1
|
Hui YM, Guo Y, Li B, Meng YQ, Feng HM, Su ZP, Lin MZ, Chen YZ, Zheng ZZ, Li HT. Comparative analysis of three-dimensional and two-dimensional models for predicting the malignancy probability of subsolid nodules. Clin Radiol 2024:S0009-9260(24)00341-6. [PMID: 39068114 DOI: 10.1016/j.crad.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024]
Abstract
AIM To construct three-dimensional (3D) and two-dimensional (2D) models to predict the malignancy probability of subsolid nodules (SSNs) and compare their effectiveness. MATERIALS AND METHODS A total of 371 SSNs from 332 patients, collected between January 2020 and January 2024, were included in the study. The SSNs were divided into a training set for constructing the models and a test set for validating the models. Models were developed using binary logistic backward regression, based on factors that showed significant differences in univariate analyses. The performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUCs of different models were compared using the DeLong test. RESULTS The AUCs for the two 3D models, one 2D model, and the Brock model were 0.785 (0.733-0.836), 0.776 (0.723-0.829), 0.764 (0.710-0.818), and 0.738 (0.679-0.798) in the training set. In the test set, these AUCs were 0.817 (0.706-0.928), 0.796 (0.679-0.913), 0.771 (0.647-0.895), and 0.790 (0.678-0.903). The two 3D models demonstrated statistically significant differences from the Brock model in the training set (P=0.024 and P=0.046). None of the four models showed significant differences in the test set (all P>0.05). CONCLUSION The 3D models outperform both the 2D model and the Brock model in predicting the malignancy probability of SSNs, and the 3D model incorporating volume, mean CT attenuation value, and lobulation as factors performed the best.
Collapse
Affiliation(s)
- Y-M Hui
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y Guo
- Department of Radiology, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - B Li
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y-Q Meng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - H-M Feng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Z-P Su
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - M-Z Lin
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y-Z Chen
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Z-Z Zheng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - H-T Li
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| |
Collapse
|
2
|
Azour L, Oh AS, Prosper AE, Toussie D, Villasana-Gomez G, Pourzand L. Subsolid Nodules: Significance and Current Understanding. Clin Chest Med 2024; 45:263-277. [PMID: 38816087 DOI: 10.1016/j.ccm.2024.02.003] [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] [Indexed: 06/01/2024]
Abstract
Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.
Collapse
Affiliation(s)
- Lea Azour
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA.
| | - Andrea S Oh
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Ashley E Prosper
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Danielle Toussie
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Geraldine Villasana-Gomez
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Lila Pourzand
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| |
Collapse
|
3
|
Zhu FY, Sun YF, Yin XP, Zhang Y, Xing LH, Ma ZP, Xue LY, Wang JN. Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes. Discov Oncol 2023; 14:224. [PMID: 38055122 DOI: 10.1007/s12672-023-00837-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
OBJECTIVE To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.
Collapse
Affiliation(s)
- Feng-Ying Zhu
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Electronic Information Engineering, Hebei University, Baoding, 071002, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Li-Hong Xing
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No.180 of Wusi Road, Lianchi District, Baoding, 071002, China.
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China.
| |
Collapse
|
4
|
Li S, Chen M, Wang Y, Li X, Gao G, Luo X, Tang L, Liu X, Wu N. An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans. Clin Lung Cancer 2023; 24:e301-e310. [PMID: 37596166 DOI: 10.1016/j.cllc.2023.08.001] [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: 06/26/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023]
Abstract
INTRODUCTION It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time. MATERIALS AND METHODS We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters. RESULTS The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals. CONCLUSION This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.
Collapse
Affiliation(s)
- Shaolei Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Mailin Chen
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yaqi Wang
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiang Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | | | | | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | | | - Nan Wu
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
Peng Z, Lin Z, He A, Yi L, Jin M, Chen Z, Tao Y, Yang Y, Cui C, Liu Y, Zuo M. Development and Validation of a Comprehensive Model for Predicting Distant Metastasis of Solid Lung Adenocarcinoma: 3D Radiomics, 2D Radiomics and Clinical Features. Cancer Manag Res 2022; 14:3437-3448. [PMID: 36536823 PMCID: PMC9758990 DOI: 10.2147/cmar.s393058] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/19/2022] [Indexed: 12/01/2023] Open
Abstract
OBJECTIVE To develop and validate models for predicting distant metastases in patients with solid lung adenocarcinomas using 3D radiomic features, 2D radiomic features, clinical features, and their combinations. METHODS This retrospective study included 253 eligible patients with solid adenocarcinoma of the lung diagnosed at our hospital between August 2018 and August 2021. 3D and 2D regions of interest were segmented from computed tomography-enhanced thin-slice images of the venous phase, and 851 radiomic features were extracted in each region. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select radiomic features and calculate radiomic scores, and logistic regression was used to develop the model. Development of a 3D radiomics model (model 1), a 2D radiomics model (model 2), a combined 3D radiomics and 2D radiomics model (model 3), a clinical model (model 4), and a comprehensive model (model 5) for the prediction of distant metastases in patients with solid lung adenocarcinomas. Nomograms were drawn to illustrate model 5, and receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used for model evaluation. RESULTS The AUC (area under the curve) of model 1, model 2, model 3, model 4, and model 5 in the test set was 0.711, 0.769, 0.775, 0.829, and 0.892, respectively. The Delong test showed that AUC values were statistically different between model 5 and model 1 (p=0.001), and there was no statistical difference in AUC between the other models. Based on a comprehensive review of DCA, ROC curve, and Akaike information criterion (AIC), Model 5 is demonstrated to have better clinical utility, goodness of fit, and parsimony. CONCLUSION A comprehensive model based on 3D radiomic features, 2D radiomic features, and clinical features has the potential to predict distant metastasis in patients with solid lung adenocarcinomas.
