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Tang EK, Wu YJ, Chen CS, Wu FZ. Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time. Quant Imaging Med Surg 2024; 14:3983-3996. [PMID: 38846271 PMCID: PMC11151246 DOI: 10.21037/qims-23-1759] [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: 12/12/2023] [Accepted: 04/22/2024] [Indexed: 06/09/2024]
Abstract
Background Prediction of subsolid nodule (SSN) interval growth is crucial for clinical management and decision making in lung cancer screening program. To the best of our knowledge, no study has investigated whether volume doubling time (VDT) is an independent factor for predicting SSN interval growth, or whether its predictive power is better than that of traditional semantic methods, such as nodular diameter or type. This study aimed to investigate whether VDT could provide added value in predicting the long-term natural course of SSNs (<3 cm) regarding stage shift. Methods This retrospective study enrolled 132 patients with spectrum lesions of lung adenocarcinoma who underwent two consecutive computed tomography (CT) examinations before surgical tissue proofing between 2012 and 2021 in Kaohsiung Veterans General Hospital. The VDTs were manually calculated from the volumetric segmentation using Schwartz's approximation formula. We utilized logistic regression to identify predictors associated with stage shift progression based on the VDT parameter. Results The average duration of follow-up period was 3.629 years. A VDT-based nomogram model (model 2) based on CT semantic features, clinical characteristics, and the VDT parameter yielded an area under the curve (AUC) of 0.877 [95% confidence interval (CI): 0.807-0.928]. Compared with model 1 (CT semantic features and clinical characteristics), model 2 exhibited the better predictive performance for stage shift (AUC model 1: 0.833 versus AUC model 2: 0.877, P=0.047). In model 2, significant predictors of stage shift growth included initial nodule size [odds ratio (OR) =4.074, 95% CI: 1.368-12.135; P=0.012], SSN classification (OR =0.042; 95% CI: 0.006-0.288; P=0.001), follow-up period (OR =1.692, 95% CI: 1.337-2.140; P<0.001), and VDT classification (OR =2.327, 95% CI: 1.368-3.958; P=0.002). For the stage shift, the mean progression time for the VDT (>400 d) group was 7.595 years, and median progression time was 7.430 years. Additionally, a VDT ≤400 d is an important prognostic factor associated with aggressive growth behavior with a stage shift. Conclusions VDT is crucial for predicting SSN stage shift growth irrespective of clinical and CT semantic features. This highlights its significance in informing follow-up protocols and surgical planning, emphasizing its prognostic value in predicting SSN growth.
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Affiliation(s)
- En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung
- Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung
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Wu FZ, Wu YJ, Chen CS, Tang EK. Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features. Acad Radiol 2023; 30:2856-2869. [PMID: 37080884 DOI: 10.1016/j.acra.2023.02.033] [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: 11/15/2022] [Revised: 12/23/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
RATIONALES AND OBJECTIVES To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas. MATERIALS AND METHODS A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis. RESULTS The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth. CONCLUSION Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.
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Affiliation(s)
- Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, 70, Lien-hai Road, Kaohsiung 80424, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
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Liu YC, Liang CH, Wu YJ, Chen CS, Tang EK, Wu FZ. Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns. Diagnostics (Basel) 2023; 13:2674. [PMID: 37627933 PMCID: PMC10453827 DOI: 10.3390/diagnostics13162674] [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: 07/07/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management.
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Affiliation(s)
- Yung-Chi Liu
- Department of Radiology, Xiamen Chang Gung Hospital, Xiamen 361028, China;
- Department of Imaging Technology Division, Xiamen Chang Gung Hospital, Xiamen 361028, China
- Department of Healthcare Administration Department, Xiamen Chang Gung Hospital, Xiamen 361028, China
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112304, Taiwan;
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Education, National Sun Yat-Sen University, Kaohsiung 804241, Taiwan
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Borghesi A, Coviello FL, Scrimieri A, Ciolli P, Ravanelli M, Farina D. Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01648-z. [PMID: 37227661 DOI: 10.1007/s11547-023-01648-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
PURPOSE Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population. MATERIAL AND METHODS We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses. RESULTS In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth. CONCLUSION According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.
