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Ping X, Jiang N, Meng Q, Hu C. Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images. Tomography 2024; 10:1042-1053. [PMID: 39058050 PMCID: PMC11280730 DOI: 10.3390/tomography10070078] [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: 05/21/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.
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Affiliation(s)
| | | | | | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Suzhou 215006, China; (X.P.); (N.J.); (Q.M.)
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Zhang C, Zhou H, Li M, Yang X, Liu J, Dai Z, Ma H, Wang P. The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules. Front Oncol 2024; 14:1427404. [PMID: 39015490 PMCID: PMC11250261 DOI: 10.3389/fonc.2024.1427404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 06/21/2024] [Indexed: 07/18/2024] Open
Abstract
Objectives This study aimed to explore the value of radiomics nomogram based on computed tomography (CT) on the diagnosis of benign and malignant solitary indeterminate smoothly marginated solid pulmonary nodules (SMSPNs). Methods This study retrospectively reviewed 205 cases with solitary indeterminate SMSPNs on CT, including 112 cases of benign nodules and 93 cases of malignant nodules. They were divided into training (n=143) and validation (n=62) cohorts based on different CT scanners. Radiomics features of the nodules were extracted from the lung window CT images. The variance threshold method, SelectKBest, and least absolute shrinkage and selection operator were used to select the key radiomics features to construct the rad-score. Through multivariate logistic regression analysis, a nomogram was built by combining rad-score, clinical factors, and CT features. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC). Results A total of 19 radiomics features were selected to construct the rad-score, and the nomogram was constructed by the rad-score, one clinical factor (history of malignant tumor), and three CT features (including calcification, pleural retraction, and lobulation). The nomogram performed better than the radiomics model, clinical model, and experienced radiologists who specialized in thoracic radiology for nodule diagnosis. The AUC values of the nomogram were 0.942 in the training cohort and 0.933 in the validation cohort. The calibration curve and decision curve showed that the nomogram demonstrated good consistency and clinical applicability. Conclusion The CT-based radiomics nomogram achieved high efficiency in the preoperative diagnosis of solitary indeterminate SMSPNs, and it is of great significance in guiding clinical decision-making.
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Affiliation(s)
- Chengzhou Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Huihui Zhou
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Mengfei Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Xinyu Yang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, China
| | - Jinling Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Ping Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
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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.
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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.
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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.
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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.)
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Wu S, Fan X, Li X, Luo TY, Li XH, Li Q. Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm. Insights Imaging 2024; 15:6. [PMID: 38191718 PMCID: PMC10774240 DOI: 10.1186/s13244-023-01585-5] [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: 06/09/2023] [Accepted: 11/25/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES To evaluate the clinical and non-contrast computed tomography (CT) features of patients with benign pulmonary subsolid nodules (SSNs) with a solid component ≤ 5 mm and their development trends via follow-up CT. METHODS We retrospectively collected 436 data from patients who had SSNs with a solid component ≤ 5 mm, including 69 with absorbable benign SSNs (AB-SSNs), 70 with nonabsorbable benign SSNs (NB-SSNs), and 297 with malignant SSNs (M-SSNs). Models 1, 2, and 3 for distinguishing the different types of SSNs were then developed and validated. RESULTS Patients with AB-SSNs were younger and exhibited respiratory symptoms more frequently than those with M-SSNs. The frequency of nodules detected during follow-up CT was in the following order: AB-SSNs > NB-SSNs > M-SSNs. NB-SSNs were smaller than M-SSNs, and ill-defined margins were more frequent in AB-SSNs than in NB-SSNs and M-SSNs. Benign SSNs exhibited irregular shape, target sign, and lower CT values more frequently compared to M-SSNs, whereas the latter demonstrated bubble lucency more commonly compared to the former. Furthermore, AB-SSNs showed more thickened interlobular septa and satellite lesions than M-SSNs and M-SSNs had more pleural retraction than AB-SSNs (all p < 0.017). The three models had AUCs ranging 0.748-0.920 and 0.790-0.912 in the training and external validation cohorts, respectively. A follow-up CT showed nodule progression in four benign SSNs. CONCLUSIONS The three SSN types have different clinical and imaging characteristics, with some benign SSNs progressing to resemble malignancy. CRITICAL RELEVANCE STATEMENT A good understanding of the imaging features and development trends of benign SSNs may help reduce unnecessary follow-up or interventions. This retrospective study explores the CT characteristics of benign SSNs with a solid component ≤ 5 mm by comparing AB-SSNs, NB-SSNs, and M-SSNs and delineates their development trends via follow-up CT. KEY POINTS 1. Different subsolid nodule types exhibit distinct clinical and imaging features. 2. A miniscule number of benign subsolid nodules can progress to resemble malignancy. 3. Knowing the clinical and imaging features and development trends of benign subsolid nodules can improve management.
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Affiliation(s)
- Shun Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xiao Fan
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Yuzhong District, Chongqing, China
| | - Xian Li
- Department of Pathology, Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Tian-You Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xing-Hua Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Zhao Z, Liu C, Yang J, Ren G, Zhang L, Wang T. Pulmonary cryptococcosis closely mimicking lung cancer in a membranous nephropathy patient taking calcineurin inhibitor. IDCases 2023; 34:e01916. [PMID: 37867565 PMCID: PMC10585382 DOI: 10.1016/j.idcr.2023.e01916] [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: 09/10/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/24/2023] Open
Abstract
In patients with membranous nephropathy (MN), malignancy may be either the underlying disease or results of immunosuppressive therapy which may also lead to opportunistic infections including the pulmonary cryptococcosis. On CT scan, nodule is the most common feature in pulmonary cryptococcosis and it can mimic lung cancer both clinically and radiologically. Therefore, pulmonary nodular lesions caused by cryptococcosis may be easily misdiagnosed and require unnecessary surgical treatment. As such, we herein presented an isolated subpleural solitary nodule with satellite lesion that closely mimicked lung cancer on both contrast-enhanced computed tomography (CT) scan and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/CT in an MN patient under long-term tacrolimus regimen. Cryptococcosis was ascertained by the finding of oval thick-walled yeast on histopathology of the lung biopsy specimen taken during the Argon-Helium cryotherapy. Further, the pulmonary lesions progressively dissipated after antifungal treatment. Arguably, our experience may help clinicians in general and nephrologists in particular with a better understanding of the cryptococcal infection manifesting as pulmonary nodule(s) in the MN patients and contribute to more efficacious differential diagnosis against the lung cancer.
