance of the cut-off value was evaluated in the validation cohort (n=341). Results A total of 1,107 lung nodules from 1,014 patients were included in the total cohort. The CT values had a significant difference between invasive and indolent lung adenocarcinomas (P<0.001). Using the primary cohort, we determined the optimal cut-off value of -415 Hounsfield units (HU) of the CT value based on ROC curve, which showed good discrimination between IA and AIS/MIA in both the primary and validation cohorts (sensitivity, 85.98% and 87.42%, specificity, 87.67% and 84.74%, respectively). Conclusions The CT value of >-415 HU could be an effective predictor of invasive lung adenocarcinoma, thereby providing an appropriate clinical decision guide.
Collapse
Affiliation(s)
- Shenhu Gao
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuxuan Wu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoyang Li
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuwei Zhou
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengli Du
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Rong Yang
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yihe Wu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
7
|
Liu J, Yang X, Li Y, Xu H, He C, Zhou P, Qing H. Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort. Diagnostics (Basel) 2024; 14:147. [PMID: 38248024 PMCID: PMC10814052 DOI: 10.3390/diagnostics14020147] [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: 12/09/2023] [Revised: 12/30/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, p < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, p = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.
Collapse
Affiliation(s)
| | | | | | | | | | - Peng Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China; (J.L.); (X.Y.); (Y.L.); (H.X.); (C.H.)
| | - Haomiao Qing
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China; (J.L.); (X.Y.); (Y.L.); (H.X.); (C.H.)
| |
Collapse
|
8
|
Zhao B, Wang X, Sun K, Kang H, Zhang K, Yin H, Liu K, Xiao Y, Liu S. Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images. J Thorac Imaging 2023; 38:297-303. [PMID: 37531613 DOI: 10.1097/rti.0000000000000731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
PURPOSE To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness. MATERIALS AND METHODS This retrospective study included 512 confirmed ground-glass nodules from 474 patients with 241 precursor glandular lesions (PGL), 126 minimally invasive adenocarcinomas (MIA), and 145 invasive adenocarcinomas (IAC). The pulmonary blood vessels were reconstructed on noncontrast computed tomography images using deep learning-based region-segmentation and region-growing techniques. The presence of intranodular vessels was evaluated based on the automatic calculation of vessel prevalence, vascular categories, and vessel volume percentage. Further comparisons were made between different invasive groups by the Mantel-Haenszel χ 2 test, χ 2 test, and analysis of variance. RESULTS The detection rate of intranodular vessels in PGL (33.2%) was significantly lower than that of MIA (46.8%, P = 0.011) and IAC (55.2%, P < 0.001), while the vascular categories were similar (all P > 0.05). Vascular changes were more common in IAC and MIA than in PGL, mainly in increased vessel volume percentage (12.4 ± 19.0% vs. 6.3 ± 13.1% vs. 3.9 ± 9.4%, P < 0.001). The average intranodular artery and vein volume percentage of IAC (7.5 ± 14.0% and 5.0 ± 10.1%) was higher than that of PGL (2.1 ± 6.9% and 1.7 ± 5.8%) and MIA (3.2 ± 9.1% and 3.1 ± 8.7%), with statistical significance (all P < 0.05). CONCLUSIONS The quantitative analysis of intranodular vessels on noncontrast computed tomography images demonstrated that the ground-glass nodules with increased internal vessel prevalence and volume percentages had higher possibility of tumor invasiveness.
Collapse
Affiliation(s)
- Baolian Zhao
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Ke Sun
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd, Ocean International Center, Beijing, China
| | - Kai Zhang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd, Ocean International Center, Beijing, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd, Ocean International Center, Beijing, China
| | - Kai Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Yi Xiao
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai
| |
Collapse
|
9
|
Broggi G, Salvatorelli L. Editorial: Bio-Pathological Markers in the Diagnosis and Therapy of Cancer. Cancers (Basel) 2023; 15:cancers15051484. [PMID: 36900276 PMCID: PMC10001332 DOI: 10.3390/cancers15051484] [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: 01/27/2023] [Accepted: 02/24/2023] [Indexed: 03/02/2023] Open
Abstract
Identifying novel biomarkers with diagnostic, prognostic and predictive value in terms of therapeutic response is a current topic in the clinical practice of oncologists, pathologists and medical researchers in general [...].
