1
|
Zarei F, Jannatdoust P, Malekpour S, Razaghi M, Chatterjee S, Varadhan Chatterjee V, Abbasi A, Haghighi RR. Quantitative analysis of lung lesions using unenhanced chest computed tomography images. THE CLINICAL RESPIRATORY JOURNAL 2024; 18:e13759. [PMID: 38714529 PMCID: PMC11076304 DOI: 10.1111/crj.13759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/01/2023] [Accepted: 04/12/2024] [Indexed: 05/10/2024]
Abstract
INTRODUCTION Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. METHODS In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. RESULTS HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign. CONCLUSION Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.
Collapse
Affiliation(s)
- Fariba Zarei
- Medical Imaging Research CenterShiraz University of Medical SciencesShirazIran
- Department of RadiologyShiraz University of Medical SciencesShirazIran
| | | | - Siamak Malekpour
- Department of RadiologyShiraz University of Medical SciencesShirazIran
| | - Mahshad Razaghi
- Student Research CommitteeShiraz University of Medical SciencesShirazIran
| | - Sabyasachi Chatterjee
- Ongil (or Retired Scientist From Indian Institue of Astrophysics, Bengluru)SalemIndia
| | | | - Amirbahador Abbasi
- Student Research CommitteeShiraz University of Medical SciencesShirazIran
| | | |
Collapse
|
2
|
Ye Y, Sun Y, Hu J, Ren Z, Chen X, Chen C. A clinical-radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype. Clin Radiol 2024; 79:e432-e439. [PMID: 38097460 DOI: 10.1016/j.crad.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 02/15/2024]
Abstract
AIM To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type. MATERIALS AND METHODS The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics. RESULTS Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes. CONCLUSIONS Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.
Collapse
Affiliation(s)
- Y Ye
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Y Sun
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - J Hu
- General Surgery, Cancer Center, Department of Gastrointestinal and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Z Ren
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - X Chen
- Cancer Center, Department of Medical Oncology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - C Chen
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
| |
Collapse
|
3
|
Xu Y, Li Y, Yin H, Tang W, Fan G. Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules. Front Oncol 2021; 11:725599. [PMID: 34568054 PMCID: PMC8461974 DOI: 10.3389/fonc.2021.725599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/19/2021] [Indexed: 01/31/2023] Open
Abstract
Introduction Tumors are continuously evolving biological systems which can be monitored by medical imaging. Previous studies only focus on single timepoint images, whether the performance could be further improved by using serial noncontrast CT imaging obtained during nodule follow-up management remains unclear. In this study, we evaluated DL model for predicting tumor invasiveness of GGNs through analyzing time series CT images. Methods A total of 168 pathologically confirmed GGN cases (48 noninvasive lesions and 120 invasive lesions) were retrospectively collected and randomly assigned to the development dataset (n = 123) and independent testing dataset (n = 45). All patients underwent consecutive noncontrast CT examinations, and the baseline CT and 3-month follow-up CT images were collected. The gross region of interest (ROI) patches containing only tumor region and the full ROI patches including both tumor and peritumor regions were cropped from CT images. A baseline model was built on the image features and demographic features. Four DL models were proposed: two single-DL model using gross ROI (model 1) or full ROI patches (model 3) from baseline CT images, and two serial-DL models using gross ROI (model 2) or full ROI patches (model 4) from consecutive CT images (baseline scan and 3-month follow-up scan). In addition, a combined model integrating serial full ROI patches and clinical information was also constructed. The performance of these predictive models was assessed with respect to discrimination and clinical usefulness. Results The area under the curve (AUC) of the baseline model, models 1, 2, 3, and 4 were 0.562 [(95% confidence interval (C)], 0.406~0.710), 0.693 (95% CI, 0.538-0.822), 0.787 (95% CI, 0.639-0.895), 0.727 (95% CI, 0.573-0.849), and 0.811 (95% CI, 0.667-0.912) in the independent testing dataset, respectively. The results indicated that the peritumor region had potential to contribute to tumor invasiveness prediction, and the model performance was further improved by integrating imaging scans at multiple timepoints. Furthermore, the combined model showed best discrimination ability, with AUC, sensitivity, specificity, and accuracy achieving 0.831 (95% CI, 0.690-0.926), 86.7%, 73.3%, and 82.2%, respectively. Conclusion The DL model integrating full ROIs from serial CT images shows improved predictive performance in differentiating noninvasive from invasive GGNs than the model using only baseline CT images, which could benefit the clinical management of GGNs.