Collapse
Affiliation(s)
- Zhiwei Peng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Ze Lin
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Anjing He
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Li Yi
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Mengni Jin
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Zhiyong Chen
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Yahong Tao
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Yuhua Yang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Can Cui
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Ying Liu
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Minjing Zuo
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| |
Collapse
|
7
|
Azour L, Moore WH, O'Donnell T, Truong MT, Babb J, Niu B, Wimmer A, Kiumehr S, Ko JP. Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features. Acad Radiol 2022; 29 Suppl 2:S98-S107. [PMID: 33610452 DOI: 10.1016/j.acra.2021.01.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/02/2021] [Accepted: 01/13/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVE To evaluate the inter-observer consistency for subsolid pulmonary nodule radiomic features. MATERIALS AND METHODS Subsolid nodules were selected by reviewing radiology reports of CT examinations performed December 1, 2015 to April 1, 2016. Patients with CTs at two time points were included in this study. There were 55 patients with subsolid nodules, of whom 14 had two nodules. Of 69 subsolid nodules, 66 were persistent at the second time point, yielding 135 lesions for segmentation. Two thoracic radiologists and an imaging fellow segmented the lesions using a semi-automated volumetry algorithm (Syngo.via Vb20, Siemens). Coefficient of variation (CV) was used to assess consistency of 91 quantitative measures extracted from the subsolid nodule segmentations, including first and higher order texture features. The accuracy of segmentation was visually graded by an experienced thoracic radiologist. Influencing factors on radiomic feature consistency and segmentation accuracy were assessed using generalized estimating equation analyses and the Exact Mann-Whitney test. RESULTS Mean patient age was 71 (38-93 years), with 39 women and 16 men. Mean nodule volume was 1.39mL, range .03-48.2mL, for 135 nodules. Several radiomic features showed high inter-reader consistency (CV<5%), including entropy, uniformity, sphericity, and spherical disproportion. Descriptors such as surface area and energy had low consistency across inter-reader segmentations (CV>10%). Nodule percent solid component and attenuation influenced inter-reader variability of some radiomic features. The presence of contrast did not significantly affect the consistency of subsolid nodule radiomic features. Near perfect segmentation, within 5% of actual nodule size, was achieved in 68% of segmentations, and very good segmentation, within 25% of actual nodule size, in 94%. Morphologic features including nodule margin and shape (each p <0.01), and presence of air bronchograms (p = 0.004), bubble lucencies (p = 0.02) and broad pleural contact (p < 0.01) significantly affected the probability of near perfect segmentation. Stroke angle (p = 0.001) and length (p < 0.001) also significantly influenced probability of near perfect segmentation. CONCLUSIONS The inter-observer consistency of radiomic features for subsolid pulmonary nodules varies, with high consistency for several features, including sphericity, spherical disproportion, and first and higher order entropy, and normalized non-uniformity. Nodule morphology influences the consistency of subsolid nodule radiomic features, and the accuracy of subsolid nodule segmentation.
Collapse
Affiliation(s)
- Lea Azour
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.).
| | - William H Moore
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.)
| | | | | | - James Babb
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.)
| | - Bowen Niu
- Department of Radiology, Wake Forest Baptist Health (B.N.)
| | | | | | - Jane P Ko
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.)
| |
Collapse
|
8
|
Lu W, Wei J, Xu T, Ding M, Li X, He M, Chen K, Yang X, She H, Huang B. Quantitative CT for detecting COVID‑19 pneumonia in suspected cases. BMC Infect Dis 2021; 21:836. [PMID: 34412614 PMCID: PMC8374412 DOI: 10.1186/s12879-021-06556-z] [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: 11/18/2020] [Accepted: 08/10/2021] [Indexed: 01/08/2023] Open
Abstract
Background Corona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia. Methods A total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia. Results The volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of − 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of − 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at − 400 HU, − 350 HU, and − 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of − 300 HU. Conclusions Quantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06556-z.
Collapse
Affiliation(s)
- Weiping Lu
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China.,Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Jianguo Wei
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China.,Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Tingting Xu
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Miao Ding
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Xiaoyan Li
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Mengxue He
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Kai Chen
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Xiaodan Yang
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Huiyuan She
- Department of Infectious Diseases, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China.
| | - Bingcang Huang
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China.
| |
Collapse
|
9
|
Lee S, Lee DH, Lee JH, Lee S, Han K, Park CH, Kim TH. Semi-Quantitative Analysis for Determining the Optimal Threshold Value on CT to Measure the Solid Portion of Pulmonary Subsolid Nodules. TAEHAN YONGSANG UIHAKHOE CHI 2021; 82:670-681. [PMID: 36238777 PMCID: PMC9432458 DOI: 10.3348/jksr.2020.0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/30/2020] [Accepted: 08/09/2020] [Indexed: 06/16/2023]
Abstract
PURPOSE This study aimed to investigate the optimal threshold value in Hounsfield units (HU) on CT to detect the solid components of pulmonary subsolid nodules using pathologic invasive foci as reference. MATERIALS AND METHODS Thin-section non-enhanced chest CT scans of 25 patients with pathologically confirmed minimally invasive adenocarcinoma were retrospectively reviewed. On CT images, the solid portion was defined as the area with higher attenuation than various HU thresholds ranging from -600 to -100 HU in 50-HU intervals. The solid portion was measured as the largest diameter on axial images and as the maximum diameter on multiplanar reconstruction images. A linear mixed model was used to evaluate bias in each threshold by using the pathological size of invasive foci as reference. RESULTS At a threshold of -400 HU, the biases were lowest between the largest/maximum diameter of the solid portion of subsolid nodule and the size of invasive foci of the pathological specimen, with 0.388 and -0.0176, respectively. They showed insignificant difference (p = 0.2682, p = 0.963, respectively) at a threshold of -400 HU. CONCLUSION For quantitative analysis, -400 HU may be the optimal threshold to define the solid portion of subsolid nodules as a surrogate marker of invasive foci.
Collapse
|
10
|
Kassin MT, Varble N, Blain M, Xu S, Turkbey EB, Harmon S, Yang D, Xu Z, Roth H, Xu D, Flores M, Amalou A, Sun K, Kadri S, Patella F, Cariati M, Scarabelli A, Stellato E, Ierardi AM, Carrafiello G, An P, Turkbey B, Wood BJ. Generalized chest CT and lab curves throughout the course of COVID-19. Sci Rep 2021; 11:6940. [PMID: 33767213 PMCID: PMC7994835 DOI: 10.1038/s41598-021-85694-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 03/03/2021] [Indexed: 12/16/2022] Open
Abstract
A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.
