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Li Q, Xiao T, Li J, Niu Y, Zhang G. The diagnosis and management of multiple ground-glass nodules in the lung. Eur J Med Res 2024; 29:305. [PMID: 38824558 PMCID: PMC11143686 DOI: 10.1186/s40001-024-01904-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
The prevalence of low-dose CT (LDCT) in lung cancer screening has gradually increased, and more and more lung ground glass nodules (GGNs) have been detected. So far, a consensus has been reached on the treatment of single pulmonary ground glass nodules, and there have been many guidelines that can be widely accepted. However, at present, more than half of the patients have more than one nodule when pulmonary ground glass nodules are found, which means that different treatment methods for nodules may have different effects on the prognosis or quality of life of patients. This article reviews the research progress in the diagnosis and treatment strategies of pulmonary multiple lesions manifested as GGNs.
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Affiliation(s)
- Quanqing Li
- Department of Thoracic Surgery, Second Hospital of Jilin University, Changchun, Jilin Province, China
| | - Tianjiao Xiao
- Department of Thoracic Surgery, Second Hospital of Jilin University, Changchun, Jilin Province, China
| | - Jindong Li
- Department of Thoracic Surgery, Second Hospital of Jilin University, Changchun, Jilin Province, China
| | - Yan Niu
- Jilin University, Changchun, Jilin Province, China
| | - Guangxin Zhang
- Department of Thoracic Surgery, Second Hospital of Jilin University, Changchun, Jilin Province, China.
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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.
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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
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Gao R, Gao Y, Zhang J, Zhu C, Zhang Y, Yan C. A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence. J Cancer Res Clin Oncol 2023; 149:15323-15333. [PMID: 37624396 DOI: 10.1007/s00432-023-05262-4] [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: 07/17/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
PURPOSE To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance. METHODS 187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. RESULTS The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05). CONCLUSION For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.
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Affiliation(s)
- Rongji Gao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Yinghua Gao
- Department of Pathology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Juan Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Chunyu Zhu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China
| | - Yue Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China.
| | - Chengxin Yan
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong Province, China.
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Koike H, Ashizawa K, Tsutsui S, Kurohama H, Okano S, Nagayasu T, Kido S, Uetani M, Toya R. Differentiation Between Heterogeneous GGN and Part-Solid Nodule Using 2 D Grayscale Histogram Analysis of Thin-Section CT Image. Clin Lung Cancer 2023; 24:541-550. [PMID: 37407293 DOI: 10.1016/j.cllc.2023.06.001] [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: 04/27/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Abstract
INTRODUCTION/BACKGROUND To evaluate cases of surgically resected pulmonary adenocarcinoma (Ad) with heterogenous ground-glass nodules (HGGNs) or part-solid nodules (PSNs) and to clarify the differences between them, and between invasive adenocarcinoma (IVA) and minimally invasive adenocarcinoma (MIA) + adenocarcinoma in situ (AIS) using grayscale histogram analysis of thin-section computed tomography (TSCT). MATERIALS AND METHODS 241 patients with pulmonary Ad were retrospectively classified into HGGNs and PSNs on TSCT by three thoracic radiologists. Sixty HGGNs were classified into 17 IVAs, 26 MIAs, and 17 AISs. 181 PSNs were classified into 114 IVAs, 55 MIAs, and 12 AISs. RESULTS We found significant differences in area (P = 0.0024), relative size of solid component (P <0.0001), circumference (P <0.0001), mean CT value (P <0.0001), standard deviation of the CT value (P <0.0001), maximum CT value (P <0.0001), skewness (P <0.0001), kurtosis (P <0.0001), and entropy (P <0.0001) between HGGNs and PSNs. In HGGNs, we found significant differences in relative size of solid component (P <0.0001), mean CT value (P = 0.0005), standard deviation of CT value (P = 0.0071), maximum CT value (P = 0.0237), and skewness (P = 0.0027) between IVAs and MIA+AIS lesions. In PSNs, we found significant differences in area (P = 0.0029), relative size of solid component (P = 0.0003), circumference (P = 0.0004), mean CT value (P = 0.0011), skewness (P = 0.0009), and entropy (P = 0.0002) between IVAs and the MIA+AIS lesions. CONCLUSION Quantitative evaluations using grayscale histogram analysis can clearly distinguish between HGGNs and PSNs, and may be useful for estimating the pathology of such lesions.