Collapse
Affiliation(s)
- Yao Xu
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Li
- Department of Radiology, Dushuhu Public Hospital Affiliated of Soochow University, Suzhou, China
| | - Hongkun Yin
- Department of Advanced Research, Infervision Medical Technology Co. Ltd, Beijing, China
| | - Wen Tang
- Department of Advanced Research, Infervision Medical Technology Co. Ltd, Beijing, China
| | - Guohua Fan
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| |
Collapse
|
4
|
Kuroda H, Nakada T, Oya Y, Takahashi Y, Matsusita H, Sakakura N. Clinical adjustability of radiological tools in patients with surgically resected cT1N0-staged non-small-cell lung cancer from the long-term survival evaluation. J Thorac Dis 2020; 12:6655-6662. [PMID: 33282366 PMCID: PMC7711385 DOI: 10.21037/jtd-20-1610] [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] [Indexed: 12/25/2022]
Abstract
Background Various radiological tools have been introduced to determine the malignancy or prognosis of lung carcinomas. We retrospectively summarized the clinical outcomes to evaluate whether radiological tools such as consolidation-to-tumor ratio (CTR), tumor disappearance ratio (TDR), and mediastinal diameter (MD) are suitable for surgically resected non-small-cell lung cancer (NSCLC). Methods This retrospective study included 260 patients (128 men and 132 women; median age, 64 years) with cT1N0-staged NSCLC who underwent thoracotomy. Disease-free survival (DFS) and overall survival (OS) outcomes were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results When the adjusted hazard ratios (HRs) with reference to cT1a/1 mi were calculated, significant differences were observed in cT1b and cT1c for DFS (P=0.04 and P<0.01, respectively) and in cT1c for OS (P=0.01). For HRs with reference to CTR (≤0.5), a significant difference was only observed in CTR (>0.5) for DFS (P=0.01). For HRs with reference to TDR (≤25%), significant differences were observed in TDR (>75%) for DFS (P=0.02) and OS (P=0.02). For HRs with reference to MD (≤5 mm), significant differences were observed in 6–20 mm (P=0.04) and >20 mm (P=0.02) for DFS and in >20 mm (P=0.02) for OS. Conclusions All radiological tools revealed significant correlations with prognosis in the patients with cT1N0-staged NSCLCs. We recommend the use of MD in a clinical context. However, further investigation of this issue is needed.