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Affiliation(s)
- ZhiPeng Zhao
- Graduate School of HeBei Medical University, Shijiazhuang 050011, China
| | - Chong Liu
- Department of Medical Imaging, the First Hospital of HeBei Medical University, ShiJiaZhuang 050030, China
| | - JianZhu Yang
- Department of Pathology, the First Hospital of HeBei Medical University, ShiJiaZhuang 050030, China
| | - GuangWei Ren
- Department of Nephrology, the First Hospital of HeBei Medical University, ShiJiaZhuang 050030, China
| | - LiHong Zhang
- Department of Nephrology, the First Hospital of HeBei Medical University, ShiJiaZhuang 050030, China
| | - Tao Wang
- Department of Nephrology, the First Hospital of HeBei Medical University, ShiJiaZhuang 050030, China
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Zhou J, Hu B, Feng W, Zhang Z, Fu X, Shao H, Wang H, Jin L, Ai S, Ji Y. An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT. NPJ Digit Med 2023; 6:119. [PMID: 37407729 DOI: 10.1038/s41746-023-00866-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 06/26/2023] [Indexed: 07/07/2023] Open
Abstract
Lung cancer screening using computed tomography (CT) has increased the detection rate of small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically meaningful to accurate assessment of the nodule histology by CT scans with advanced deep learning algorithms. However, recent studies mainly focus on predicting benign and malignant nodules, lacking of model for the risk stratification of invasive adenocarcinoma. We propose an ensemble multi-view 3D convolutional neural network (EMV-3D-CNN) model to study the risk stratification of lung adenocarcinoma. We include 1075 lung nodules (≤30 mm and ≥4 mm) with preoperative thin-section CT scans and definite pathology confirmed by surgery. Our model achieves a state-of-art performance of 91.3% and 92.9% AUC for diagnosis of benign/malignant and pre-invasive/invasive nodules, respectively. Importantly, our model outperforms senior doctors in risk stratification of invasive adenocarcinoma with 77.6% accuracy [i.e., Grades 1, 2, 3]). It provides detailed predictive histological information for the surgical management of pulmonary nodules. Finally, for user-friendly access, the proposed model is implemented as a web-based system ( https://seeyourlung.com.cn ).
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Affiliation(s)
- Jing Zhou
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China
| | - Bin Hu
- Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Wei Feng
- Department of Cardiothoracic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Zhang Zhang
- Department of Thoracic Surgery, Changsha Central Hospital, Changsha, China
| | - Xiaotong Fu
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China
| | - Handie Shao
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China
| | - Hansheng Wang
- Guanghua School of Management, Peking University, Beijing, China
| | - Longyu Jin
- Department of Cardiothoracic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Siyuan Ai
- Department of Thoracic Surgery, Beijing LIANGXIANG Hospital, Beijing, China
| | - Ying Ji
- Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
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Liu J, Xie C, Li Y, Xu H, He C, Qing H, Zhou P. The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography. Cancer Imaging 2023; 23:65. [PMID: 37349824 DOI: 10.1186/s40644-023-00577-4] [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: 03/27/2023] [Accepted: 05/29/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND There is no consensus on 3-dimensional (3D) quantification method for solid component within part-solid nodules (PSNs). This study aimed to find the optimal attenuation threshold for the 3D solid component proportion in low-dose computed tomography (LDCT), namely the consolidation/tumor ratio of volume (CTRV), basing on its correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas (PAs) according to the 5th edition of World Health Organization classification. Then we tested the ability of CTRV to predict high-risk nonmucinous PAs in PSNs, and compare its performance with 2-dimensional (2D) measures and semantic features. METHODS A total of 313 consecutive patients with 326 PSNs, who underwent LDCT within one month before surgery and were pathologically diagnosed with nonmucinous PAs, were retrospectively enrolled and were divided into training and testing cohorts according to scanners. The CTRV were automatically generated by setting a series of attenuation thresholds from - 400 to 50 HU with an interval of 50 HU. The Spearman's correlation was used to evaluate the correlation between the malignant grade of nonmucinous PAs and semantic, 2D, and 3D features in the training cohort. The semantic, 2D, and 3D models to predict high-risk nonmucinous PAs were constructed using multivariable logistic regression and validated in the testing cohort. The diagnostic performance of these models was evaluated by the area under curve (AUC) of receiver operating characteristic curve. RESULTS The CTRV at attenuation threshold of -250 HU (CTRV- 250HU) showed the highest correlation coefficient among all attenuation thresholds (r = 0.655, P < 0.001), which was significantly higher than semantic, 2D, and other 3D features (all P < 0.001). The AUCs of CTRV- 250HU to predict high-risk nonmucinous PAs were 0.890 (0.843-0.927) in the training cohort and 0.832 (0.737-0.904) in the testing cohort, which outperformed 2D and semantic models (all P < 0.05). CONCLUSIONS The optimal attenuation threshold was - 250 HU for solid component volumetry in LDCT, and the derived CTRV- 250HU might be valuable for the risk stratification and management of PSNs in lung cancer screening.