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Affiliation(s)
- Hirofumi Koike
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Kazuto Ashizawa
- Departments of Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
| | - Shin Tsutsui
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Hirokazu Kurohama
- Department of Pathology, Nagasaki University Hospital, Nagasaki, Japan
| | - Shinji Okano
- Department of Pathology, Nagasaki University Hospital, Nagasaki, Japan
| | - Takeshi Nagayasu
- Departments of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shoji Kido
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Masataka Uetani
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Ryo Toya
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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Liu J, Yang X, Li Y, Xu H, He C, Qing H, Ren J, Zhou P. Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening. Quant Imaging Med Surg 2022; 12:2917-2931. [PMID: 35502397 PMCID: PMC9014141 DOI: 10.21037/qims-21-912] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 02/03/2022] [Indexed: 08/04/2023]
Abstract
BACKGROUND Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS). METHODS A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. RESULTS The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively). CONCLUSIONS The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.
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Affiliation(s)
- Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Li
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Li Y, Liu J, Yang X, Xu H, Qing H, Ren J, Zhou P. Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening. Br J Radiol 2022; 95:20211048. [PMID: 34995082 PMCID: PMC10993960 DOI: 10.1259/bjr.20211048] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/16/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). METHODS A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. RESULTS The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801-0.935), 0.929 (95% CI, 0.862-0.971), 0.941 (95% CI, 0.876-0.978), and 0.884 (95% CI, 0.805-0.938) in the primary cohort and 0.897 (95% CI, 0.768-0.968), 0.933 (95% CI, 0.815-0.986), 0.901 (95% CI, 0.773-0.970), and 0.828 (95% CI, 0.685-0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). CONCLUSION The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be pre-operative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. ADVANCES IN KNOWLEDGE The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be pre-operative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital &
Institute, Sichuan Cancer Center, School of Medicine, University of
Electronic Science and Technology of China,
Chengdu, China
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Jiang Y, Xiong Z, Zhao W, Zhang J, Guo Y, Li G, Li Z. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs. Gan To Kagaku Ryoho 2022; 70:880-890. [PMID: 35301662 DOI: 10.1007/s11748-022-01801-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/03/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs. PATIENTS AND METHODS We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC). RESULTS The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness. CONCLUSION The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.
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Affiliation(s)
- Yining Jiang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Guosheng Li
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China. .,Dalian Engineering Research Centre for Artificial Intelligence in Medical Imaging, Dalian, China.
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Yu Y, Fu Y, Chen X, Zhang Y, Zhang F, Li X, Zhao X, Cheng J, Wu H. Dual-layer spectral detector CT: predicting the invasiveness of pure ground-glass adenocarcinoma. Clin Radiol 2022; 77:e458-e465. [DOI: 10.1016/j.crad.2022.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 02/02/2022] [Indexed: 12/15/2022]
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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.
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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.)
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Zhang T, Li X, Liu J. Prediction of the Invasiveness of Ground-Glass Nodules in Lung Adenocarcinoma by Radiomics Analysis Using High-Resolution Computed Tomography Imaging. Cancer Control 2022; 29:10732748221089408. [PMID: 35848489 PMCID: PMC9297444 DOI: 10.1177/10732748221089408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background Pure ground-glass nodules (pGGNs) have been considered inert tumors due to their biological behavior; however, their prognosis is not completely consistent because of differences in internal pathological component. The aim of this study was to explore whether radiomics can be used to identify the invasiveness of pGGNs. Methods The retrospective study received the relevant ethical approval. After postoperative pathological confirmation, sixty-five patients with lung adenocarcinoma pGGNs (≤30 mm) were enrolled in this study from January 2015 to October 2018. All the cases were randomly divided into training and test groups in a 7:3 ratio. In total, 385 radiomics features were obtained from HRCT images, and then least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the training group to obtain optimal features to distinguish the invasion degree of lesions. The diagnostic efficiency of the radiomics model was estimated by the area under the curve (AUC) of the receiver operating curve (ROC), and verified by the test group. Results The optimal features (“GLCMEntropy_angle135_offset1” and “Sphericity”) were selected after applying the LASSO regression to develop the proposed radiomics model. This prediction model exhibited good differentiation between pre-invasive and invasive lesions. The AUC for the test group was 0.824 (95%CI: 0.599-1.000), indicating that the radiomics model has some prediction ability. Conclusion The HRCT radiomics features can discriminate pre-invasive from invasive lung adenocarcinoma pGGNs. This non-invasive method can provide more information for surgeons before operation, and can also predict the prognosis of patients to some extent.