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Affiliation(s)
- Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy.
| | - Felice Leopoldo Coviello
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Alessandra Scrimieri
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Pietro Ciolli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
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Zhang Z, Yin F, Kang S, Tuo X, Zhang X, Han D. Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules. J Cancer Res Clin Oncol 2023:10.1007/s00432-022-04543-8. [PMID: 36595045 PMCID: PMC9808726 DOI: 10.1007/s00432-022-04543-8] [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: 09/20/2022] [Accepted: 12/16/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Mixed ground-glass lung nodules are a high-risk factor for lung adenocarcinoma. This study aimed to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs). METHOD 150 patients with GGNs confirmed by chest SDCT and surgical pathology were retrospectively analyzed. GGNs were screened by two senior radiologists by the double-blind method based on conventional CT and SDCT electron density images. Average CT values and electron density (ED) values of GGNs were measured for all, solid and ground-glass. RESULT Thirty pGGN cases determined by conventional CT were found to be mGGN on electron density images, including 23 in the invasive adenocarcinoma group (detection rate of 35.38%), which was significantly higher than that of the PGL group (14.89%, P < 0.05). In electron density images, average CT values and ED values in the PGL and invasive adenocarcinoma groups with pGGNs were no difference. The average CT value and ED value were significantly higher in the mGGN invasive adenocarcinoma group compared with the PGL group (P < 0.05). Meanwhile, ROC curve analysis of average CT value and ED value revealed AUC values for mGGN infiltration of 0.759 and 0.752. CONCLUSION SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT. Attention should be paid to invasive adenocarcinoma for lung GGNs detected as mGGNs with high average CT value or ED value.
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Affiliation(s)
- Zhenghua Zhang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Fang Yin
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shaolei Kang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiaoyu Tuo
- Pathology Department, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | | | - Dan Han
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, China.
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Zhang Z, Zhou L, Yang F, Li X. The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management. Front Oncol 2022; 12:1011712. [PMID: 36568242 PMCID: PMC9772280 DOI: 10.3389/fonc.2022.1011712] [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: 08/04/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.
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Liao RQ, Li AW, Yan HH, Lin JT, Liu SY, Wang JW, Fang JS, Liu HB, Hou YH, Song C, Yang HF, Li B, Jiang BY, Dong S, Nie Q, Zhong WZ, Wu YL, Yang XN. Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images. Front Oncol 2022; 12:1002953. [PMID: 36313666 PMCID: PMC9597322 DOI: 10.3389/fonc.2022.1002953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background Estimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs. Methods A total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain the diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth-associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified. Results The double time of diameter, volume, and mass were 711 vs. 963 days (P = 0.20), 552 vs. 621 days (P = 0.04) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760 (95% CI 0.646–0.857) in the validation set and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first-time CT to predict the growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set. Conclusion Mass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rates. A deep learning-based model has a great potential to predict the growth of SSNs.