Collapse
Affiliation(s)
- Michael T Kassin
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Radiology and Imaging Sciences, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892-1182, USA
| | - Nicole Varble
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Philips Research North America, Cambridge, MA, 02141, USA
| | - Maxime Blain
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Sheng Xu
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Evrim B Turkbey
- Radiology and Imaging Sciences, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892-1182, USA
| | - Stephanie Harmon
- National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, NCI, Frederick, MD, 21702, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Dong Yang
- NVIDIA Corporation, Bethesda, MD, 20892, USA
| | - Ziyue Xu
- NVIDIA Corporation, Bethesda, MD, 20892, USA
| | - Holger Roth
- NVIDIA Corporation, Bethesda, MD, 20892, USA
| | - Daguang Xu
- NVIDIA Corporation, Bethesda, MD, 20892, USA
| | - Mona Flores
- NVIDIA Corporation, Santa Clara, CA, 95051, USA
| | - Amel Amalou
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kaiyun Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Sameer Kadri
- Critical Care Medicine Department, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Francesca Patella
- Diagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, Milan, Italy
| | - Maurizio Cariati
- Diagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, Milan, Italy
| | - Alice Scarabelli
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Elvira Stellato
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Anna Maria Ierardi
- Department of Radiology and Department of Health Sciences, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico and University of Milano, 20122, Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology and Department of Health Sciences, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico and University of Milano, 20122, Milan, Italy
| | - Peng An
- Department of Radiology, Xiangyang NO. 1 People's Hospital Affiliated to Hubei University of Medicine, Xiangyang, Hubei, 441000, China
| | - Baris Turkbey
- National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Bradford J Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
- Radiology and Imaging Sciences, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892-1182, USA.
- National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, 20892, USA.
| |
Collapse
|
11
|
Ahn Y, Lee SM, Noh HN, Kim W, Choe J, Do KH, Seo JB. Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination. Radiology 2021; 299:202-210. [PMID: 33529136 DOI: 10.1148/radiol.2021202803] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background The solid portion size of lung cancer lesions manifesting as subsolid lesions is key in their management, but the automatic measurement of such lesions by means of a deep learning (DL) algorithm needs evaluation. Purpose To evaluate the performance of a commercially available DL algorithm for automatic measurement of the solid portion of surgically proven lung adenocarcinomas manifesting as subsolid lesions. Materials and Methods Surgically proven lung adenocarcinomas manifesting as subsolid lesions on CT images between January 2018 and December 2018 were retrospectively included. Five radiologists independently measured the maximal axial diameter of the solid portion of lesions. The DL algorithm automatically segmented and measured the maximal axial diameter of the solid portion. Reader measurements, software measurements, and invasive component size at pathologic examination were compared by using intraclass correlation coefficient (ICC) and Bland-Altman plots. Results A total of 448 patients (mean age, 63 years ± 10 [standard deviation]; 264 women) with 448 lesions were evaluated (invasive component size, 3-65 mm). The measurement agreements between each radiologist and the DL algorithm were very good (ICC range, 0.82-0.89). When a radiologist was replaced with the DL algorithm, the ICCs ranged from 0.87 to 0.90, with an ICC of 0.90 among five radiologists. The mean difference between the DL algorithm and each radiologist ranged from -3.7 to 1.5 mm. The widest 95% limit of agreement between the DL algorithm and each radiologist (-15.7 to 8.3 mm) was wider than pairwise comparisons of radiologists (-7.7 to 13.0 mm). The agreement between the DL algorithm and invasive component size at pathologic evaluation was good, with an ICC of 0.67. Measurements by the DL algorithm (mean difference, -6.0 mm) and radiologists (mean difference, -7.5 to -2.3 mm) both underestimated invasive component size. Conclusion Automatic measurements of solid portions of lung cancer manifesting as subsolid lesions by the deep learning algorithm were comparable with manual measurements and showed good agreement with invasive component size at pathologic evaluation. © RSNA, 2021 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Yura Ahn
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| | - Sang Min Lee
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| | - Han Na Noh
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| | - Wooil Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| | - Jooae Choe
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| | - Kyung-Hyun Do
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| | - Joon Beom Seo
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea
| |
Collapse
|
12
|
Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Ma T, Liu J, Shen D. Severity assessment of COVID-19 using CT image features and laboratory indices. Phys Med Biol 2021; 66:035015. [PMID: 33032267 DOI: 10.1088/1361-6560/abbf9e] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 ± 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19.
Collapse
Affiliation(s)
- Zhenyu Tang
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, People's Republic of China. Zhenyu Tang and Wei Zhao contributed equally to this work
| | | | | | | | | | | | | | | |
Collapse
|
13
|
Pan F, Li L, Liu B, Ye T, Li L, Liu D, Ding Z, Chen G, Liang B, Yang L, Zheng C. A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19). Sci Rep 2021; 11:417. [PMID: 33432072 PMCID: PMC7801482 DOI: 10.1038/s41598-020-80261-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/17/2020] [Indexed: 01/08/2023] Open
Abstract
This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.
Collapse
Affiliation(s)
- Feng Pan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Lin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Bo Liu
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, No.523 Louguanshan Road, Changning District, Shanghai, 200000, China
- Hangzhou YITU Healthcare Technology Co., Ltd., Shanghai, 200000, China
| | - Tianhe Ye
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Lingli Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Dehan Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Zezhen Ding
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, No.523 Louguanshan Road, Changning District, Shanghai, 200000, China
- Hangzhou YITU Healthcare Technology Co., Ltd., Shanghai, 200000, China
| | - Guangfeng Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Bo Liang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| |
Collapse
|
14
|
Azour L, Ko JP, Naidich DP, Moore WH. Shades of Gray: Subsolid Nodule Considerations and Management. Chest 2020; 159:2072-2089. [PMID: 33031828 PMCID: PMC7534873 DOI: 10.1016/j.chest.2020.09.252] [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] [Received: 05/29/2020] [Revised: 09/16/2020] [Accepted: 09/27/2020] [Indexed: 12/15/2022] Open
Abstract
Subsolid nodules are common on chest CT imaging and may be either benign or malignant. Their varied features and broad differential diagnoses present management challenges. Although subsolid nodules often represent lung adenocarcinomas, other possibilities are common and influence management. Practice guidelines exist for subsolid nodule management for both incidentally and screening-detected nodules, incorporating patient and nodule characteristics. This review highlights the similarities and differences among these algorithms, with the intent of providing a resource for comparison and aid in choosing management options.