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Affiliation(s)
- Jieke Liu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Chaolian Xie
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Li
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Changjiu He
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Peng Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Li M, Xi J, Zhang H, Jin X, Fan Z, Zhan C, Feng M, Tan L, Wang Q. Ground glass nodules with scattered or eccentric island-shaped consolidations may have poor outcomes. CANCER INNOVATION 2023; 2:148-158. [PMID: 38090062 PMCID: PMC10686148 DOI: 10.1002/cai2.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 10/15/2024]
Abstract
Background To explore the effect of scattered or eccentric shaped types of ground glass opacity (GGO) on outcomes of patients with solid-dominant peripheral lung adenocarcinoma. Methods We evaluated patients with solid-dominant peripheral lung adenocarcinoma, who underwent radical surgery at Zhongshan Hospital, Fudan University, between January 2013 and December 2015. Morphologically heterogeneous solid-dominant lung adenocarcinoma in imaging findings was based on the last preoperative computed tomography (CT) scans. Endpoints were recurrence-free survival (RFS) and overall survival (OS). Kaplan-Meier analysis and the log-rank test were used to estimate survival differences. Impact factors were assessed by univariable logistic regression analysis. Results We retrospectively collected data from 200 patients, including 170 patients with central island-shaped CT imaging, 18 patients with scattered shaped CT imaging, and 12 patients with eccentric shaped CT imaging. Eleven patients experienced recurrence or metastases. Kaplan-Meier survival curves showed significant survival differences between the central island-shaped type and scattered shaped or eccentric shaped type for OS (c-stage IA: 5-year OS: 100% vs. 92.1%; HR = 0.019, p = 0.0025; p-stage IA: 100% vs. 95.2%; HR = 0.146, p = 0.1139) and RFS (c-stage IA: 5-year RFS: 100% vs. 59.7%; HR = 0.001, p < 0.0001; p-stage IA: 100% vs. 64.5%; HR < 0.001, p < 0.0001). Univariable logistic regression analysis showed that scattered and eccentric shaped types were related to poor outcomes (p < 0.012, odds ratio = 18.8). Conclusions Relative spatial position of GGO and solid components may affect patient outcomes. Patients with scattered or eccentric shaped GGO may have a poor prognosis.
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Affiliation(s)
- Ming Li
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Junjie Xi
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Huan Zhang
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Xing Jin
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Zhuoyang Fan
- Department of Interventional Radiology, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Cheng Zhan
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Mingxiang Feng
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Lijie Tan
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
- Cancer Center, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Qun Wang
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
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Yan G, Li H, Fan X, Deng J, Yan J, Qiao F, Yan G, Liu T, Chen J, Wang L, Yang Y, Li Y, Zhao L, Bhetuwal A, McClure MA, Li N, Peng C. Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study. Radiol Oncol 2023; 57:20-34. [PMID: 36795007 PMCID: PMC10039475 DOI: 10.2478/raon-2023-0008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/05/2022] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Solitary pulmonary nodules (SPNs) are one of the most common chest computed tomography (CT) abnormalities clinically. We aimed to investigate the value of non-contrast enhanced CT (NECT), contrast enhanced CT (CECT), CT perfusion imaging (CTPI), and dual- energy CT (DECT) used for differentiating benign and malignant SPNs with a multi-institutional and prospective study. PATIENTS AND METHODS Patients with 285 SPNs were scanned with NECT, CECT, CTPI and DECT. Differences between the benign and malignant SPNs on NECT, CECT, CTPI, and DECT used separately (NECT combined with CECT, DECT, and CTPI were methods of A, B, and C) or in combination (Method A + B, A + C, B + C, and A + B + C) were compared by receiver operating characteristic curve analysis. RESULTS Multimodality CT imaging showed higher performances (sensitivities of 92.81% to 97.60%, specificities of 74.58% to 88.14%, and accuracies of 86.32% to 93.68%) than those of single modality CT imaging (sensitivities of 83.23% to 85.63%, specificities of 63.56% to 67.80%, and accuracies of 75.09% to 78.25%, all p < 0.05). CONCLUSIONS SPNs evaluated with multimodality CT imaging contributes to improving the diagnostic accuracy of benign and malignant SPNs. NECT helps to locate and evaluate the morphological characteristics of SPNs. CECT helps to evaluate the vascularity of SPNs. CTPI using parameter of permeability surface and DECT using parameter of normalized iodine concentration at the venous phase both are helpful for improving the diagnostic performance.
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Affiliation(s)
- Gaowu Yan
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Hongwei Li
- Department of Radiology, The Third Hospital of Mianyang and Sichuan Mental Health Center, Mianyang, China
| | - Xiaoping Fan
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Jiantao Deng
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Jing Yan
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Fei Qiao
- Department of CT and MRI, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, China
| | - Gaowen Yan
- Department of Radiology, The First People's Hospital of Suining, Suining, China
| | - Tao Liu
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Jiankang Chen
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Lei Wang
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Yong Li
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Linwei Zhao
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Anup Bhetuwal
- Sichuan Key Laboratory of Medical Imaging and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Morgan A McClure
- Department of Radiology and Imaging; Institute of Rehabilitation and Development of Brain Function, The Second Clinical Medical College of North Sichuan Medical College Nanchong Central Hospital, Nanchong, China
| | - Na Li
- Department of Oncology, Suining Central Hospital, Suining, China
| | - Chen Peng
- Department of Gastroenterology, The First People's Hospital of Suining, Suining, China
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Lin RY, Zheng YN, Lv FJ, Fu BJ, Li WJ, Liang ZR, Chu ZG. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules. Med Phys 2023; 50:2835-2843. [PMID: 36810703 DOI: 10.1002/mp.16316] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PURPOSE This study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). METHODS The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. CONCLUSIONS Radiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.
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Affiliation(s)
- Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi-Neng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhang-Rui Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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12
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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.
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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
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Liang ZR, Ye M, Lv FJ, Fu BJ, Lin RY, Li WJ, Chu ZG. Differential diagnosis of benign and malignant patchy ground-glass opacity by thin-section computed tomography. BMC Cancer 2022; 22:1206. [DOI: 10.1186/s12885-022-10338-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022] Open
Abstract
Abstract
Background
Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis.
Methods
From January 2016 to September 2021, 226 patients with 247 patchy GGOs (103 benign and 144 malignant) confirmed by postoperative pathological examination or follow-up were retrospectively enrolled. Their clinical and CT data were reviewed, and their CT features were compared. A binary logistic regression analysis was performed to reveal the predictors of malignancy.
Results
Compared to patients with benign patchy GGOs, malignant cases were older (P < 0.001), had a lower incidence of malignant tumor history (P = 0.003), and more commonly occurred in females (P = 0.012). Based on CT images, there were significant differences in the location, distribution, density pattern, internal bronchial changes, and boundary between malignant and benign GGOs (P < 0.05). The binary logistic regression analysis revealed that the independent predictors of malignant GGOs were the following: patient age ≥ 58 years [odds ratio (OR), 2.175; 95% confidence interval (CI), 1.135–6.496; P = 0.025], locating in the upper lobe (OR, 5.481; 95%CI, 2.027–14.818; P = 0.001), distributing along the bronchovascular bundles (OR, 12.770; 95%CI, 4.062–40.145; P < 0.001), centrally distributed solid component (OR, 3.024; 95%CI, 1.124–8.133; P = 0.028), and well-defined boundary (OR, 5.094; 95%CI, 2.079–12.482; P < 0.001).