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Affiliation(s)
- Ri-qiang Liao
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - An-wei Li
- Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China
| | - Hong-hong Yan
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jun-tao Lin
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Si-yang Liu
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jing-wen Wang
- Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China
| | | | - Hong-bo Liu
- Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China
| | - Yong-he Hou
- Yibicom Health Management Center, CVTE, Guangzhou, China
| | - Chao Song
- Yibicom Health Management Center, CVTE, Guangzhou, China
| | - Hui-fang Yang
- Yibicom Health Management Center, CVTE, Guangzhou, China
| | - Bin Li
- Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Ben-yuan Jiang
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Song Dong
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qiang Nie
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wen-zhao Zhong
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yi-long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Xue-ning Yang, ; Yi-long Wu,
| | - Xue-ning Yang
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Xue-ning Yang, ; Yi-long Wu,
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Xia T, Cai M, Zhuang Y, Ji X, Huang D, Lin L, Liu J, Yang Y, Fu G. Risk Factors for The Growth of Residual Nodule in Surgical Patients with Adenocarcinoma Presenting as Multifocal Ground-glass Nodules. Eur J Radiol 2020; 133:109332. [PMID: 33152625 DOI: 10.1016/j.ejrad.2020.109332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 09/18/2020] [Accepted: 09/30/2020] [Indexed: 11/19/2022]
Abstract
PURPOSE We aim to investigate the risk factors influencing the growth of residual nodule (RN) in surgical patients with adenocarcinoma presenting as multifocal ground-glass nodules (GGNs). METHOD From January 2014 to June 2018, we enrolled 238 patients with multiple GGNs in a retrospective review. Patients were categorized into growth group 63 (26.5%), and non-growth group 175 (73.5%). The median follow-up time was 28.2 months (range, 6.3-73.0 months). To obtain the time of RN growth and find the risk factors for growth, data such as age, gender, history of smoking, history of malignancy, type of surgery, pathology and radiological characteristics were analyzed to use Kaplan-Meier method with the log-rank test and Cox regression analysis. RESULTS The median growth time of RN was 56.0 months (95% CI, 45.0-67.0 months) in all 238 patients. Roundness (HR 4.62, 95% CI 2.20-9.68), part-solid nodule (CTR ≥ 50%) (HR 4.39, 95% CI 2.29-8.45), vascular convergence sign (HR 2.32, 95% CI 1.36-3.96) of RN, and age (HR 1.04, 95% CI 1.01-1.07) were independent predictors of further nodule growth. However, radiological characteristics and pathology of domain tumour (DT) cannot be used as indicators to predict RN growth. CONCLUSIONS RN showed an indolent growth pattern in surgical patients with multifocal GGNs. RN with a higher roundness, presence of vascular convergence sign, more solid component, and in the elder was likely to grow. However, the growth of RN showed no association with the radiological features and pathology of DT.
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Affiliation(s)
- Tianyi Xia
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Mengting Cai
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Yuandi Zhuang
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Xiaowei Ji
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Dingpin Huang
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Liaoyi Lin
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Jinjin Liu
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China
| | - Yunjun Yang
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China.
| | - Gangze Fu
- Depatment of Radiology, Wenzhou Medical University, First Affiliated Hospital, NO. 2 Fuxue Rd., Wenzhou, 325000, China.
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Gao C, Yan J, Luo Y, Wu L, Pang P, Xiang P, Xu M. The Growth Trend Predictions in Pulmonary Ground Glass Nodules Based on Radiomic CT Features. Front Oncol 2020; 10:580809. [PMID: 33194710 PMCID: PMC7606974 DOI: 10.3389/fonc.2020.580809] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/10/2020] [Indexed: 12/27/2022] Open
Abstract
Background: The management of ground glass nodules (GGNs) remains a distinctive challenge. This study is aimed at comparing the predictive growth trends of radiomic features against current clinical features for the evaluation of GGNs. Methods: A total of 110 GGNs in 85 patients were included in this retrospective study, in which follow up occurred over a span ≥2 years. A total of 396 radiomic features were manually segmented by radiologists and quantitatively analyzed using an Analysis Kit software. After feature selection, three models were developed to predict the growth of GGNs. The performance of all three models was evaluated by a receiver operating characteristic (ROC) curve. The best performing model was also assessed by calibration and clinical utility. Results: After using a stepwise multivariate logistic regression analysis and dimensionality reduction, the diameter and five specific radiomic features were included in the clinical model and the radiomic model. The rad-score [odds ratio (OR) = 5.130; P < 0.01] and diameter (OR = 1.087; P < 0.05) were both considered as predictive indicators for the growth of GGNs. Meanwhile, the area under the ROC curve of the combined model reached 0.801. The high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test and the decision curve analysis was utilized for the nomogram. Conclusions: A combined model using the current clinical features alongside the radiomic features can serve as a powerful tool to assist clinicians in guiding the management of GGNs.
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Affiliation(s)
- Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jing Yan
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Yifan Luo
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- GE Healthcare Life Sciences, Hangzhou, China
| | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
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