Collapse
|
10
|
Liu XL, Lv FJ, Fu BJ, Lin RY, Li WJ, Chu ZG. Correlations Between Inflammatory Cell Infiltration and Relative Density and the Boundary Manifestation of Pulmonary Non-Neoplastic Ground Glass Nodules. J Inflamm Res 2023; 16:1147-1155. [PMID: 36945317 PMCID: PMC10024903 DOI: 10.2147/jir.s399953] [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: 12/06/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Purpose To investigate the influence factors for the various boundary manifestations of pulmonary non-neoplastic ground glass nodules (GGNs) on computed tomography (CT). Materials and Methods From January 2015 to March 2022, a total of 280 patients with 318 non-neoplastic GGNs were enrolled. The correlations between degree of inflammatory cell infiltration and relative density (ΔCT) and the boundary manifestations of lesions were evaluated, respectively. Results Nongranulomatous nodules (283, 89.0%) with fibrous tissue proliferation and/or inflammatory cells as the predominant pathological findings were the most common non-neoplastic GGNs, followed by granulomatous nodules (28, 8.8%). Among nongranulomatous GGNs, cases with more and less/no inflammatory cells were 15 (10.9%) and 122 (89.1%) in 137 well-defined ones with smooth margin, 16 (24.6%) and 49 (75.4%) in 65 well-defined ones with coarse margin, 43 (91.5%) and 4 (8.5%) in 47 ill-defined ones with higher ΔCT (>151HU), and 4 (11.8%) and 30 (88.2%) in 34 ill-defined ones with lower ΔCT (< 151HU). The proportion of cases with more inflammatory cells in well-defined nodules was similar to that in ill-defined ones with lower ΔCT (P = 0.587) but significantly lower than that in ill-defined ones with higher ΔCT (P < 0.001). Among the granulomatous nodules, ill-defined cases with higher ΔCT (16, 57.1%) were the most common, and they (7/8, 87.5%) frequently had changes during short-term follow-up. Conclusion Nongranulomatous nodules are the most common non-neoplastic GGNs, their diverse boundary manifestations closely correlate with degree of inflammatory cell infiltration and density difference.
Collapse
Affiliation(s)
- Xiang-Ling Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Correspondence: Zhi-Gang Chu, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86 18723032809, Fax +86 23 68811487, Email
| |
Collapse
|
11
|
Gao Z, Wang X, Zuo T, Zhang M, Zhang Z. A predictive nomogram for lymph node metastasis in part-solid invasive lung adenocarcinoma: A complement to the IASLC novel grading system. Front Oncol 2022; 12:916889. [PMID: 36046052 PMCID: PMC9423719 DOI: 10.3389/fonc.2022.916889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background The International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system for invasive lung adenocarcinoma, but lymphatic invasion was not evaluated. Meanwhile, the scope of lymph node dissection in part-solid invasive lung adenocarcinoma (PSILA) is still controversial. Therefore, this study aims to explore preoperative risk factors for lymph node metastasis in PSILA, to provide reference for intraoperative dissection of lymph nodes. Methods From 2018 to 2020, clinical data of patients (stage cN0) consecutively diagnosed as PSILA were retrospectively analyzed and classified according to the novel grading system. Logistic regression was conducted to screen the clinicopathological factors of lymph node metastasis in PSILA. Results A large cohort of 960 patients with PSILA who underwent lobectomy or sub-lobectomy were enrolled. By logistic regression analyses, solid part size, bronchial cutoff sign, spiculation, and carbohydrate antigen 199 (CA199) were eventually identified as independent risk factors for lymph node metastasis, based on which a nomogram was built to preoperatively predict the risk of lymph node metastasis [area under the receiver operating characteristic curve (AUC)=0.858; concordance index = 0.857; best cutoff, 0.027]. This suggests that intraoperative systematic lymph node dissection is recommended when the predicted risk value exceeds 0.027. Reproducibility of the novel grading system was verified. Conclusions The novel IASLC grading system was applicative in real world. The nomogram for preoperative prediction of lymph node metastasis may provide reference for the lymph node dissection strategy during PSILA surgeries.