Collapse
Affiliation(s)
- Lea Azour
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY.
| | - Jane P Ko
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY
| | - David P Naidich
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY
| | - William H Moore
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; and NYU Langone Health, New York, NY
| |
Collapse
|
15
|
Ji M, Yuan L, Shen W, Lv J, Li Y, Chen J, Zhu C, Liu B, Liang Z, Lin Q, Xie W, Li M, Chen Z, Lu X, Ding Y, An P, Zhu S, Gao M, Ni H, Hu L, Shi G, Shi L, Dong W. A predictive model for disease progression in non-severely ill patients with coronavirus disease 2019. Eur Respir J 2020; 56:2001234. [PMID: 32430433 PMCID: PMC7241108 DOI: 10.1183/13993003.01234-2020] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/10/2020] [Indexed: 02/07/2023]
Abstract
Over the past 3 months, coronavirus disease 2019 (COVID-19) has emerged across China and developed into a worldwide outbreak [1]. The disease has caused varying degrees of illness. The proportion of patients with COVID-19 with non-severe illness was 84.3% on admission, and severe cases accounted for 15.7% [2]. Most of the non-severe pneumonia patients would gradually alleviate and be cured with treatment, while others would rapidly progress to severe illness, which has a poor prognosis [3, 4]. As recently reported, the cumulative risk of the composite end-point was 3.6% in all COVID-19 patients, and the cumulative risk was 20.6% for severe illness [2]. A predictive model for COVID-19 https://bit.ly/2WHiEkJ
Collapse
Affiliation(s)
- Mengyao Ji
- Dept of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Mengyao Ji, Lei Yuan, Wei Shen and Yong Li contributed equally to this paper
| | - Lei Yuan
- Dept of Information Center, Renmin Hospital of Wuhan University, Wuhan China
- Mengyao Ji, Lei Yuan, Wei Shen and Yong Li contributed equally to this paper
| | - Wei Shen
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
- Mengyao Ji, Lei Yuan, Wei Shen and Yong Li contributed equally to this paper
| | - Junwei Lv
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Yong Li
- Dept of Radiology, Suining Central Hospital, Suining, China
- Mengyao Ji, Lei Yuan, Wei Shen and Yong Li contributed equally to this paper
| | - Jia Chen
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Chaonan Zhu
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Bo Liu
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Zhenzhen Liang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Qiang Lin
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Wenjie Xie
- Dept of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Li
- Dept of Information Center, Renmin Hospital of Wuhan University, Wuhan China
| | - Zhifan Chen
- Dept of Radiology, Affiliated Hospital of Chengdu University, Chengdu, China
| | - Xuefang Lu
- Dept of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - YiJuan Ding
- Dept of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ping An
- Dept of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Sheng Zhu
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Mengting Gao
- Dept of Information Center, Renmin Hospital of Wuhan University, Wuhan China
| | - Hao Ni
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Lanhua Hu
- Dept of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Guanglei Shi
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
| | - Lei Shi
- Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
- Hangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China
- Weiguo Dong and Lei Shi contributed equally to this article as lead authors and supervised the work
| | - Weiguo Dong
- Dept of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Weiguo Dong and Lei Shi contributed equally to this article as lead authors and supervised the work
| |
Collapse
|
16
|
Roberts JM, Greenlaw K, English JC, Mayo JR, Sedlic A. Radiological-pathological correlation of subsolid pulmonary nodules: A single centre retrospective evaluation of the 2011 IASLC adenocarcinoma classification system. Lung Cancer 2020; 147:39-44. [PMID: 32659599 DOI: 10.1016/j.lungcan.2020.06.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 06/01/2020] [Accepted: 06/25/2020] [Indexed: 01/06/2023]
Abstract
INTRODUCTION The 2011 IASLC classification system proposes guidelines for radiologists and pathologists to classify adenocarcinomas spectrum lesions as preinvasive, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). IA portends the worst clinical prognosis, and the imaging distinction between MIA and IA is controversial. MATERIALS AND METHODS Subsolid pulmonary nodules resected by microcoil localization over a three-year period were retrospectively reviewed by three chest radiologists and a pulmonary pathologist. Nodules were classified radiologically based on preoperative computed tomography (CT), with the solid nodule component measured on mediastinal windows applied to high-frequency lung kernel reconstructions, and pathologically according to 2011 IASLC criteria. Radiology interobserver and radiological-pathological variability of nodule classification, and potential reasons for nodule classification discordance were assessed. RESULTS Seventy-one subsolid nodules in 67 patients were included. The average size of invasive disease focus at histopathology was 5 mm (standard deviation 5 mm). Radiology interobserver agreement of nodule classification was good (Cohen's Kappa = 0.604, 95 % CI: 0.447 to 0.761). Agreement between consensus radiological interpretation and pathological category was fair (Cohen's Kappa = 0.236, 95 % CI: 0.054-0.421). Radiological and pathological nodule classification were concordant in 52 % (37 of 71) of nodules. The IASLC proposed CT solid component cut-off of 5 mm to distinguish MIA and IA yielded a sensitivity of 59 % and specificity of 80 %. Common reasons for nodule classification discordance included multiple solid components within a nodule on CT, scar and stromal collapse at pathology, and measurement variability. CONCLUSION Solid component(s) within persistent part-solid pulmonary nodules raise suspicion for invasive adenocarcinoma. Preoperative imaging classification is frequently discordant from final pathology, reflecting interpretive and technical challenges in radiological and pathological analysis.
Collapse
Affiliation(s)
- James M Roberts
- Department of Radiology, Vancouver General Hospital, 910 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada.
| | - Kristin Greenlaw
- Department of Radiology, Vancouver General Hospital, 910 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada
| | - John C English
- Department of Pathology and Laboratory Medicine, Vancouver General Hospital, 910 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada
| | - John R Mayo
- Department of Radiology, Vancouver General Hospital, 910 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada
| | - Anto Sedlic
- Department of Radiology, Vancouver General Hospital, 910 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada
| |
Collapse
|
17
|
Abstract
Coronavirus disease 2019 (COVID-19) patients were classified into four clinical stages (uncomplicated illness, mild, severe and critical pneumonia) depending on disease severity. We aim to investigate the corresponding clinical, radiological and laboratory characteristics between different clinical stages. A retrospective, single-centre study of 101 confirmed patients with COVID-19 at Renmin Hospital of Wuhan University from 2 January to 28 January 2020 was enrolled; follow-up endpoint was on 8 February 2020. Clinical data were collected and compared during the course of illness. The median age of the 101 patients was 51.0 years and 33.6% were medical staff. Fever (68%), cough (50%) and fatigue (23%) are the most common symptoms. About 26% patients underwent the mechanical ventilation and 98% patients were treated with antibiotics. Thirty-seven per cent patients were cured and 11 died. On admission, the number of patients with uncomplicated illness, mild, severe and critical pneumonia were 2 [2%], 86 [85%], 11 [11%] and 2 [2%]. Forty-four of the 86 mild pneumonia progressed to severe illness within 4 days, with nine patients worsened due to critical pneumonia within 4 days. Two of the 11 severe patients improved to mild condition while three others deteriorated. Significant differences were observed among groups of different clinical stages in numbers of influenced pulmonary segments (6 vs. 12 vs. 17, P < 0.001). A significantly upward trend was witnessed in ground-glass opacities overlapped with striped shadows (33% vs. 42% vs. 55% vs. 80%, P < 0.001), while pure ground-glass opacities gradually decreased as disease progressed (45% vs. 35% vs. 24% vs. 13%, P < 0.001) within 12 days. Lymphocytes, prealbumin and albumin showed a downtrend as disease progressed from mild to severe or critical condition, an uptrend was found in white blood cells, C-reactive protein, neutrophils and lactate dehydrogenase. The proportions of serum amyloid A > 300 mg/l in mild, severe and critical conditions were 18%, 46% and 71%, respectively.