Conclusions
In older patients (≥58 years), well-defined patchy GGOs with centric solid component, locating in the upper lobe, and distributing along the bronchovascular bundles should be highly suspected as malignancy.
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14
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He C, Liu J, Li Y, Lin L, Qing H, Guo L, Hu S, Zhou P. Quantitative parameters of enhanced dual-energy computed tomography for differentiating lung cancers from benign lesions in solid pulmonary nodules. Front Oncol 2022; 12:1027985. [PMID: 36276069 PMCID: PMC9582258 DOI: 10.3389/fonc.2022.1027985] [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: 08/25/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives This study aimed to investigate the ability of quantitative parameters of dual-energy computed tomography (DECT) and nodule size for differentiation between lung cancers and benign lesions in solid pulmonary nodules. Materials and Methods A total of 151 pathologically confirmed solid pulmonary nodules including 78 lung cancers and 73 benign lesions from 147 patients were consecutively and retrospectively enrolled who underwent dual-phase contrast-enhanced DECT. The following features were analyzed: diameter, volume, Lung CT Screening Reporting and Data System (Lung-RADS) categorization, and DECT-derived quantitative parameters including effective atomic number (Zeff), iodine concentration (IC), and normalized iodine concentration (NIC) in arterial and venous phases. Multivariable logistic regression analysis was used to build a combined model. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results The independent factors for differentiating lung cancers from benign solid pulmonary nodules included diameter, Lung-RADS categorization of diameter, volume, Zeff in arterial phase (Zeff_A), IC in arterial phase (IC_A), NIC in arterial phase (NIC_A), Zeff in venous phase (Zeff_V), IC in venous phase (IC_V), and NIC in venous phase (NIC_V) (all P < 0.05). The IC_V, NIC_V, and combined model consisting of diameter and NIC_V showed good diagnostic performance with AUCs of 0.891, 0.888, and 0.893, which were superior to the diameter, Lung-RADS categorization of diameter, volume, Zeff_A, and Zeff_V (all P < 0.001). The sensitivities of IC_V, NIC_V, and combined model were higher than those of IC_A and NIC_A (all P < 0.001). The combined model did not increase the AUCs compared with IC_V (P = 0.869) or NIC_V (P = 0.633). Conclusion The DECT-derived IC_V and NIC_V may be useful in differentiating lung cancers from benign lesions in solid pulmonary nodules.
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Affiliation(s)
| | | | | | | | | | | | | | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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15
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Zhu Y, Yang L, Li Q, Chen B, Hao Q, Sun X, Tan J, Li W. Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations. J Evid Based Med 2022; 15:106-122. [PMID: 35794787 DOI: 10.1111/jebm.12481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting. METHODS The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs. RESULTS A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs. CONCLUSION Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.
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Affiliation(s)
- Yuqi Zhu
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qianrui Li
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qiukui Hao
- The Center of Gerontology and Geriatrics, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Tan
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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Zhang X, Qiao W, Kang Z, Pan C, Chen Y, Li K, Shen W, Zhang L. CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model. Int J Gen Med 2022; 15:5455-5463. [PMID: 35692354 PMCID: PMC9176337 DOI: 10.2147/ijgm.s368344] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 05/23/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model. Methods Fifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model. Results Univariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05). Conclusion Compared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.
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Affiliation(s)
- Xiuming Zhang
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Wei Qiao
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Zheng Kang
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Chunhan Pan
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Yan Chen
- Department of Pathology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Kang Li
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Wenrong Shen
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
| | - Lei Zhang
- Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
- Correspondence: Lei Zhang; Wenrong Shen, Email ;
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Liu J, Yang X, Li Y, Xu H, He C, Qing H, Ren J, Zhou P. Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening. Quant Imaging Med Surg 2022; 12:2917-2931. [PMID: 35502397 PMCID: PMC9014141 DOI: 10.21037/qims-21-912] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 02/03/2022] [Indexed: 08/04/2023]
Abstract
BACKGROUND Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS). METHODS A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. RESULTS The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively). CONCLUSIONS The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.
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Affiliation(s)
- Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Li
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Xin Z, Li B, Xue W, Lin W, Zhao Q, Zhang X. Pulmonary cryptococcosis mimicking lung cancer: 3 case report. Int J Surg Case Rep 2022; 94:106973. [PMID: 35658271 PMCID: PMC9093010 DOI: 10.1016/j.ijscr.2022.106973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Zhifei Xin
- Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Bowen Li
- Hebei General Hospital, Shijiazhuang, Hebei, China; North China University of Science and Technology, Tangshan, Hebei, China
| | - Wenfei Xue
- Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Wei Lin
- Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Qingtao Zhao
- Hebei General Hospital, Shijiazhuang, Hebei, China
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Li Y, Liu J, Yang X, Xu H, Qing H, Ren J, Zhou P. Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening. Br J Radiol 2022; 95:20211048. [PMID: 34995082 PMCID: PMC10993960 DOI: 10.1259/bjr.20211048] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/16/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). METHODS A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. RESULTS The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801-0.935), 0.929 (95% CI, 0.862-0.971), 0.941 (95% CI, 0.876-0.978), and 0.884 (95% CI, 0.805-0.938) in the primary cohort and 0.897 (95% CI, 0.768-0.968), 0.933 (95% CI, 0.815-0.986), 0.901 (95% CI, 0.773-0.970), and 0.828 (95% CI, 0.685-0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). CONCLUSION The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be pre-operative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. ADVANCES IN KNOWLEDGE The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be pre-operative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
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20
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Qiu Z, Wu Q, Wang S, Chen Z, Lin F, Zhou Y, Jin J, Xian J, Tian J, Li W. Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging. Thorac Cancer 2022; 13:602-612. [PMID: 34994091 PMCID: PMC8841714 DOI: 10.1111/1759-7714.14305] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/17/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023] Open
Abstract
Background Early identification of the malignant propensity of pulmonary ground‐glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning‐based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs. Methods This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time‐point CT scans. We developed a deep learning‐based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models. Results The deep learning model that used integrated DL‐features from initial and follow‐up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component. Conclusions Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.
