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Zheng Y, Li H, Zhang K, Luo Q, Ding C, Han X, Shi H. Dual-energy CT-based radiomics for predicting pathological grading of invasive lung adenocarcinoma. Clin Radiol 2024:S0009-9260(24)00347-7. [PMID: 39098469 DOI: 10.1016/j.crad.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 06/04/2024] [Accepted: 07/09/2024] [Indexed: 08/06/2024]
Abstract
AIMS The purpose of the study was to build a radiomics model using Dual-energy CT (DECT) to predict pathological grading of invasive lung adenocarcinoma. MATERIALS AND METHODS The retrospective study enrolled 107 patients (80 low-grade and 27 high-grade) with invasive lung adenocarcinoma before surgery. Clinical features, radiographic characteristics, and quantitative parameters were measured. Virtual monoenergetic images at 50kev and 150kev were reconstructed for extracting DECT radiomics features. To select features for constructing models, Pearson's correlation analysis, intraclass correlation coefficients, and least absolute shrinkage and selection operator penalized logistic regression were performed. Four models, including the DECT radiomics model, the clinical-DECT model, the conventional CT radiomics model, and the mixed model, were established. Area under the curve (AUC) and decision curve analysis were used to measure the performance and the clinical value of the models. RESULTS The radiomics model based on DECT exhibited outstanding performance in predicting tumor differentiation, with an AUC of 0.997 and 0.743 in the training and testing sets, respectively. Incorporating tumor density, lobulation, and effective atomic number at AP, the clinical-DECT model showed a comparable performance with an AUC of 0.836 in both the training and testing sets. In comparison to the conventional CT radiomics model (AUC of 0.998 in the training and 0.529 in the testing set) and the mixed model (AUC of 0.988 in the training and 0.707 in the testing set), the DECT radiomics model demonstrated a greater AUC value and provided patients with a more significant net benefit in the testing set. CONCLUSIONS In contrast to the conventional CT radiomics model, the DECT radiomics model produced greater predictive performance in pathological grading of invasive lung adenocarcinoma.
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Affiliation(s)
- Y Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - H Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - K Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - Q Luo
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - C Ding
- Bayer Healthcare, No. 399, West Haiyang Road, Shanghai 200126, China.
| | - X Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - H Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
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Guedes Pinto E, Penha D, Ravara S, Monaghan C, Hochhegger B, Marchiori E, Taborda-Barata L, Irion K. Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis. Insights Imaging 2023; 14:152. [PMID: 37741928 PMCID: PMC10517915 DOI: 10.1186/s13244-023-01480-z] [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: 04/18/2023] [Accepted: 07/08/2023] [Indexed: 09/25/2023] Open
Abstract
Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.
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Affiliation(s)
- Erique Guedes Pinto
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal.
| | - Diana Penha
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | - Sofia Ravara
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Colin Monaghan
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | | | - Edson Marchiori
- Faculdade de Medicina, Universidade Federal Do Rio de Janeiro, Bloco K - Av. Carlos Chagas Filho, 373 - 2º Andar, Sala 49 - Cidade Universitária da Universidade Federal Do Rio de Janeiro, Rio de Janeiro - RJ, 21044-020, Brasil
- Faculdade de Medicina, Universidade Federal Fluminense, Av. Marquês Do Paraná, 303 - Centro, Niterói - RJ, 24220-000, Brasil
| | - Luís Taborda-Barata
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Klaus Irion
- Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Oxford Rd, Manchester, M13 9WL, UK
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Zhou X, Zhang H, Jin X, Zhang X, Lu X, Han Q, Xiong X, Liu T, Feng Y, Tu W, Zhou T, Ge Y, Dong P, Liu S, Fan L. Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study. Diagn Interv Radiol 2023; 29:691-703. [PMID: 37559745 PMCID: PMC10679552 DOI: 10.4274/dir.2023.232233] [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: 03/31/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol. METHODS A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared. RESULTS With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs. CONCLUSION Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.