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Affiliation(s)
- Tianqi Zhang
- College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China.,Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China
| | - Xiuling Li
- College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China
| | - Jianhua Liu
- Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China
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11
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Shi L, Zhao J, Peng X, Wang Y, Liu L, Sheng M. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. Eur J Radiol 2021; 144:109956. [PMID: 34563797 DOI: 10.1016/j.ejrad.2021.109956] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To provide an overview of the available studies investigating the use of computer tomography (CT) radiomics features for differentiating invasive adenocarcinomas (IAC) from indolent lung adenocarcinomas presenting as ground-glass nodules (GGNs), to identify the bias of the studies and to propose directions for future research. METHOD PubMed, Embase, Web of Science Core Collection were searched for relevant studies. The studies differentiating IAC from indolent lung adenocarcinomas appearing as GGNs based on CT radiomics features were included. Basic information, patient information, CT-scanner information, technique information and performance information were extracted for each included study. The quality of each study was assessed using the Radiomic Quality Score (RQS) and the Prediction model Risk of Bias Assessment Tool (PROBAST). RESULTS Twenty-eight studies were included with patients ranging from 34 to 794. All of them were retrospective. Patients in three studies were from multiple centers. Most studies segmented regions of interest manually. Pyradiomics and AK software were the most frequently used for features extraction. The number of radiomics features extracted varied from 7 to 10329. Logistic regression was the most frequently chosen model. Entropy was identified as radiomics signature in seven studies. The AUC of included studies ranged from 0.77 to 0.98 in 15 validation sets. The percentage RQS ranged from 3% to 50%. According to PROBAST, the overall risk of bias (ROB) was high in 89.3% (25/28) of included studies, unclear in 7.1% (2/28) of included studies, and low in 3.6% (1/28) of included studies. All studies were low concern regarding the applicability of primary studies to the review question. CONCLUSION CT radiomics-based model is promising and encouraging in differentiating IAC from indolent lung adenocarcinomas, though they require methodological rigor. Well-designed studies are necessary to demonstrate their validity and standardization of methods and results can prompt their use in daily clinical practice.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China; Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Xueqing Peng
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunpeng Wang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China; School of Basic Medical Sciences, and Academy of Engineering and Technology, Fudan University, Shanghai, China.
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China.
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12
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Cao XZ, Luo SZ, Li JC, Pan JH. An optimized automatic prediction of stage and grade in bladder cancer based on U-ResNet. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method.
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Affiliation(s)
- Xin-Zi Cao
- School of Software, South China Normal University, Guangzhou, China
| | - Sheng-Zhou Luo
- School of Software, South China Normal University, Guangzhou, China
| | - Jing-Cong Li
- School of Software, South China Normal University, Guangzhou, China
| | - Jia-Hui Pan
- School of Software, South China Normal University, Guangzhou, China
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13
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Xiong Z, Jiang Y, Che S, Zhao W, Guo Y, Li G, Liu A, Li Z. Use of CT radiomics to differentiate minimally invasive adenocarcinomas and invasive adenocarcinomas presenting as pure ground-glass nodules larger than 10 mm. Eur J Radiol 2021; 141:109772. [PMID: 34022476 DOI: 10.1016/j.ejrad.2021.109772] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 04/12/2021] [Accepted: 05/10/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE This study aimed to develop a model based on radiomics features extracted from computed tomography (CT) images to effectively differentiate between minimally invasive adenocarcinomas (MIAs) and invasive adenocarcinomas (IAs) manifesting as pure ground-glass nodules (pGGNs) larger than 10 mm. METHOD This retrospective study included patients who underwent surgical resection for persistent pGGN between November 2012 and June 2018 and diagnosed with MIAs or IAs. The patients were randomly assigned to the training and test cohorts. The correlation coefficient method and the least absolute shrinkage and selection operator (LASSO) method were applied to select radiomics features useful for constructing a model whose performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The radiomics model was compared to a standard CT model (shape, volume and mean CT value of the largest cross-section) and the combined radiomics-standard CT model using univariate and multivariate logistic regression analysis. RESULTS The radiomics model showed better discriminative ability (training AUC, 0.879; test AUC, 0.877) than the standard CT model (training AUC, 0.820; test AUC, 0.828). The combined model (training AUC, 0.879; test AUC, 0.870) did not demonstrate improved performance compared with the radiomics model. Radiomics_score was an independent predictor of invasiveness following multivariate logistic analysis. CONCLUSIONS For pGGNs larger than 10 mm, the radiomics model demonstrated superior diagnostic performance in differentiating between IAs and MIAs, which may be useful to clinicians for diagnosis and treatment selection.