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Affiliation(s)
- Zhixin Qiu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qingxia Wu
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Zhixia Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Feng Lin
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuyan Zhou
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Jin
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jinghong Xian
- Department of Clinical Research, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Tian
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
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21
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Niu R, Gao J, Shao X, Wang J, Jiang Z, Shi Y, Zhang F, Wang Y, Shao X. Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2022; 11:727094. [PMID: 34976790 PMCID: PMC8718929 DOI: 10.3389/fonc.2021.727094] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
To investigate whether the maximum standardized uptake value (SUVmax) of 18F-deoxyglucose (FDG) PET imaging can increase the diagnostic efficiency of CT radiomics-based prediction model in differentiating benign and malignant pulmonary ground-glass nodules (GGNs). We retrospectively collected 190 GGNs from 165 patients who underwent 18F-FDG PET/CT examination from January 2012 to March 2020. Propensity score matching (PSM) was performed to select GGNs with similar baseline characteristics. LIFEx software was used to extract 49 CT radiomic features, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to select parameters and establish the Rad-score. Logistic regression analysis was performed combined with semantic features to construct a CT radiomics model, which was combined with SUVmax to establish the PET + CT radiomics model. Receiver operating characteristic (ROC) was used to compare the diagnostic efficacy of different models. After PSM at 1:4, 190 GGNs were divided into benign group (n = 23) and adenocarcinoma group (n = 92). After texture analysis, the Rad-score with three CT texture features was constructed for each nodule. Compared with the Rad-score and CT radiomics model (AUC: 0.704 (95%CI: 0.562-0.845) and 0.908 (95%CI: 0.842-0.975), respectively), PET + CT radiomics model had the best diagnostic efficiency (AUC: 0.940, 95%CI: 0.889-0.990), and there was significant difference between each two of them (P = 0.001-0.030). SUVmax can effectively improve CT radiomics model performance in the differential diagnosis of benign and malignant GGNs. PET + CT radiomics might become a noninvasive and reliable method for differentiating of GGNs.
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Affiliation(s)
- Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Jianxiong Gao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Jianfeng Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Zhenxing Jiang
- Department of Radiology, the Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Yunmei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Feifei Zhang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
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22
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Li WJ, Lv FJ, Tan YW, Fu BJ, Chu ZG. Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography. Quant Imaging Med Surg 2022; 12:699-710. [PMID: 34993112 DOI: 10.21037/qims-21-145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 08/11/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Pulmonary part-solid nodules (PSNs) reportedly have a high possibility of malignancy, while benign PSNs are common. This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section computed tomography (CT) features. METHODS Patients with PSNs confirmed by postoperative pathological examination or follow-up (at the same period) were retrospectively enrolled from March 2016 to January 2020. The clinical data of patients and CT features of benign and malignant PSNs were reviewed and compared. Binary logistic regression analysis was performed to reveal the predictors of malignant PSNs. RESULTS A total of 119 PSNs in 117 patients [age (mean ± standard deviation), 56±11 years; 70 women] were evaluated. Of the 119 PSNs, 44 (37.0%) were benign, and 75 (63.0%) were malignant (12 adenocarcinomas in situ, 22 minimally invasive adenocarcinomas, and 41 invasive adenocarcinomas). There were significant differences in the patients' age and smoking history between benign and malignant PSNs. In terms of CT characteristics, malignant and benign lesions significantly differed in the following CT features: whole nodule, internal solid component, and peripheral ground-glass opacity. The binary logistic regression analysis revealed that well-defined border [odds ratio (OR), 4.574; 95% confidence interval (CI), 1.186-17.643; P=0.027] and lobulation (OR, 61.739; 95% CI, 5.230-728.860; P=0.001) of the nodule, as well as irregular shape (OR, 9.502; 95% CI, 1.788-50.482; P=0.008) and scattered distribution (OR, 13.238; 95% CI, 1.359-128.924; P=0.026) of the internal solid components were significant independent predictors distinguishing malignant PSNs. However, the lesion shape, density, and margin were similar between malignant and benign lesions. CONCLUSIONS Well-defined and lobulated PSNs with irregular and scattered solid components are highly likely to be malignant.
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Affiliation(s)
- Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi-Wen Tan
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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23
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Shao X, Niu R, Shao X, Gao J, Shi Y, Jiang Z, Wang Y. Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules. EJNMMI Phys 2021; 8:74. [PMID: 34727258 PMCID: PMC8561359 DOI: 10.1186/s40658-021-00423-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 10/25/2021] [Indexed: 12/31/2022] Open
Abstract
Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-021-00423-1.
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Affiliation(s)
- Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Jianxiong Gao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Yunmei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Zhenxing Jiang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. .,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China.
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24
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Chen F, Liu YB, Fu BJ, Lv FJ, Chu ZG. Clinical and Computed Tomography (CT) Characteristics of Pulmonary Nodules Caused by Cryptococcal Infection. Infect Drug Resist 2021; 14:4227-4235. [PMID: 34703249 PMCID: PMC8523807 DOI: 10.2147/idr.s330159] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/30/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose To investigate the clinical and computed tomography (CT) characteristics of pulmonary nodules caused by cryptococcal infection and gain a greater insight and understanding that will reduce misdiagnosis. Materials and Methods A total of 47 patients with confirmed pulmonary cryptococcosis (PC) manifested as nodules were retrospectively enrolled from January 2013 to December 2020. The clinical and CT data of patients with single and multiple nodules were analyzed and compared with emphasis on exploring the characteristics of the solitary ones. Results Among the 47 patients, single and multiple nodules were detected in 25 (53.2%) and 22 (46.8%) patients, respectively, with similar clinical characteristics. The diameter of solitary nodules was smaller than that of the largest ones of the multiple nodules (P = 0.000). Compared with multiple nodules, less solitary nodules were located in the subpleural zone (P = 0.031) and had a cavity or vacuole (P = 0.049). Regarding the solitary nodules, 22 (88.0%) and 3 (12.0%) were solid and subsolid, respectively. Eighteen (81.8%) solid solitary nodules exhibited either one of air bronchogram or vacuole, satellite lesions, and halo sign. Twenty-three (92.0%) of 25 patients with solitary nodules had follow-up CT data, and most of them had no changes (12, 52.1%) or increased in size and/or density (8, 34.8%), but only a few (3, 13.0%) decreased with or without anti-inflammatory therapy. Conclusion The clinical characteristics of patients with solitary and multiple nodules caused by PC are similar. The solitary nodules are smaller, more scattered, but less necrotic than multiple ones. For solitary nodules with air bronchogram or vacuole, satellite lesions, or halo sign, PC should be considered as one of the differential diagnosis.