Collapse
Affiliation(s)
- Hiroaki Kuroda
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Takeo Nakada
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yuko Oya
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan.,Department of Thoracic Oncology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yusuke Takahashi
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Hirokazu Matsusita
- Division of Translational Oncoimmunology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Noriaki Sakakura
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| |
Collapse
|
5
|
Tu W, Li Z, Wang Y, Li Q, Xia Y, Guan Y, Xiao Y, Fan L, Liu S. The "solid" component within subsolid nodules: imaging definition, display, and correlation with invasiveness of lung adenocarcinoma, a comparison of CT histograms and subjective evaluation. Eur Radiol 2018; 29:1703-1713. [PMID: 30324380 DOI: 10.1007/s00330-018-5778-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/21/2018] [Accepted: 09/19/2018] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To validate three proposed definitions of the "solid" component of subsolid nodules, as compared to CT histograms and the use of different window settings, for discriminating the invasiveness of adenocarcinomas in a manner that facilitates routine clinical assessment. METHODS We retrospectively analyzed 328 pathologically confirmed lung adenocarcinomas, manifesting as subsolid nodules. Three-dimensional CT histograms were generated by setting 11 CT attenuation intervals from - 400 to 50 HU, at 50 HU intervals, and the voxel percentage within each CT attenuation interval was generated automatically. Three definitions of the "solid" component were proposed, and 10 medium window settings were set to evaluate the "solid" component. The diagnostic performance of the three definitions for identifying invasive adenocarcinoma was compared with that of CT histogram analysis and subjective evaluation with medium window settings. RESULTS A parallel diagnosis using five intervals with the largest AUC (AUC ≥ 0.797) demonstrated good differential diagnostic performance, with 78% sensitivity and 73.7% specificity. Definition 2 (visibility in the mediastinum window) yielded higher accuracy (75.6%) than the other two definitions (p < 0.01). A medium window setting of - 50 WL/2 WW gave a larger AUC than the other nine medium window settings as well as definition 2, with 82.5% specificity and 88.5% PPV, which was higher than those of parallel diagnosis with CT histogram and definition 2. CONCLUSION Using - 50 WL/2 WW is the optimum approach for evaluating the "solid" component and discriminating invasiveness, superior to using 3D CT histograms and definition 2, and convenient in routine clinical assessment. KEY POINTS • - 50 WL/2 WW gave a larger AUC than definition 2. • The specificity of - 50 WL/2 WW was higher than CT histograms. • - 50 WL/2 WW offers the best evaluation of the solid component.
Collapse
Affiliation(s)
- WenTing Tu
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - ZhaoBin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Yun Wang
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Qiong Li
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Yi Xia
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Yu Guan
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Yi Xiao
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
| | - ShiYuan Liu
- Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
| |
Collapse
|
6
|
Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons. Clin Radiol 2018; 73:504.e9-504.e16. [DOI: 10.1016/j.crad.2017.12.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 12/06/2017] [Indexed: 01/15/2023]
|
7
|
Peng M, Yu G, Zhang C, Li C, Wang J. Three-dimensional substructure measurements for the differential diagnosis of ground glass nodules. BMC Pulm Med 2017. [PMID: 28629453 PMCID: PMC5477248 DOI: 10.1186/s12890-017-0438-y] [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] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We analyzed the differences between maximum and peak computed tomography (CT) numbers (M-P), respectively representing the densities of the solid center and the main periphery of ground-glass nodules (GGNs), and the average change in M-P velocity (V(M-P)) during follow-up to differentiate between pre-invasive (PIA) and invasive adenocarcinoma (IAC). METHODS Data of 102 patients were retrospectively collected and analyzed in our study including 43 PIAs and 59 IACs. Diameters, total volumes, and the maximum and peak CT numbers in CT number histograms were measured and followed for at least 3 months. This study was registered retrospectively. RESULTS The M-P values for IACs were higher than those for PIAs (p = 0.001), with an area under the curve (AUC) of 0.810 and a threshold of 489.5 Hounsfield units (HU) in ROC analysis. The V(M-P) values for IACs were smaller than those for PIAs (p = 0.04), with an AUC of 0.805 and a threshold of 11.01 HU/day. CONCLUSIONS M-P and V(M-P) values may help distinguish IACs from PIAs by representing the changes in the sub-structural densities of GGNs during follow-up.
Collapse
Affiliation(s)
- Mingzheng Peng
- Shanghai Key Laboratory of Orthopaedic Implant, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School Of Medicine, Room 703, No. 3 Building, 693 Zhizaoju Road, Shanghai, 200011, China
| | - Gang Yu
- Department of Anesthesiology, Binzhou Central Hospital, Binzhou Medical College, Binzhou, China
| | - Chengzhong Zhang
- Department of Radiology, Shanghai First People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cuidi Li
- School of Biomedical Engineering, MED-X Research Institute of Shanghai Jiao Tong University, Shanghai, China
| | - Jinwu Wang
- Shanghai Key Laboratory of Orthopaedic Implant, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School Of Medicine, Room 703, No. 3 Building, 693 Zhizaoju Road, Shanghai, 200011, China. .,School of Biomedical Engineering, MED-X Research Institute of Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|