Collapse
|
18
|
Cui X, Fan S, Heuvelmans MA, Han D, Zhao Y, Groen HJM, Dorrius MD, Oudkerk M, de Bock GH, Vliegenthart R, Ye Z. Optimization of CT windowing for diagnosing invasiveness of adenocarcinoma presenting as sub-solid nodules. Eur J Radiol 2020; 128:108981. [PMID: 32371183 DOI: 10.1016/j.ejrad.2020.108981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/06/2020] [Accepted: 03/28/2020] [Indexed: 11/27/2022]
Abstract
PURPOSE To evaluate the optimal window setting to diagnose the invasiveness of lung adenocarcinoma in sub-solid nodules (SSNs). METHODS We retrospectively included 437 SSNs and randomly divided them 3:1 into a training group (327) and a testing group (110). The presence of a solid component was regarded as indicator of invasiveness. At fixed window level (WL) of 35 Hounsfield Units (HU), two readers adjusted the window width (WW) in the training group and recorded once a solid component appeared or disappeared on CT images acquired at 120 kVp. The optimal WW cut-off value to differentiate between invasive and pre-invasive lesions, based on the receiver operating characteristic (ROC) curve, was defined as "core" WW. The diagnostic performances of the mediastinal window setting (WW/WL, 350/35 HU) and core window setting were then compared in the testing group. RESULTS Of the 437 SSNs, 88 were pre-invasive [17 atypical adenomatous hyperplasia (AAH) and 71 adenocarcinoma in situ (AIS)], 349 were invasive [233 minimally invasive adenocarcinoma (MIA), 116 invasive adenocarcinoma (IA)]. In training group, the core WW of 1175 HU was the optimal cut-off to detect solid components of SSNs (AUC:0.79). In testing group, the sensitivity, specificity, positive, negative predictive value, and diagnostic accuracy for SSN invasiveness were 49.4%, 90.5%, 95.7%, 29.7%, and 57.3% for mediastinal window setting, and 87.6%, 76.2%, 91.6%, 76.2%, and 85.5% for core window setting. CONCLUSION At 120 kVp, core window setting (WW/WL, 1175/35 HU) outperformed the traditional mediastinal window setting to diagnose the invasiveness of SSNs.
Collapse
Affiliation(s)
- Xiaonan Cui
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin, People's Republic of China; University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands
| | - Shuxuan Fan
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin, People's Republic of China
| | - Marjolein A Heuvelmans
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands; Medisch Spectrum Twente, Department of Pulmonology, Enschede, the Netherlands
| | - Daiwei Han
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands
| | - Yingru Zhao
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin, People's Republic of China
| | - Harry J M Groen
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands
| | - Monique D Dorrius
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands
| | - Matthijs Oudkerk
- Institute for Diagnostic Accuracy (iDNA) BV, Groningen, the Netherlands; University of Groningen, Faculty of Medical Sciences, Groningen, the Netherlands
| | - Geertruida H de Bock
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands
| | - Rozemarijn Vliegenthart
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands
| | - Zhaoxiang Ye
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin, People's Republic of China.
| |
Collapse
|
19
|
Gong J, Liu J, Hao W, Nie S, Zheng B, Wang S, Peng W. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol 2019; 30:1847-1855. [PMID: 31811427 DOI: 10.1007/s00330-019-06533-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/10/2019] [Accepted: 10/18/2019] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists. METHODS First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs. RESULTS The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6. CONCLUSIONS The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm. KEY POINTS • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma.
Collapse
|
20
|
Hutchinson BD, Shroff GS, Truong MT, Ko JP. Spectrum of Lung Adenocarcinoma. Semin Ultrasound CT MR 2019; 40:255-264. [DOI: 10.1053/j.sult.2018.11.009] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
21
|
Robins M, Solomon J, Koweek LMH, Christensen J, Samei E. Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases. Med Phys 2019; 46:1931-1937. [PMID: 30703259 DOI: 10.1002/mp.13412] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Revised: 12/04/2018] [Accepted: 01/18/2019] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To make available to the medical imaging community a computed tomography (CT) image database composed of hybrid datasets (patient CT images with digitally inserted anthropomorphic lesions) where lesion ground truth is known a priori. It is envisioned that such a dataset could be a resource for the assessment of CT image quality, machine learning, and imaging technologies [e.g., computer aided detection (CAD) and segmentation algorithms]. ACQUISITION AND VALIDATION METHODS This HIPPA compliant, IRB waiver of approval study consisted of utilizing 120 chest and 100 abdominal clinically acquired adult CT exams. One image series per patient exam was utilized based on coverage of the anatomical region of interest (either the thorax or abdomen). All image series were de-identified. Simulated lesions were derived from a library of anatomically informed digital lesions (93 lung and 50 liver lesions) where six and four digital lesions with nominal diameters ranging from 4 to 20 mm were inserted into lung and liver image series, respectively. Locations for lesion insertion were randomly chosen. A previously validated lesion simulation and virtual insertion technique were utilized. The resulting hybrid images were reviewed by three experienced radiologists to assure similarity with routine clinical imaging in a diverse adult population. DATA FORMAT AND USAGE NOTES The database is composed of four datasets that contain 100 patient cases each, for a total of 400 image series accompanied by Matlab.mat tables that provide descriptive information about the virtually inserted lesions (i.e., size, shape, opacity, and insertion location in physical (world) coordinates and voxel indices). All image and metadata are stored in DICOM format on the Quantitative Imaging Data Warehouse (https://qidw.rsna.org/#collection/57d463471cac0a4ec8ff8f46/folder/5b23dceb1cac0a4ec800a770?dialog=login), in two sets: (a) QIBA CT Hybrid Dataset I which contains Lung I and Liver I datasets, and (b) QIBA CT Hybrid Dataset II which contains Lung II and Liver II datasets. The QIDW is supported by the Radiological Society of North America (RSNA). Registration is required upon initial log in. POTENTIAL APPLICATIONS By simulating lesion opacity (full solid, part solid and ground glass), size, and texture, the relationship between lesion morphology and segmentation or CAD algorithm performance can be investigated without the need for repetitive patient exams. This database can also serve as a reference standard for device and reader performance studies.