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Affiliation(s)
- Fang Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.,Department of Radiology, Youyang Hospital, A Branch of The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Yuan-Bing Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.,Department of Radiology, Qijiang People's Hospital, 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
| | - Fa-Jin Lv
- 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
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25
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Shen T, Hou R, Ye X, Li X, Xiong J, Zhang Q, Zhang C, Cai X, Yu W, Zhao J, Fu X. Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning. Front Oncol 2021; 11:700158. [PMID: 34381723 PMCID: PMC8351466 DOI: 10.3389/fonc.2021.700158] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/08/2021] [Indexed: 12/28/2022] Open
Abstract
Background To develop and validate a deep learning-based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs). Materials and Methods This study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis. Results A total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885-0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers' performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877-0.939), sensitivity of 87.4%, and specificity of 80.8%. Conclusion The deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.
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Affiliation(s)
- Tianle Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Runping Hou
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaodan Ye
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoyang Li
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Junfeng Xiong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qin Zhang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Chenchen Zhang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xuwei Cai
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wen Yu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
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26
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Shi L, Shi W, Peng X, Zhan Y, Zhou L, Wang Y, Feng M, Zhao J, Shan F, Liu L. Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter. Front Oncol 2021; 11:618677. [PMID: 33968722 PMCID: PMC8096901 DOI: 10.3389/fonc.2021.618677] [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] [Received: 10/18/2020] [Accepted: 03/25/2021] [Indexed: 12/09/2022] Open
Abstract
Purpose To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter. Materials and Methods This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts. Results Five radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion This study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.
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Affiliation(s)
- Lili Shi
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Medical School, Nantong University, Nantong, China
| | - Weiya Shi
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xueqing Peng
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yi Zhan
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Linxiao Zhou
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunpeng Wang
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mingxiang Feng
- Chest Surgery Department, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jinli Zhao
- Radiology Department, Affiliated Hospital of Nantong University, Nantong, China
| | - Fei Shan
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,School of Basic Medical Sciences, and Academy of Engineering and Technology, Fudan University, Shanghai, China
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27
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Hu X, Gong J, Zhou W, Li H, Wang S, Wei M, Peng W, Gu Y. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features. Phys Med Biol 2021; 66:065015. [PMID: 33596552 DOI: 10.1088/1361-6560/abe735] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVES This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance. METHODS We first retrospectively collected 513 surgery histopathology confirmed GGNs from two centers. Among these GGNs, 100 were benign and 413 were malignant. All malignant tumors were stage I lung adenocarcinoma. To segment GGNs, we applied a deep convolutional neural network and residual architecture to train and build a 3D U-Net. Then, based on the pre-trained U-Net, we used a transfer learning approach to build a deep neural network (DNN) to classify between benign and malignant GGNs. With the GGN segmentation results generated by 3D U-Net, we also developed a CT radiomics model by adopting a series of image processing techniques, i.e. radiomics feature extraction, feature selection, synthetic minority over-sampling technique, and support vector machine classifier training/testing, etc. Finally, we applied an information fusion method to fuse the prediction scores generated by DNN based CADx model and CT-radiomics based model. To evaluate the proposed model performance, we conducted a comparison experiment by testing on an independent testing dataset. RESULTS Comparing with DNN model and radiomics model, our fusion model yielded a significant higher area under a receiver operating characteristic curve (AUC) value of 0.73 ± 0.06 (P < 0.01). The fusion model generated an accuracy of 75.6%, F1 score of 84.6%, weighted average F1 score of 70.3%, and Matthews correlation coefficient of 43.6%, which were higher than the DNN model and radiomics model individually. CONCLUSIONS Our experimental results demonstrated that (1) applying a CADx scheme was feasible to diagnosis of early-stage lung adenocarcinoma, (2) deep image features and radiomics features provided complementary information in classifying benign and malignant GGNs, and (3) it was an effective way to build DNN model with limited dataset by using transfer learning. Thus, to build a robust image analysis based CADx model, one can combine different types of image features to decode the imaging phenotypes of GGN.
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Affiliation(s)
- Xianfang Hu
- Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, 1558 Sanhuan North Road, Huzhou, Zhejiang, 313000, People's Republic of China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Zhou
- Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, 1558 Sanhuan North Road, Huzhou, Zhejiang, 313000, People's Republic of China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Meng Wei
- Medical imaging Center, The first Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu, Anhui, 241001, People's Republic of China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Li WJ, Lv FJ, Tan YW, Fu BJ, Chu ZG. Pulmonary Benign Ground-Glass Nodules: CT Features and Pathological Findings. Int J Gen Med 2021; 14:581-590. [PMID: 33679139 PMCID: PMC7930605 DOI: 10.2147/ijgm.s298517] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 01/22/2021] [Indexed: 12/18/2022] Open
Abstract
Background Some pulmonary ground-glass nodules (GGNs) are benign and frequently misdiagnosed due to lack of understanding of their CT characteristics. This study aimed to reveal the CT features and corresponding pathological findings of pulmonary benign GGNs to help improve diagnostic accuracy. Patients and Methods From March 2016 to October 2019, patients with benign GGNs confirmed by operation or follow-up were enrolled retrospectively. According to overall CT manifestations, GGNs were classified into three types: I, GGO with internal high-attenuation zone; II, nodules lying on adjacent blood vessels; and other type, lesions without obvious common characteristics. CT features and pathological findings of each nodule type were evaluated. Results Among the 40 type I, 25 type II, and 14 other type GGNs, 24 (60.0%), 19 (76.0%), and 10 (71.4%) nodules were resected, respectively. Type I GGNs were usually irregular (25 of 40, 62.5%) with only one high-attenuation zone (38 of 40, 95.0%) (main pathological components: thickened alveolar walls with inflammatory cells, fibrous tissue, and exudation), which was usually centric (24 of 40, 60.0%), having blurred margin (38 of 40, 95.0%), and connecting to blood vessels (32 of 40, 80.0%). The peripheral GGO (main pathological component: a small amount of inflammatory cell infiltration with fibrous tissue proliferation) was usually ill-defined (28 of 40, 70.0%). Type II GGNs (main pathological components: focal interstitial fibrosis with or without inflammatory cell infiltration) lying on adjacent vessel branches were usually irregular (19 of 25, 76.0%) and well defined (16 of 25, 64.0%) but showed coarse margins (15 of 16, 93.8%). Other type GGNs had various CT manifestations but their pathological findings were similar to that of type II. Conclusion For subsolid nodules with CT features manifested in type I or II GGNs, follow-up should be firstly considered in further management.