Collapse
Affiliation(s)
- Zhaoming Gao
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- Department of Thoracic Surgery, Binzhou People’s Hospital Affiliated to Shandong First Medical University, Binzhou, China
| | - Xiaofei Wang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Tao Zuo
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- Department of Thoracic Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, China
| | - Mengzhe Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zhenfa Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
- *Correspondence: Zhenfa Zhang,
| |
Collapse
|
12
|
Correlation Analysis of Computed Tomography Features and Pathological Types of Multifocal Ground-Glass Nodular Lung Adenocarcinoma. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7267036. [PMID: 35928980 PMCID: PMC9345702 DOI: 10.1155/2022/7267036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/24/2022] [Accepted: 07/01/2022] [Indexed: 11/17/2022]
Abstract
To investigate the correlation between computed tomography (CT) image characteristics of multiple lung ground-glass nodules (GGNs) and pathological classification, the CT image data of multiple lung GGN patients confirmed by pathology (n = 132) in our hospital were collected. The imaging features of GGNs were analyzed by qualified physicians, including lesion size (diameter, volume, and mass), location, CT values (mean and relative CT values), lesion morphology (round and irregular), marginal structure (pagination and burr), internal structure (bronchial inflation sign), and adjacent structure (pleural depression). CT imaging analysis was performed for the subtype of infiltrating adenocarcinoma (IAC). In CT findings, GGNs were greatly different from adenomatous hyperplasia (AAH), pure GGN adenocarcinoma in situ (AIS), and microinvasive adenocarcinoma (MIA) in terms of marginal structure, lesion morphology, internal structure, adjacent structure, and size (P < 0.05). The mean and relative CT values of mural adenocarcinoma, acinar adenocarcinoma, and papillary adenocarcinoma of IAC subtypes were greatly different from those of AAH/AIS/MIA (P < 0.05). In summary, the CT images of GGNs can be used as the basis for the differentiation of AAH, AIS, and MIA early noninvasive types and IAC invasive types, and the CT value of the IAC subtype can be used as the basis for the classification and differentiation of IAC pathological subtypes.
Collapse
|
13
|
A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules. Clin Radiol 2022; 77:e741-e748. [PMID: 35840455 DOI: 10.1016/j.crad.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/10/2022] [Accepted: 06/01/2022] [Indexed: 11/20/2022]
Abstract
AIM To investigate and compare the performance of conventional, radiomic, combined, and delta-radiomic features to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs). MATERIALS AND METHODS The present retrospective study included 216 GGNs confirmed surgically as pulmonary adenocarcinomas. All the thin-section computed tomography (CT) images were imported into the software of the United Imaging Intelligence research portal, and radiomic features were extracted with three-dimensional (3D) regions of interest. Least Absolute Shrinkage and Selection Operator was used to select the optimal radiomic features. Four models were constructed, including conventional, radiomic, combined conventional and radiomic, and delta-radiomic models. The receiver operating characteristic curves were built to evaluate the validity of these. RESULTS The type, long diameter, shape, margin, vacuole, air bronchus, vascular convergence, and pleural traction exhibited significant differences between pre-invasive lesions (PILs)/minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) groups were selected for conventional model building. Nine radiomic features were selected to build the radiomic model. The four models indicated optimal performance (AUC > 0.7). The radiomic and combined models exhibited the highest diagnostic efficiency, and their AUC were 0.89 and 0.88 in the training set, and 0.87 and 0.88 in the validation set, respectively. The delta-radiomic model indicated that the AUC was 0.83 in the training set, and 0.76 in the validation set. Finally, the conventional model exhibited an AUC in the training and validation sets of 0.78 and 0.76. CONCLUSIONS The radiomic model and combined model, in particular, and the delta-radiomic model all demonstrated improved diagnostic efficiency in differentiating IA from PIL/MIA than that of the conventional model.
Collapse
|
14
|
Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs. Biosci Rep 2022; 42:230629. [PMID: 35005775 PMCID: PMC8766821 DOI: 10.1042/bsr20212416] [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: 10/15/2021] [Revised: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 12/05/2022] Open
Abstract
Objective: To explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated. Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor–lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05). Conclusion: AI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.
Collapse
|