Collapse
Affiliation(s)
- Marthony Robins
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Lynne M Hurwitz Koweek
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Jared Christensen
- Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA.,Departments of Biomedical Engineering, Electrical and Computer Engineering, and Physics, Duke University Medical Center, Durham, NC, 27705, USA
| |
Collapse
|
22
|
Measurement of Multiple Solid Portions in Part-Solid Nodules for T Categorization: Evaluation of Prognostic Implication. J Thorac Oncol 2018; 13:1864-1872. [DOI: 10.1016/j.jtho.2018.09.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 07/26/2018] [Accepted: 09/07/2018] [Indexed: 12/17/2022]
|
23
|
Lee JH, Kim TH, Lee S, Han K, Byun MK, Chang YS, Kim HJ, Lee GD, Park CH. High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules. PLoS One 2018; 13:e0205490. [PMID: 30335856 PMCID: PMC6193644 DOI: 10.1371/journal.pone.0205490] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 09/26/2018] [Indexed: 12/18/2022] Open
Abstract
Objectives To evaluate and compare the diagnostic accuracy of high versus low attenuation thresholds for determining the solid component of ground-glass opacity nodules (GGNs) for the differential diagnosis of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA). Methods Eighty-six pathologically confirmed GGNs < 3 cm observed in 86 patients (27 male, 59 female; mean age, 59.3 ± 11.0 years) between January 2013 and December 2015 were retrospectively included. The solid component of each GGN was defined using two different attenuation thresholds: high (-160 Hounsfield units [HU]) and low (-400 HU). According to the presence or absence of solid portions, each GGN was categorized as a pure GGN or part-solid GGN. Solid components were regarded as indicators of invasive foci, suggesting MIA or IA. Results Among the 86 GGNs, there were 57 cases of IA, 19 of MIA, and 10 of AIS. Using the high attenuation threshold, 44 were categorized as pure GGNs and 42 as part-solid GGNs. Using the low attenuation threshold, 13 were categorized as pure GGNs and 73 as part-solid GGNs. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the invasive focus were 55.2%, 100%, 100%, 22.7%, and 60.4%, respectively, for the high attenuation threshold, and 93.4%, 80%, 97.2%, 61.5%, and 91.8%, respectively, for the low attenuation threshold. Conclusion The low attenuation threshold was better than the conventional high attenuation threshold for determining the solid components of GGNs, which indicate invasive foci.
Collapse
Affiliation(s)
- Jae Ho Lee
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
| | - Tae Hoon Kim
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
| | - Sungsoo Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Kwang Byun
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yoon Soo Chang
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyung Jung Kim
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Geun Dong Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
- * E-mail: (GDL); (CHP)
| | - Chul Hwan Park
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
- * E-mail: (GDL); (CHP)
| |
Collapse
|
24
|
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
|
25
|
A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection. Eur Radiol 2018; 29:1674-1683. [PMID: 30255253 DOI: 10.1007/s00330-018-5739-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 07/25/2018] [Accepted: 08/28/2018] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model. METHODS This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity. RESULTS Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively). CONCLUSIONS Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations. KEY POINTS • Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules. • Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules. • Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.
Collapse
|
26
|
Implication of total tumor size on the prognosis of patients with clinical stage IA lung adenocarcinomas appearing as part-solid nodules: Does only the solid portion size matter? Eur Radiol 2018; 29:1586-1594. [PMID: 30132107 DOI: 10.1007/s00330-018-5685-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/11/2018] [Accepted: 07/27/2018] [Indexed: 02/08/2023]
Abstract
OBJECTIVES The aim was to investigate the effect of clinico-radiologic variables, including total tumor (Ttotal) size and clinical T category, on the prognosis of patients with stage IA (T1N0M0) lung adenocarcinomas appearing as part-solid nodules (PSNs). METHODS This institutional review board-approved retrospective study included 506 patients (male:female = 200:306; median age, 62 years) with PSNs of the adenocarcinoma spectrum in clinical stage IA who underwent standard lobectomy at a single tertiary medical center. Prognostic stratification of the patients in terms of disease-free survival was analyzed with variables including age, sex, Ttotal size, solid portion size, clinical T category, and tumor location using univariate and subsequent multivariate Cox regression analysis. Subgroup analysis was performed to reveal the effect of the Ttotal size at each clinical T category. RESULTS Multivariate Cox regression analysis demonstrated that Ttotal size*cT1b [interaction term; hazard ratio (HR) = 1.091; 95% confidence interval (CI): 1.015, 1.173; p = 0.019] and cT1c (HR = 68.436; 95% CI: 2.797, 1674.415; p = 0.010) were independent risk factors for the tumor recurrence. When patients with cT1b were dichotomized based on a Ttotal size cutoff of 3.0 cm, PSNs with Ttotal > 3.0 cm showed a significantly worse outcome (HR = 3.796; 95% CI: 1.006, 14.317; p = 0.049). No significant difference was observed in the probability of recurrence between cT1b with Ttotal > 3.0 cm and cT1c (p = 0.915). CONCLUSIONS Ttotal size is a significant prognostic factor in adenocarcinoma patients in cT1b without lymph node or distant metastasis. PSNs in cT1b with Ttotal > 3.0 cm have a comparable risk of lung cancer recurrence to those in cT1c. KEY POINTS • Current T descriptor was a powerful prognostic factor in stage IA adenocarcinomas appearing as part-solid nodules. • Total tumor size further stratified risk of recurrence of adenocarcinomas in cT1b. • Upstaging of tumors in cT1b with total tumor size > 3.0 cm may be more appropriate.