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Affiliation(s)
- Xiuxiu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hanxiao Zhang
- Department of Radiology, Xuzhou Medical University, School of Medical Imaging, Xuzhou, China
| | - Xiaoxing Jin
- Department of Radiology Medicine, The Second People’s Hospital of Linhai, Linhai, China
| | - Xiaohui Zhang
- Department of Clinical Science, Philips Healthcare, Shanghai, China
| | - Xiaomei Lu
- CT Clinical Science, Philips Healthcare, Shanghai, China
| | - Qun Han
- Department of Clinical Science, Philips Healthcare, Shanghai, China
| | - Xiaoge Xiong
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Tian Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yan Feng
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Weifang Medical University, School of Medical Imaging, Shanghai, China
| | - Yanming Ge
- Department of Radiology, Weifang Medical University, School of Medical Imaging, Shanghai, China
| | - Peng Dong
- Department of Radiology, Weifang Medical University, School of Medical Imaging, Shanghai, China
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
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Zheng Y, Han X, Jia X, Ding C, Zhang K, Li H, Cao X, Zhang X, Zhang X, Shi H. Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules. Front Oncol 2023; 13:1208758. [PMID: 37637058 PMCID: PMC10449576 DOI: 10.3389/fonc.2023.1208758] [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: 04/19/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Objectives To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery. Methods The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA). Results The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model. Conclusion DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.
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Affiliation(s)
- Yuting Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaoyu Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chengyu Ding
- ShuKun (BeiJing) Technology Co., Ltd., Beijing, China
| | - Kailu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Hanting Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xuexiang Cao
- Clinical Solution, Philips Healthcare, Shanghai, China
| | - Xiaohui Zhang
- Clinical Solution, Philips Healthcare, Shanghai, China
| | - Xin Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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Zhong H, Li A, Chen Y, Huang Q, Chen X, Kang J, You Y. Comparative analysis of automatic segmentation of esophageal cancer using 3D Res-UNet on conventional and 40-keV virtual mono-energetic CT Images: a retrospective study. PeerJ 2023; 11:e15707. [PMID: 37483982 PMCID: PMC10358343 DOI: 10.7717/peerj.15707] [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: 03/07/2023] [Accepted: 06/15/2023] [Indexed: 07/25/2023] Open
Abstract
Objectives To assess the performance of 3D Res-UNet for fully automated segmentation of esophageal cancer (EC) and compare the segmentation accuracy between conventional images (CI) and 40-keV virtual mono-energetic images (VMI40 kev). Methods Patients underwent spectral CT scanning and diagnosed of EC by operation or gastroscope biopsy in our hospital from 2019 to 2020 were analyzed retrospectively. All artery spectral base images were transferred to the dedicated workstation to generate VMI40 kev and CI. The segmentation model of EC was constructed by 3D Res-UNet neural network in VMI40 kev and CI, respectively. After optimization training, the Dice similarity coefficient (DSC), overlap (IOU), average symmetrical surface distance (ASSD) and 95% Hausdorff distance (HD_95) of EC at pixel level were tested and calculated in the test set. The paired rank sum test was used to compare the results of VMI40 kev and CI. Results A total of 160 patients were included in the analysis and randomly divided into the training dataset (104 patients), validation dataset (26 patients) and test dataset (30 patients). VMI40 kevas input data in the training dataset resulted in higher model performance in the test dataset in comparison with using CI as input data (DSC:0.875 vs 0.859, IOU: 0.777 vs 0.755, ASSD:0.911 vs 0.981, HD_95: 4.41 vs 6.23, all p-value <0.05). Conclusion Fully automated segmentation of EC with 3D Res-UNet has high accuracy and clinically feasibility for both CI and VMI40 kev. Compared with CI, VMI40 kev indicated slightly higher accuracy in this test dataset.