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Affiliation(s)
- Ziqi Xiong
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Yining Jiang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Siyu Che
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Wenjing Zhao
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Yan Guo
- GE Healthcare, Shenyang, China
| | - Guosheng Li
- Department of Pathology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Ailian Liu
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Zhiyong Li
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
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Fu G, Yu H, Liu J, Xia T, Xiang L, Li P, Huang D, Lin L, Zhuang Y, Yang Y. Arc concave sign on thin-section computed tomography:A novel predictor for invasive pulmonary adenocarcinoma in pure ground-glass nodules. Eur J Radiol 2021; 139:109683. [PMID: 33836337 DOI: 10.1016/j.ejrad.2021.109683] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/01/2021] [Accepted: 03/23/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE We aimed to investigate the risk factors of invasive pulmonary adenocarcinoma, especially to report and validate the use of our newly identified arc concave sign in predicting invasiveness of pure ground-glass nodules (pGGNs). METHODS From January 2015 to August 2018, we retrospectively enrolled 302 patients with 306 pGGNs ≤ 20 mm pathologically confirmed (141 preinvasive lesions and 165 invasive lesions). Arc concave sign was defined as smooth and sunken part of the edge of the lesion on thin-section computed tomography (TSCT). The degree of arc concave sign was expressed by the arc chord distance to chord length ratio (AC-R); deep arc concave sign was defined as AC-R larger than the optimal cut-off value. Logistic regression analysis was used to identify the independent risk factors of invasiveness. RESULTS Arc concave sign was observed in 65 of 306 pGGNs (21.2 %), and deep arc concave sign (AC-R > 0.25) were more common in invasive lesions (P = 0.008). Under microscope, interlobular septal displacements were found at tumour surface. Multivariate analysis indicated that irregular shape (OR, 3.558; CI: 1.374-9.214), presence of deep arc concave sign (OR, 3.336; CI: 1.013-10.986), the largest diameter > 10.1 mm (OR, 4.607; CI: 2.584-8.212) and maximum density > -502 HU (OR, 6.301; CI: 3.562-11.148) were significant independent risk factors of invasive lesions. CONCLUSIONS Arc concave sign on TSCT is caused by interlobular septal displacement. The degree of arc concave sign can reflect the invasiveness of pGGNs. Invasive lesions can be effectively distinguished from preinvasive lesions by the presence of deep arc concave sign, irregular shape, the largest diameter > 10.1 mm and maximum density > -502 HU in pGGNs ≤ 20 mm.
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Affiliation(s)
- Gangze Fu
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Huibo Yu
- Department of Radiology, Xiangshan Affiliated Hospital of Wenzhou Medical University, Xiangshan, 315700, China
| | - Jinjin Liu
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Tianyi Xia
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Lanting Xiang
- Depatment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Peng Li
- Depatment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Dingpin Huang
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Liaoyi Lin
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Yuandi Zhuang
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China
| | - Yunjun Yang
- Depatment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, NO.2 Fuxue Rd, Wenzhou, 325000, China.
