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Ye T, Wu H, Wang S, Li Q, Gu Y, Ma J, Lin J, Kang M, Qian B, Hu H, Zhang Y, Sun Y, Zhang Y, Xiang J, Li Y, Shen X, Wang Z, Chen H. Radiologic Identification of Pathologic Tumor Invasion in Patients With Lung Adenocarcinoma. JAMA Netw Open 2023; 6:e2337889. [PMID: 37843862 PMCID: PMC10580106 DOI: 10.1001/jamanetworkopen.2023.37889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/28/2023] [Indexed: 10/17/2023] Open
Abstract
Importance It is currently unclear whether high-resolution computed tomography can preoperatively identify pathologic tumor invasion for ground-glass opacity lung adenocarcinoma. Objectives To evaluate the diagnostic value of high-resolution computed tomography for identifying pathologic tumor invasion for ground-glass opacity featured lung tumors. Design, Setting, and Participants This prospective, multicenter diagnostic study enrolled patients with suspicious malignant ground-glass opacity nodules less than or equal to 30 mm from November 2019 to July 2021. Thoracic high-resolution computed tomography was performed, and pathologic tumor invasion (invasive adenocarcinoma vs adenocarcinoma in situ or minimally invasive adenocarcinoma) was estimated before surgery. Pathologic nonadenocarcinoma, benign diseases, or those without surgery were excluded from analyses; 673 patients were recruited, and 620 patients were included in the analysis. Statistical analysis was performed from October 2021 to January 2022. Exposure Patients were grouped according to pathologic tumor invasion. Main Outcomes and Measures Primary end point was diagnostic yield for pathologic tumor invasion. Secondary end point was diagnostic value of radiologic parameters. Results Among 620 patients (442 [71.3%] female; mean [SD] age, 53.5 [12.0] years) with 622 nodules, 287 (46.1%) pure ground-glass opacity nodules and 335 (53.9%) part-solid nodules were analyzed. The median (range) size of nodules was 12.1 (3.8-30.0) mm; 47 adenocarcinomas in situ, 342 minimally invasive adenocarcinomas, and 233 invasive adenocarcinomas were confirmed. Overall, diagnostic accuracy was 83.0% (516 of 622; 95% CI, 79.8%-85.8%), diagnostic sensitivity was 82.4% (192 of 233; 95% CI, 76.9%-87.1%), and diagnostic specificity was 83.3% (324 of 389; 95% CI, 79.2%-86.9%). For tumors less than or equal to 10 mm, 3.6% (8 of 224) were diagnosed as invasive adenocarcinomas. The diagnostic accuracy was 96.0% (215 of 224; 95% CI, 92.5%-98.1%), diagnostic specificity was 97.2% (210 of 216; 95% CI, 94.1%-99.0%); for tumors greater than 20 mm, 6.9% (6 of 87) were diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. The diagnostic accuracy was 93.1% (81 of 87; 95% CI, 85.6%-97.4%) and diagnostic sensitivity was 97.5% (79 of 81; 95% CI, 91.4%-99.7%). For tumors between 10 to 20 mm, the diagnostic accuracy was 70.7% (220 of 311; 95% CI, 65.3%-75.7%), diagnostic sensitivity was 75.0% (108 of 144; 95% CI, 67.1%-81.8%), and diagnostic specificity was 67.1% (112 of 167; 95% CI, 59.4%-74.1%). Tumor size (odds ratio, 1.28; 95% CI, 1.18-1.39) and solid component size (odds ratio, 1.31; 95% CI, 1.22-1.42) could each independently serve as identifiers of pathologic invasive adenocarcinoma. When the cutoff value of solid component size was 6 mm, the diagnostic sensitivity was 84.6% (95% CI, 78.8%-89.4%) and specificity was 82.9% (95% CI, 75.6%-88.7%). Conclusions and relevance In this diagnostic study, radiologic analysis showed good performance in identifying pathologic tumor invasion for ground-glass opacity-featured lung adenocarcinoma, especially for tumors less than or equal to 10 mm and greater than 20 mm; these results suggest that a solid component size of 6 mm could be clinically applied to distinguish pathologic tumor invasion.