Collapse
|
27
|
|
28
|
Kim H, Goo JM, Park CM. Evaluation of T categories for pure ground-glass nodules with semi-automatic volumetry: is mass a better predictor of invasive part size than other volumetric parameters? Eur Radiol 2018; 28:4288-4295. [PMID: 29713766 DOI: 10.1007/s00330-018-5440-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 03/02/2018] [Accepted: 03/19/2018] [Indexed: 12/19/2022]
Abstract
OBJECTIVES This study aimed to investigate the diagnostic advantage of nodule mass in differentiating invasive pulmonary adenocarcinomas (IPAs) among pure ground-glass nodules (pGGNs) over other volumetric measurements. Another aim of this study was to analyse the correlation between volumetric measurements on computed tomography (CT) scans and the pathological invasive component size. METHODS This Institutional Review Board-approved retrospective study included 117 patients (men:women = 53:64; mean age, 57.3 years) with 117 pGGNs. Semi-automatic segmentation was performed for all nodules, and volumetric measurements, such as nodule volume, attenuation, mass, two-dimensional (2D) average diameter and three-dimensional (3D) longest diameter, were obtained. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performances of the volumetric parameters in discriminating IPAs. Spearman correlation coefficients were calculated between the volumetric measurements and the invasive component size. RESULTS Area under the ROC curve for mass was 0.792 (95% CI, 0.691-0.872) in non-enhanced CT and 0.730 (95% CI, 0.607-0.832) in contrast-enhanced CT. Nodule mass was not superior to 2D average diameter for the differentiation of IPAs in both non-enhanced (0.792 vs 0.780; p = 0.501) CT and contrast-enhanced CT scans (0.730 vs 0.700; p = 0.319). The correlation between the volumetric measurements (mass, 3D longest diameter and 2D average diameter) and the invasive component size was moderate (Spearman's rho, 0.401-0.422) in non-enhanced CT and weak (Spearman's rho, 0.276-0.310) in contrast-enhanced CT. CONCLUSIONS Nodule mass measurement had no strength over other volumetric parameters for the prediction of pathological invasiveness in the diagnosis of pGGNs. KEY POINTS • Mass is not superior to other volumetric measurements for the diagnosis of pure ground-glass nodules. • Mass and two-dimensional average diameter exhibited comparable performance for the discrimination of invasive adenocarcinomas among pure ground-glass nodules. • The diagnostic performance of volumetric measurements was lower on contrast-enhanced CT scans. • The correlation between the volumetric measurements and the invasive component size was moderate on non-enhanced CT scans and weak on contrast-enhanced CT scans.
Collapse
Affiliation(s)
- Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea. .,Cancer Research Institute, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.,Cancer Research Institute, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| |
Collapse
|
29
|
Kim H, Park CM. Current perspectives for the size measurement of screening-detected lung nodules. J Thorac Dis 2018; 10:1242-1244. [PMID: 29708162 DOI: 10.21037/jtd.2018.03.24] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Cancer Research Institute, Seoul National University, Seoul, Korea
| |
Collapse
|
30
|
Garzelli L, Goo JM, Ahn SY, Chae KJ, Park CM, Jung J, Hong H. Improving the prediction of lung adenocarcinoma invasive component on CT: Value of a vessel removal algorithm during software segmentation of subsolid nodules. Eur J Radiol 2018; 100:58-65. [DOI: 10.1016/j.ejrad.2018.01.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 11/15/2017] [Accepted: 01/15/2018] [Indexed: 12/17/2022]
|
31
|
Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement. Eur Radiol 2017; 28:2124-2133. [PMID: 29196857 DOI: 10.1007/s00330-017-5171-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 09/30/2017] [Accepted: 11/03/2017] [Indexed: 12/17/2022]
Abstract
OBJECTIVES We hypothesized that semi-automatic diameter measurements would improve the accuracy and reproducibility in discriminating preinvasive lesions and minimally invasive adenocarcinomas from invasive pulmonary adenocarcinomas appearing as subsolid nodules (SSNs) and increase the reproducibility in classifying SSNs. METHODS Two readers independently performed semi-automatic and manual measurements of the diameters of 102 SSNs and their solid portions. Diagnostic performance in predicting invasive adenocarcinoma based on diameters was tested using logistic regression analysis with subsequent receiver operating characteristic curves. Inter- and intrareader reproducibilities of diagnosis and SSN classification according to Fleischner's guidelines were investigated for each measurement method using Cohen's κ statistics. RESULTS Semi-automatic effective diameter measurements were superior to manual average diameters for the diagnosis of invasive adenocarcinoma (AUC, 0.905-0.923 for semi-automatic measurement and 0.833-0.864 for manual measurement; p<0.05). Reproducibility of diagnosis between the readers also improved with semi-automatic measurement (κ=0.924 for semi-automatic measurement and 0.690 for manual measurement, p=0.012). Inter-reader SSN classification reproducibility was significantly higher with semi-automatic measurement (κ=0.861 for semi-automatic measurement and 0.683 for manual measurement, p=0.022). CONCLUSIONS Semi-automatic effective diameter measurement offers an opportunity to improve diagnostic accuracy and reproducibility as well as the classification reproducibility of SSNs. KEY POINTS • Semi-automatic effective diameter measurement improves the diagnostic accuracy for pulmonary subsolid nodules. • Semi-automatic measurement increases the inter-reader agreement on the diagnosis for subsolid nodules. • Semi-automatic measurement augments the inter-reader reproducibility for the classification of subsolid nodules.
Collapse
|
32
|
Rydzak CE, Armato SG, Avila RS, Mulshine JL, Yankelevitz DF, Gierada DS. Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening. Br J Radiol 2017; 91:20170401. [PMID: 28830225 DOI: 10.1259/bjr.20170401] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
After years of assessment through controlled clinical trials, low-dose CT screening for lung cancer is becoming part of clinical practice. As with any cancer screening test, those undergoing lung cancer screening are not being evaluated for concerning signs or symptoms, but are generally in good health and proactively trying to prevent premature death. Given the resultant obligation to achieve the screening aim of early diagnosis while also minimizing the potential for morbidity from workup of indeterminate but ultimately benign screening abnormalities, careful implementation of screening with conformance to currently recognized best practices and a focus on quality assurance is essential. In this review, we address the importance of each component of the screening process to optimize the effectiveness of CT screening, discussing options for quality assurance at each step. We also discuss the potential added advantages, quality assurance requirements and current status of quantitative imaging biomarkers related to lung cancer screening. Finally, we highlight suggestions for improvements and needs for further evidence in evaluating the performance of CT screening as it transitions from the research trial setting into daily clinical practice.