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Xu QQ, Shan WL, Zhu Y, Huang CC, Bao SY, Guo LL. Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics. Eur J Radiol 2021; 139:109667. [PMID: 33867180 DOI: 10.1016/j.ejrad.2021.109667] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 02/27/2021] [Accepted: 03/13/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To investigate the relationship between CT radiomic features, pathological classification of pulmonary nodules, and evaluate the prediction effect of different stratified progressive radiomic models on the pathological classification of pulmonary nodules. METHODS Altogether, 189 patients pathologically confirmed with pulmonary nodules from July 2017 to August 2019 who had complete data were enrolled, including 71 patients with benign nodules, 51 with malignant non-invasive nodules, and 67 with invasive nodules. Three CT radiomic models were established respectively. Model 1 classified benign and malignant nodules (including malignant non-invasive and invasive nodules). Model 2 classified malignant non-invasive and invasive nodules. Model 3 classified benign, malignant non-invasive, and invasive nodules. High-throughput feature collection was carried out for all delineated regions of interest (ROIs), and the best models were established by screening features and classifiers using intelligent methods. ROC curves and areas under the curve (AUCs) were used to evaluate the prediction efficacy of the models by calculating the sensitivity, specificity, accuracies, positive predictive values, and negative predictive values. RESULTS Through Models 1, 2, and 3, we screened out 20, 2, and 20 radiomic features, respectively, and plotted the ROC curves. In the test group, the AUC values were 0.85, 0.89, and 0.84, respectively; the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 79.66 %, 70.42 %, 84.59 %, and 81.74 % and 67.57% for Model 1, 88.06 %, 74.51 %, 82.2 %, 81.94 %, and 82.61 % for Model 2, and 71.34 %, 85.05 %, 70.37 %, 83.2 %, and 76.3 % for Model 3. CONCLUSION The radiomic feature models based on CT images could well reflect the differences between benign nodules, malignant non-invasive nodules, and invasive nodules, and assist in their classification.
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Affiliation(s)
- Qing-Qing Xu
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Wen-Li Shan
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Yan Zhu
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Chen-Cui Huang
- Department of research collaboration, Beijing Deepwise &League of PHD technology Co. LTD, R&D center, Beijing, 100089, China
| | - Si-Yu Bao
- Department of research collaboration, Beijing Deepwise &League of PHD technology Co. LTD, R&D center, Beijing, 100089, China
| | - Li-Li Guo
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China.
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16
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Dieckmeyer M, Inhuber S, Schlaeger S, Weidlich D, Mookiah MRK, Subburaj K, Burian E, Sollmann N, Kirschke JS, Karampinos DC, Baum T. Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength. Diagnostics (Basel) 2021; 11:diagnostics11020239. [PMID: 33557080 PMCID: PMC7913879 DOI: 10.3390/diagnostics11020239] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/28/2021] [Accepted: 02/01/2021] [Indexed: 11/16/2022] Open
Abstract
Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water–fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2–L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R2adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R2adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP.
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Affiliation(s)
- Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (S.S.); (E.B.); (N.S.); (J.S.K.); (T.B.)
- Correspondence: ; Tel.: +49-89-4140-4651; Fax: +49-89-4140-4653
| | - Stephanie Inhuber
- Department of Sport and Health Sciences, Technical University of Munich, Georg-Brauchle-Ring 60, 80992 Munich, Germany;
| | - Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (S.S.); (E.B.); (N.S.); (J.S.K.); (T.B.)
| | - Dominik Weidlich
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (D.W.); (D.C.K.)
| | | | - Karupppasamy Subburaj
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore;
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (S.S.); (E.B.); (N.S.); (J.S.K.); (T.B.)
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (S.S.); (E.B.); (N.S.); (J.S.K.); (T.B.)
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (S.S.); (E.B.); (N.S.); (J.S.K.); (T.B.)
| | - Dimitrios C. Karampinos
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (D.W.); (D.C.K.)
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany; (S.S.); (E.B.); (N.S.); (J.S.K.); (T.B.)