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Affiliation(s)
- Hua Zhong
- Department of Radiology, Zhong Shan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Anqi Li
- Department of Radiology, Zhong Shan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Yingdong Chen
- Department of Radiology, Zhong Shan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Qianwen Huang
- Department of Radiology, Zhong Shan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Jianghe Kang
- Department of Radiology, Zhong Shan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Youkuang You
- Department of Radiology, Xiamen Xianyue Hospital, Xiamen, Fujian, China
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Mu R, Meng Z, Zhang X, Guo Z, Zheng W, Zhuang Z, Zhu X. Parameters of Dual-Layer Spectral Detector CT could be Used to Differentiate Non-Small Cell Lung Cancer from Small Cell Lung Cancer. Curr Med Imaging 2022; 18:1070-1078. [PMID: 35260059 DOI: 10.2174/1573405618666220308105359] [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: 08/31/2021] [Revised: 12/20/2021] [Accepted: 12/31/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Differentiating non-small cell lung cancer (NSCLC) from small cell lung cancer (SCLC) remains a substantial challenge. This study aimed at evaluating the performance of dual-layer spectral detector CT (DLCT) in differentiating NSCLC from SCLC. METHODS Spectral images of 247 cancer patients confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP), which includes 197 cases of NSCLC and 50 cases of SCLC. Effective atomic umber (Z-eff), Spectral CT-Mono Energetic (MonoE [40keV~90keV]), iodine density (ID) and thoracic aorta iodine density (IDaorta) in contrast-enhanced images were calculated and compared between the SCLC and NSCLC subgroups of tumors. Slope of spectral curve (λ, interval of 10 keV) and normalized iodine density (NID) was calculated between the SCLC and NSCLC too. Through statistical analysis, the diagnostic efficiency of each spectral parameter was calculated, and the difference of their efficiency was analyzed. RESULTS Both in NSCLS and SCLC, all parameters in VP were significantly higher than those in AP (p<0.001), except for λ90. There were significant differences in all spectral parameters between NSCLS and SCLC, both in AP and VP (p < 0.001). Except VP-λ90, there was no significant difference in ROC curves of all spectral parameters. VP-NID has the best diagnostic performance with AUC value of 0.917 (95%[CI]: 0.870~0.965), sensitivity and specificity of 92.9%, 80%, and diagnostic threshold of 0.217. CONCLUSION All parameters of DLCT have high diagnostic efficiency in differentiating NSCLC from SCLC except for VP-λ90, and VP-NID has the highest diagnostic efficiency.
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Affiliation(s)
- Ronghua Mu
- Graduate School of Guilin Medical University,541004,China
| | - Zhuoni Meng
- Graduate School of Guilin Medical University,541004,China
| | - Xiaodi Zhang
- Philips (China) Investment Co., Ltd. Chengdu Branch,610000,China
| | - Zixuan Guo
- Graduate School of Guilin Medical University,541004,China
| | - Wei Zheng
- Graduate School of Guilin Medical University,541004,China
| | - Zeyu Zhuang
- Graduate School of Guilin Medical University,541004,China
| | - Xiqi Zhu
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region,541004 , China
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Wen Q, Yue Y, Shang J, Lu X, Gao L, Hou Y. The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification. Quant Imaging Med Surg 2021; 11:521-532. [PMID: 33532253 DOI: 10.21037/qims-20-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Differentiating between malignant solitary pulmonary nodules (SPNs) and other lung diseases remains a substantial challenge. The latest generation of dual-energy computed tomography (CT), which realizes dual-energy technology at the detector level, has clinical potential for distinguishing lung cancer from other benign SPNs. This study aimed to evaluate the performance of dual-layer spectral detector CT (SDCT) for the differentiation of SPNs. Methods Spectral images of 135 SPNs confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP). Patients were classified into two groups [the malignant group (n=93) and the benign group (n=42)], with the malignant group further divided into small cell lung cancer (SCLC, n=30) and non-small cell lung cancer (NSCLC, n=63) subtypes. The slope of the spectral Hounsfield Unit (HU) curve (λHU), normalized iodine concentration (NIC), CT values of 40 keV monochromatic images (CT40keV), and normalized arterial enhancement fraction (NAEF) in contrast-enhanced images were calculated and compared between the benign and malignant groups, as well as between the SCLC and NSCLC subgroups. ROC curve analysis was performed to assess the diagnostic performance of the above parameters. Seventy cases were randomly selected and independently measured by two radiologists, and intraclass correlation coefficient (ICC) and Bland-Altman analyses were performed to calculate the reliability of the measurements. Results Except for NAEF (P=0.23), the values of the parameters were higher in the malignant group than in the benign group (all P<0.05). NIC, λHU, and CT40keV performed better in the VP (NICVP, λVPHU, and CTVP40keV) (P<0.001), with an area under the ROC curve (AUC) of 0.93, 0.89, and 0.89 respectively. With respective cutoffs of 0.31, 1.83, and 141.00 HU, the accuracy of NICVP, λVPHU, and CTVP40keV was 91.11%, 85.19%, and 88.15%, respectively. In the subgroup differentiating NSCLC and SCLC, the diagnostic performances of NICAP (AUC =0.89) were greater than other parameters. NICAP had an accuracy of 86.02% when the cutoff was 0.14. ICC and Bland-Altman analyses indicated that the measurement of SDCT has great reproducibility. Conclusions Quantitative measures from SDCT can help to differentiate benign from malignant SPNs and may help with the further subclassification of malignant cancer into SCLC and NSCLC.