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Affiliation(s)
- Wang-Jia Li
- 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
| | - Yi-Wen Tan
- Department of Pathology, 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
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
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Liu J, Xu H, Qing H, Li Y, Yang X, He C, Ren J, Zhou P. Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules. Front Oncol 2021; 10:634298. [PMID: 33604303 PMCID: PMC7884759 DOI: 10.3389/fonc.2020.634298] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
Objectives This study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated. Methods A total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results Shape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models. Conclusions The diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.
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Affiliation(s)
- Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Li
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Wu LL, Lai JJ, Liu X, Huang YY, Lin P, Long H, Zhang LJ, Ma GW. Association between number of dissected lymph nodes and survival in stage IA non-small cell lung cancer: a propensity score matching analysis. World J Surg Oncol 2020; 18:322. [PMID: 33287841 PMCID: PMC7722454 DOI: 10.1186/s12957-020-02090-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 11/23/2020] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND For patients with stage IA non-small cell lung cancer (NSCLC) with tumor size ≤ 2 cm, the prognostic significance of the number of removed lymph nodes (NLNs) through different surgical methods remains unclear. To determine the association of NLNs with cancer-specific survival (CSS) and overall survival (OS) in patients with stage IA NSCLC with tumor size ≤ 2 cm who underwent different lung surgeries. METHODS We retrospectively enrolled 7293 patients from the Surveillance, Epidemiology and End Results database. Median NLNs was used to classify the patients into two groups: group A with NLNs ≤ 5 and group B with NLNs > 5. Propensity score matching (PSM) was performed to decrease selection bias. Kaplan-Meier analysis and Cox regression analysis were performed to identify the association between NLNs and survival outcomes. RESULTS Group B had better survival than group A in the unmatched cohort and matched cohort (all P < 0.05). Multivariable analyses revealed that the NLNs significantly affected CSS and OS of eligible cases in the unmatched cohort and matched cohort. Additionally, we found that the NLNs was a protective prognostic predictor of OS for patients who underwent wedge resection, segmental resection, or lobectomy. CONCLUSION The NLNs was a protective prognostic factor in NSCLC patients with tumor size ≤ 2 cm. We demonstrated that patients with > 5 NLNs in the cohort of wedge resection, segmental resection, or lobectomy exhibited a significantly better OS.
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Affiliation(s)
- Lei-Lei Wu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China
| | - Jia-Jian Lai
- Sun Yat-sen University, Guangzhou, 510060, P. R. China
| | - Xuan Liu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China
| | - Yang-Yu Huang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China
| | - Peng Lin
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China
| | - Hao Long
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China
| | - Lan-Jun Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China
| | - Guo-Wei Ma
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P. R. China.
- The Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, 651 Dongfengdong Road, Guangzhou, 510060, P. R. China.
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31
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Wang YH, Chen CF, Lin YK, Chiang C, Tzao C, Yen Y. Predicting malignancy: subsolid nodules detected on LDCT in a surgical cohort of East Asian patients. J Thorac Dis 2020; 12:4315-4326. [PMID: 32944344 PMCID: PMC7475597 DOI: 10.21037/jtd-20-659] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Due to widespread use of low-dose computed tomography (LDCT) screening, increasing number of patients are found to have subsolid nodules (SSNs). The management of SSNs is a clinical challenge and primarily depends on CT imaging. We seek to identify risk factors that may help clinicians determine an optimal course of management. Methods We retrospectively reviewed the characteristics of 83 SSN lesions, including 48 pure ground-glass nodules and 35 part-solid nodules, collected from 83 patients who underwent surgical resection. Results Of the 83 SSNs, 16 (19.28%) were benign and 67 (80.72%) were malignant, including 23 adenocarcinomas in situ (AIS), 16 minimally invasive adenocarcinomas (MIA), and 28 invasive adenocarcinomas (IA). Malignant lesions were found to have significantly larger diameters (P<0.05) with an optimal cut-off point of 9.24 mm. Significant indicators of malignancy include female sex (P<0.05), air bronchograms (P<0.001), spiculation (P<0.05), pleural tail sign (P<0.05), and lobulation (P<0.05). When compared with AIS/MIA combined, IA lesions were found to be larger (P<0.05) with an optimal cut-off of 12 mm, and have a higher percentage of part-solid nodules (P<0.001), pleural tail sign (P<0.001), air bronchograms (P<0.05), and lobulation (P<0.05). Further multivariate analysis found that lesion size and spiculation were independent factors for malignancy while part-solid nodules were associated with IA histology. Conclusions East Asian females are at risk of presenting with a malignant lesion even without history of heavy smoking or old age. Nodule features associated with malignancy include larger size, air bronchograms, lobulation, pleural tail sign, spiculation, and solid components. A combination of patient characteristic and LDCT features can be effectively used to guide management of patients with SSNs.