Collapse
Affiliation(s)
- Chara E Rydzak
- 1 Mallinckrodt Institute of Radiology, Washington University School of Medicine , St. Louis, MO , USA
| | - Samuel G Armato
- 2 Department of Radiology, University of Chicago , Chicago, IL , USA
| | | | - James L Mulshine
- 4 Department of Internal Medicine, Rush University , Chicago, IL , USA
| | - David F Yankelevitz
- 5 Department of Radiology, Icahn School of Medicine at Mount Sinai , New York, NY , USA
| | - David S Gierada
- 1 Mallinckrodt Institute of Radiology, Washington University School of Medicine , St. Louis, MO , USA
| |
Collapse
|
33
|
Affiliation(s)
- Julien G Cohen
- Pôle Imagerie, Centre Hospitalier Universitaire de Grenoble, Grenoble, France.,Grenoble Alpes Université, Grenoble, France
| | - Gilbert R Ferretti
- Pôle Imagerie, Centre Hospitalier Universitaire de Grenoble, Grenoble, France.,Grenoble Alpes Université, Grenoble, France
| |
Collapse
|
34
|
Devaraj A, van Ginneken B, Nair A, Baldwin D. Use of Volumetry for Lung Nodule Management: Theory and Practice. Radiology 2017; 284:630-644. [DOI: 10.1148/radiol.2017151022] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Anand Devaraj
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| | - Bram van Ginneken
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| | - Arjun Nair
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| | - David Baldwin
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| |
Collapse
|
35
|
Zheng W, Wang Q, Wang Y, Guo F, Wang X, Yu T. [Threshold Segmentation of Pulmonary Subsolid Nodules on CT Images:
Detection and Quantification of the Solid Component]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2017; 20:341-345. [PMID: 28532542 PMCID: PMC5973070 DOI: 10.3779/j.issn.1009-3419.2017.05.07] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The detection and quantification of solid components in pulmonary subsolid nodules (SSN) are of vital importance on differential diagnosis, pathological speculation and prognosis prediction. However, no objective and wide-accepted criterion has been built up to now. The purpose of this study is to explore the optimal threshold that can be used for the detection and quantification of solid components in SSNs by using threshold segmentation method on computed tomography (CT) images. METHODS CT images of 102 SSNs were retrospectively analyzed. To establish a reference standard, the observers made judgments on whether the solid component existed in every SSN and did manual measurements of the volume of solid component with the help of software. Threshold segmentations of every nodule were then performed using different threshold settings and all of the measured volumes were assumed to be solid volumes, then solid-to-total volume ratios were calculated. The results were compared with the reference standards using the receiver operating characteristic curve and Wilcoxon test. RESULTS The application of thresholds as -250 HU or -300 HU resulted in high diagnostic value on the detection of solid component, with area under curve values as 0.982 and 0.977, respectively; the cut-off values of solid-to-total volume ratio were 1.10% and 6.14%, respectively; the median volumes of solid components were 202.7 mm3 (598.2 mm3), 247.1 mm3(696.0 mm3), which were not significantly different from the reference standard[199.5 mm3 (743.1 mm3)](P=0.125,1, 0.061,3). CONCLUSIONS Threshold segmentation on chest CT images is valuable to detect and quantify the solid component on SSNs, the thresholds as -250 HU and -300 HU are recommended.
.
Collapse
Affiliation(s)
- Wensong Zheng
- Medical Imaging Department, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qing Wang
- Medical Imaging Department, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Ying Wang
- Medical Imaging Department, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Fangfang Guo
- Medical Imaging Department, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xinyue Wang
- Medical Imaging Department, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Tielian Yu
- Medical Imaging Department, Tianjin Medical University General Hospital, Tianjin 300052, China
| |
Collapse
|
36
|
Comparison of the effects of model-based iterative reconstruction and filtered back projection algorithms on software measurements in pulmonary subsolid nodules. Eur Radiol 2017; 27:3266-3274. [DOI: 10.1007/s00330-016-4716-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 11/01/2016] [Accepted: 12/16/2016] [Indexed: 01/06/2023]
|
37
|
Cohen JG, Reymond E, Jankowski A, Brambilla E, Arbib F, Lantuejoul S, Ferretti GR. Lung adenocarcinomas: correlation of computed tomography and pathology findings. Diagn Interv Imaging 2016; 97:955-963. [PMID: 27639313 DOI: 10.1016/j.diii.2016.06.021] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 06/29/2016] [Accepted: 06/30/2016] [Indexed: 12/13/2022]
Abstract
Adenocarcinoma is the most common histologic type of lung cancer. Recent lung adenocarcinoma classifications from the International Association for the Study of Lung cancer, the American Thoracic Society and the European Respiratory Society (IASLC/ETS/ERS, 2011) and World Health Organization (WHO, 2015) define a wide range of adenocarcinoma types and subtypes featuring different prognosis and management. This spectrum of lesions translates into various CT presentations and features, which generally show good correlation with histopathology, stressing the key role of the radiologist in the diagnosis and management of those patients. This review aims at helping radiologists to understand the basics of the up-to-date adenocarcinoma pathological classifications, radio-pathological correlations and how to use them in the clinical setting, as well as other imaging-related correlations (radiogenomics, quantitative analysis, PET-CT).
Collapse
Affiliation(s)
- J G Cohen
- Clinique universitaire de radiologie et imagerie médicale (CURIM), CHU A.-Michallon, BP 217, 38043 Grenoble cedex 9, France; Université Grenoble-Alpes, 38000 Grenoble, France.
| | - E Reymond
- Clinique universitaire de radiologie et imagerie médicale (CURIM), CHU A.-Michallon, BP 217, 38043 Grenoble cedex 9, France.
| | - A Jankowski
- Clinique universitaire de radiologie et imagerie médicale (CURIM), CHU A.-Michallon, BP 217, 38043 Grenoble cedex 9, France.
| | - E Brambilla
- Université Grenoble-Alpes, 38000 Grenoble, France; Département d'anatomo-cytologie pathologie (DACP), CHU A.-Michallon, 38043 Grenoble, France; Inserm U 823, institut A.-Bonniot, 38000 Grenoble, France.
| | - F Arbib
- Clinique universitaire de pneumologie, pôle d'oncologie, CHU A.-Michallon, 38043 Grenoble, France.
| | - S Lantuejoul
- Université Grenoble-Alpes, 38000 Grenoble, France; Département d'anatomo-cytologie pathologie (DACP), CHU A.-Michallon, 38043 Grenoble, France; Inserm U 823, institut A.-Bonniot, 38000 Grenoble, France.
| | - G R Ferretti
- Clinique universitaire de radiologie et imagerie médicale (CURIM), CHU A.-Michallon, BP 217, 38043 Grenoble cedex 9, France; Université Grenoble-Alpes, 38000 Grenoble, France; Département d'anatomo-cytologie pathologie (DACP), CHU A.-Michallon, 38043 Grenoble, France.
| |
Collapse
|