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Affiliation(s)
- Ting Ye
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haoxuan Wu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shengping Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qiao Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yajia Gu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Junjie Ma
- Department of Thoracic Surgery, The Second Hospital of Liaocheng Affiliated to Shandong First Medical University, Linqing, Shandong Province, China
| | - Jihong Lin
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Mingqiang Kang
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Bin Qian
- Department of Thoracic Surgery, Jiangdu People’s Hospital of Yangzhou, Yangzhou, Jiangsu Province, China
| | - Hong Hu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yang Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yihua Sun
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yawei Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaqing Xiang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuan Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xuxia Shen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zezhou Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haiquan Chen
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Cui X, Zheng S, Zhang W, Fan S, Wang J, Song F, Liu X, Zhu W, Ye Z. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT. Eur Radiol 2023:10.1007/s00330-023-09432-3. [PMID: 36723725 DOI: 10.1007/s00330-023-09432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/31/2022] [Accepted: 01/02/2023] [Indexed: 02/02/2023]
Abstract
OBJECTIVES This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT. METHODS A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately. RESULTS Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts. CONCLUSIONS We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment. KEY POINTS • Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Sunyi Zheng
- Artificial Intelligence and Biomedical Image Analysis Lab, School of Engineering, Westlake University, Hangzhou, People's Republic of China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, People's Republic of China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Jing Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, People's Republic of China
| | - Feipeng Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Xu Liu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Weijie Zhu
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China.
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Cui X, Heuvelmans MA, Sidorenkov G, Zhao Y, Fan S, Groen HJM, Dorrius MD, Oudkerk M, de Bock GH, Vliegenthart R, Ye Z. A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population. J Thorac Dis 2021; 13:4407-4417. [PMID: 34422367 PMCID: PMC8339765 DOI: 10.21037/jtd-21-588] [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: 04/03/2021] [Accepted: 06/25/2021] [Indexed: 11/14/2022]
Abstract
Background To develop and validate a contrast-enhanced CT based classification tree model for classifying solid lung tumors in clinical patients into malignant or benign. Methods Between January 2015 and October 2017, 827 pathologically confirmed solid lung tumors (487 malignant, 340 benign; median size, 27.0 mm, IQR 18.0–39.0 mm) from 827 patients from a dedicated Chinese cancer hospital were identified. Nodules were divided randomly into two groups, a training group (575 cases) and a testing group (252 cases). CT characteristics were collected by two radiologists, and analyzed using a classification and regression tree (CART) model. For validation, we used the decision analysis threshold to evaluate the classification performance of the CART model and radiologist’s diagnosis (benign; malignant) in the testing group. Results Three out of 19 characteristics [margin (smooth; slightly lobulated/lobulated/spiculated), and shape (round/oval; irregular), subjective enhancement (no/uniform enhancement; heterogeneous enhancement)] were automatically generated by the CART model for classifying solid lung tumors. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the CART model is 98.5%, 58.1%, 80.6%, 98.6%, 79.8%, and 90.4%, 54.7%, 82.4% 98.5%, 74.2% for the radiologist’s diagnosis by using three-threshold decision analysis. Conclusions Tumor margin and shape, and subjective tumor enhancement were the most important CT characteristics in the CART model for classifying solid lung tumors as malignant. The CART model had higher discriminatory power than radiologist’s diagnosis. The CART model could help radiologists making recommendations regarding follow-up or surgery in clinical patients with a solid lung tumor.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Tianjin, China.,Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department of Pulmonology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Grigory Sidorenkov
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Yingru Zhao
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Tianjin, China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Tianjin, China
| | - Harry J M Groen
- Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Tianjin, China
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