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Affiliation(s)
- Yung-Hsien Wang
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei
| | - Chieh-Feng Chen
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei.,Department of Public Health, College of Medicine, Taipei Medical University, Taipei.,Cochrane Taiwan, Taipei Medical University, Taipei.,Division of Plastic Surgery, Wan Fang Hospital, Taipei Medical University, Taipei.,Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei
| | - Yen-Kuang Lin
- Biostatistics Research Center, College of Nursing, Taipei Medical University, Taipei.,School of Nursing, College of Nursing, Taipei Medical University, Taipei
| | - Caleb Chiang
- Trinity College of Arts & Sciences, Duke University, Durham, USA
| | - Ching Tzao
- Division of Thoracic Surgery, Kuang Tien General Hospital, Taichung
| | - Yun Yen
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei.,PhD Program for Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei.,Graduate Institute of Cancer Biology and Drug Discovery, Taipei Medical University, Taipei.,Cancer Center, Taipei Municipal Wan Fang Hospital, Taipei
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de Margerie-Mellon C, Gill RR, Salazar P, Oikonomou A, Nguyen ET, Heidinger BH, Medina MA, VanderLaan PA, Bankier AA. Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models. Sci Rep 2020; 10:14585. [PMID: 32883973 PMCID: PMC7471897 DOI: 10.1038/s41598-020-70316-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 07/13/2020] [Indexed: 01/22/2023] Open
Abstract
The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.
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Affiliation(s)
| | - Ritu R Gill
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Elsie T Nguyen
- Department of Medical Imaging, Toronto General Hospital, University of Toronto, Toronto, Canada
| | - 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
| | - Mayra A Medina
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Paul A VanderLaan
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Alexander A Bankier
- Department of Radiology, UMass Memorial Medical Center, University of Massachusetts Medical School, Worcester, MA, USA
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Chen X, Feng B, Chen Y, Liu K, Li K, Duan X, Hao Y, Cui E, Liu Z, Zhang C, Long W, Liu X. A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules. Cancer Imaging 2020; 20:45. [PMID: 32641166 PMCID: PMC7346427 DOI: 10.1186/s40644-020-00320-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 06/25/2020] [Indexed: 01/15/2023] Open
Abstract
PURPOSE To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.
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Affiliation(s)
- Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Bao Feng
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
- School of electronic information and automation, Guilin University of Aerospace Technology, Guilin City, Guangxi Province 541004 People’s Republic of China
| | - Yehang Chen
- School of electronic information and automation, Guilin University of Aerospace Technology, Guilin City, Guangxi Province 541004 People’s Republic of China
| | - Kunfeng Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000 People’s Republic of China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000 People’s Republic of China
| | - Xiaobei Duan
- Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Yixiu Hao
- Department of Radiology, Guangzhou First People’s Hospital, Guangzhou City, Guangdong Province 510180 People’s Republic of China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Zhuangsheng Liu
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Chaotong Zhang
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Xueguo Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000 People’s Republic of China
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Digumarthy SR, Padole AM, Rastogi S, Price M, Mooradian MJ, Sequist LV, Kalra MK. Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? Cancer Imaging 2019; 19:36. [PMID: 31182167 PMCID: PMC6558852 DOI: 10.1186/s40644-019-0223-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 05/30/2019] [Indexed: 01/17/2023] Open
Abstract
Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Materials and methods The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. Results Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). Conclusions Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.
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Affiliation(s)
- Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. .,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Court, Suite 236, Boston, MA, 02114, USA.
| | - Atul M Padole
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Shivam Rastogi
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Melissa Price
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Meghan J Mooradian
- Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Lecia V Sequist
- Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
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Xiang Y, Sun Y, Liu Y, Han B, Chen Q, Ye X, Zhu L, Gao W, Fang W. Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods. J Thorac Dis 2019; 11:950-958. [PMID: 31019785 DOI: 10.21037/jtd.2019.01.90] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background The purpose of this study is to develop a predictive model to accurately predict the malignancy of solid solitary pulmonary nodule (SPN) by data mining methods. Methods A training cohort of 388 consecutive patients with solid SPNs was used to develop a predictive model to evaluate the malignancy of solid SPNs. By using SPSS Modeler, we utilized logistic regression (LR), artificial neural network (ANN), k-nearest neighbor (KNN), random forest (RF), and support vector machines (SVM) classifiers to build predictive models. Another cohort of 200 consecutive patients with solid SPNs was used to verify the accuracy of the predictive model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results There was no significant difference in patients' characteristics between the training cohort and the validation cohort. The AUCs of LR, ANN, KNN, RF, and SVM models for the validation cohort were 0.874±0.0280 (P=0.605), 0.833±0.0351 (P=0.104), 0.792±0.0418 (P=0.014), 0.775±0.0400 (P=0.013), and 0.890±0.0323 (reference), respectively. The SVM algorithm had the highest AUC, and the best sensitivity (90.3%), specificity (80.4%), positive predictive value (93.9%), negative predictive value (71.2%) and accuracy (88.0%) for the validation cohort among the five models. Conclusions Data mining by SVM might be a useful auxiliary algorithm in predicting malignancy of solid SPNs.
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Affiliation(s)
- Yangwei Xiang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Yifeng Sun
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Yuan Liu
- Department of Statistics Cente, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Baohui Han
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Qunhui Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Xiaodan Ye
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Li Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Wen Gao
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China.,Department of Thoracic Surgery, Shanghai Huadong Hospital, Fudan University School of Medicine, Shanghai 200030, China
| | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
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Loverdos K, Fotiadis A, Kontogianni C, Iliopoulou M, Gaga M. Lung nodules: A comprehensive review on current approach and management. Ann Thorac Med 2019; 14:226-238. [PMID: 31620206 PMCID: PMC6784443 DOI: 10.4103/atm.atm_110_19] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
In daily clinical practice, radiologists and pulmonologists are faced with incidental radiographic findings of pulmonary nodules. Deciding how to manage these findings is very important as many of them may be benign and require no further action, but others may represent early disease and importantly early-stage lung cancer and require prompt diagnosis and definitive treatment. As the diagnosis of pulmonary nodules includes invasive procedures which can be relatively minimal, such as bronchoscopy or transthoracic aspiration or biopsy, but also more invasive procedures such as thoracic surgical biopsies, and as these procedures are linked to anxiety and to cost, it is important to have clearly defined algorithms for the description, management, and follow-up of these nodules. Clear algorithms for the imaging protocols and the management of positive findings should also exist in lung cancer screening programs, which are already established in the USA and which will hopefully be established worldwide. This article reviews current knowledge on nodule definition, diagnostic evaluation, and management based on literature data and mainly recent guidelines.
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Affiliation(s)
| | - Andreas Fotiadis
- 7th Respiratory Medicine Department, Athens Chest Hospital, Athens, Greece
| | | | | | - Mina Gaga
- 7th Respiratory Medicine Department, Athens Chest Hospital, Athens, Greece
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