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Affiliation(s)
- Qingyun Wen
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jin Shang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaomei Lu
- CT Clinical Science, Philips Healthcare, Shenyang, China
| | - Lu Gao
- Department of Radiology, Liaoning Cancer Hospital, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Du Y, Shi GF, Wang YN, Wang Q, Feng H. Repeatability of small lung nodule measurement in low-dose lung screening: a phantom study. BMC Med Imaging 2020; 20:112. [PMID: 33008318 PMCID: PMC7532632 DOI: 10.1186/s12880-020-00510-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/14/2020] [Indexed: 12/05/2022] Open
Abstract
Background Lung cancer screening revealed that people with small pulmonary nodules are mostly asymptomatic and that some of these people are at risk of developing lung cancer, so we intended to explore the repeatability of small lung nodule measurement in low-dose lung screening. Methods We scanned eight ground-glass nodules (GGNs) and solid nodules, with diameters of 3, 5, 8, and 10 mm. They were divided according to the different combination schemes of tube voltage (KV) and tube current (mA) as 70, 80, 100, and 120 KV, and currents of nine tubes were divided as 20, 30, 40, 50, 60, 70, 80, 90, and 100 mAs. Results Compared with the conventional dose group (120 kVp, 100 mAs), the nodule diameter and solid nodule volume measured by all scanning combinations were more consistent (P > 0.05), the volumes of 10 mm GGNs combinations were consistent (P > 0.05), the volumes of 8 mm GGNs were consistent (P > 0.05), the volumes of 5 mm GGNs combinations were consistent (P > 0.05), and the volumes of 3 mm were consistent (P > 0.05). Conclusion In lung cancer screening, CT parameters should be as follows: tube voltage is more than 80 kVp, and tube current is 80 mAs in order to meet the requirements for the accurate measurement of the diameter and volume of pulmonary nodules.
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Affiliation(s)
- Yu Du
- Department of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, 050011, China
| | - Gao-Feng Shi
- Department of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, 050011, China.
| | - Ya-Ning Wang
- Department of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, 050011, China
| | - Qi Wang
- Department of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, 050011, China
| | - Hui Feng
- Department of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, 050011, China
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Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study. J Comput Assist Tomogr 2020; 44:847-851. [PMID: 32976271 DOI: 10.1097/rct.0000000000001102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of the study was to assess accuracy of pulmonary nodule volumetry using noise-optimized virtual monoenergetic image (VMI+) and nonlinear blending image (NBI) algorithms in dual-energy computed tomography (DECT). METHODS An anthropomorphic chest phantom with 10 simulated nodules (5 solid nodules and 5 ground-glass opacities) was scanned using DECT80/Sn140kV, DECT100/Sn140kV, and single-energy CT (SECT120kV/200mAs), respectively. The dual-energy images were reconstructed using VMI+ (70 keV) and NBI algorithms. The contrast-to-noise ratio and absolute percentage error (APE) of nodule volume were measured to assess image quality and accuracy of nodule volumetry. The radiation dose was also estimated. RESULTS The contrast-to-noise ratio of SECT120kV/200mAs was significantly higher than that of NBI80/Sn140kV and VMI+80/Sn140kV (both corrected P < 0.05), whereas there were no significant differences between NBI100/sn140kV and SECT120kV/200mAs and between VMI+100/sn140kV and SECT120kV/200mAs (both corrected P > 0.05). The APE of SECT120kV/200mAs was significantly lower than that of NBI80/Sn140kV and VMI+80/Sn140kV in both types of nodules (all corrected P < 0.05), whereas there were no significant differences between VMI+100/sn140kV and SECT120kV/200mAs in solid nodules and between NBI100/Sn140kV and SECT120kV/200mAs in ground-glass opacities (both corrected P > 0.05). The radiation dose of DECT100/Sn140kV and DECT80/Sn140kV were significantly lower than that of SECT120kV/200mAs (both corrected P < 0.05). CONCLUSIONS The DECT100/sn140kV can ensure image quality and nodule volumetry accuracy with lower radiation dose compared with SECT120kV/200mAs. Specifically, the VMI+ algorithm could be used in solid nodules and NBI algorithm in ground-glass opacities.
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