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Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact. Cancer Imaging 2021; 21:1. [PMID: 33407884 PMCID: PMC7788838 DOI: 10.1186/s40644-020-00376-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 12/18/2020] [Indexed: 12/13/2022] Open
Abstract
Background Pure ground-glass nodules (pGGNs) with pleural contact (P-pGGNs) comprise not only invasive adenocarcinoma (IAC), but also minimally invasive adenocarcinoma (MIA). Radiomics recognizes complex patterns in imaging data by extracting high-throughput features of intra-tumor heterogeneity in a non-invasive manner. In this study, we sought to develop and validate a radiomics signature to identify IAC and MIA presented as P-pGGNs. Methods In total, 100 patients with P-pGGNs (69 training samples and 31 testing samples) were retrospectively enrolled from December 2012 to May 2018. Imaging and clinical findings were also analyzed. In total, 106 radiomics features were extracted from the 3D region of interest (ROI) using computed tomography (CT) imaging. Univariate analyses were used to identify independent risk factors for IAC. The least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation was used to generate predictive features to build a radiomics signature. Receiver-operator characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy of the radiomics signature. Decision curve analyses (DCA) were also conducted to evaluate whether the radiomics signature was sufficiently robust for clinical practice. Results Univariate analysis showed significant differences between MIA (N = 47) and IAC (N = 53) groups in terms of patient age, lobulation signs, spiculate margins, tumor size, CT values and relative CT values (all P < 0.05). ROC curve analysis showed, when MIA was identified from IAC, that the critical value of tumor length diameter (TLD) was1.39 cm and the area under the ROC curve (AUC) was 0.724 (sensitivity = 0.792, specificity = 0.553). The critical CT value on the largest axial plane (CT-LAP) was − 597.45 HU, and the AUC was 0.666 (sensitivity = 0.698, specificity= 0.638). The radiomics signature consisted of seven features and exhibited a good discriminative performance between IAC and MIA, with an AUC of 0.892 (sensitivity = 0.811, specificity 0.719), and 0.862 (sensitivity = 0.625, specificity = 0.800) in training and testing samples, respectively. Conclusions Our radiomics signature exhibited good discriminative performance in differentiating IAC from MIA in P-pGGNs, and may offer a crucial reference point for follow-up and selective surgical management. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-020-00376-1.
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Meng F, Guo Y, Li M, Lu X, Wang S, Zhang L, Zhang H. Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules. Transl Oncol 2021; 14:100936. [PMID: 33221688 PMCID: PMC7689413 DOI: 10.1016/j.tranon.2020.100936] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 12/17/2022] Open
Abstract
In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916-0.964) and validation set (AUC, 0.946; 95% CI, 0.907-0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Mingyang Li
- State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China
| | - Xiaoqian Lu
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Shuo Wang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, NO.71 Xinmin Street, Changchun 130012, China.
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Zhang L, Ye Z, Ruan L, Jiang M. Pretreatment MRI-Derived Radiomics May Evaluate the Response of Different Induction Chemotherapy Regimens in Locally advanced Nasopharyngeal Carcinoma. Acad Radiol 2020; 27:1655-1664. [PMID: 33004261 DOI: 10.1016/j.acra.2020.09.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/01/2020] [Accepted: 09/03/2020] [Indexed: 01/04/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate and compare the performance of radiomics in predicting induction chemotherapy response treated with two different regimens in patients with advanced nasopharyngeal carcinoma. MATERIALS AND METHODS A total of 265 patients with pathologically confirmed locally advanced nasopharyngeal carcinoma (stage II-IV), including 115 treated with gemcitabine plus cisplatin (GP group) and 150 treated with docetaxel plus cisplatin (TP group) were retrospectively enrolled. Radiomics features were extracted from the volume of interest delineated in multi-MR sequences on a 3T scanner. After random stratified grouping (training and validation cohorts) and logistic regression based on selected features, the association between the radiomics signature and the early response to induction chemotherapy were established for GP and TP regiments, respectively. RESULTS Clinical factors showed no significant difference between the response and non-response groups for the GP and TP regiments (all p > 0.05). The accuracy of the radiomics signature consisting of selected features from the joint T1, T2, and T1C in the GP group (0.852 in the training cohort vs. 0.853 in the validation cohort) was significantly higher than that in the TP group (0.774 vs 0.727). The overall performance of the GP model was steady, with efficiency to distinguish responders from nonresponders with an AUC reaching 0.907 (95% confidence interval [CI] [0.843-0.970]) in the training cohort and 0.886 (95% CI [0.772-0.998]) in the validation cohort, while leveling at 0.800 (95% CI [0.712-0.888]) in the training cohort and 0.863 (95% CI [0.758-0.967]) in the validation cohort in the TP group. CONCLUSION Pretreatment MR radiomics signature can better predict the early response to IC in the GP regimen than the TP regimen, which may be helpful to guide IC management.
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Affiliation(s)
- Lei Zhang
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China
| | - Zhimin Ye
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiotherapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China
| | - Lei Ruan
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China
| | - Mingxiang Jiang
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China.
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Guan X, Wang S, Kuang P, Lu H, Zhang M, Qian D, Xu X. The Usefulness of Imaging Quantification in Discriminating Non-Calcified Pulmonary Hamartoma From Adenocarcinoma. Front Oncol 2020; 10:568069. [PMID: 33194653 PMCID: PMC7664822 DOI: 10.3389/fonc.2020.568069] [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: 05/31/2020] [Accepted: 08/25/2020] [Indexed: 11/24/2022] Open
Abstract
Background Patients with non-calcified hamartoma were more susceptible to surgery or needle biopsy for the tough discrimination from lung adenocarcinoma. Radiomics have the ability to quantify the lesion features and potentially improve disease diagnosis. Thus, this study aimed to discriminate non-calcified hamartoma from adenocarcinoma by employing imaging quantification and machine learning. Methods Forty-two patients with non-calcified hamartoma and 49 patients with adenocarcinoma were retrospentation; Manual lesion segmentation, feature quantification (e.g., texture features), and artificial neural network were performed consecutively. Independent t-test was used to conduct the inter-group comparisons of those imaging features. Receiver operating characteristic curve was performed to investigate the discriminating efficacy. Results Significantly higher contrast, cluster prominence, cluster shade, dissimilarity, energy, and entropy in non-calcified hamartoma were observed compared with lung adenocarcinoma. Texture-grey-level co-occurrence matrix showed a well discrimination between non-calcified hamartoma and adenocarcinoma as the detection sensitivity, specificity, accuracy, and the area under the curve were 87.22% ± 9.07%, 82.64% ± 8.07%, 85.11% ± 5.40%, and 0.942, respectively. Conclusion Quantifying imaging features is a potentially useful tool for clinical diagnosis. This study demonstrated that non-calcified hamartoma has a heterogeneous distribution of attenuations probably resulting from its complex organizations. Based on this property, imaging quantification could improve discrimination of non-calcified hamartoma from adenocarcinoma.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaoze Wang
- Institute of Very Large Scale Integrated-circuits (VLSI) Design, Zhejiang University, Hangzhou, China
| | - Pingding Kuang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haitong Lu
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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21
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Hu D, Zhen T, Ruan M, Wu L. The value of percentile base on computed tomography histogram in differentiating the invasiveness of adenocarcinoma appearing as pure ground-glass nodules. Medicine (Baltimore) 2020; 99:e23114. [PMID: 33157987 PMCID: PMC7647573 DOI: 10.1097/md.0000000000023114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
To investigate the value of percentile base on computed tomography (CT) histogram analysis for distinguishing invasive adenocarcinoma (IA) from adenocarcinoma in situ (AIS) or micro invasive adenocarcinoma (MIA) appearing as pure ground-glass nodules.A total of 42 cases of pure ground-glass nodules that were surgically resected and pathologically confirmed as lung adenocarcinoma between January 2015 and May 2019 were included. Cases were divided into IA and AIS/MIA in the study. The percentile on CT histogram was compared between the 2 groups. Univariate and multivariate logistic regression were used to determine which factors demonstrated a significant effect on invasiveness. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate the predictive ability of individual characteristics and the combined model.The 4 histogram parameters (25th percentile, 55th percentile, 95th percentile, 97.5th percentile) and the combined model all showed a certain diagnostic value. The combined model demonstrated the best diagnostic performance. The AUC values were as follows: 25th percentile = 0.693, 55th percentile = 0.706, 95th percentile = 0.713, 97.5th percentile = 0.710, and combined model = 0.837 (all P < .05).The percentile of histogram parameters help to improve the ability to radiologically determine the invasiveness of lung adenocarcinoma appearing as pure ground-glass nodules.
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Affiliation(s)
- Dacheng Hu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine
| | - Tao Zhen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine
| | - Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine
| | - Linyu Wu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University
- The First Clinical Medical College of Zhejiang Chinese Medical University, Zhejiang, Hangzhou, China
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22
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Ni Y, Yang Y, Zheng D, Xie Z, Huang H, Wang W. The Invasiveness Classification of Ground-Glass Nodules Using 3D Attention Network and HRCT. J Digit Imaging 2020; 33:1144-1154. [PMID: 32705434 PMCID: PMC7649842 DOI: 10.1007/s10278-020-00355-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The early stage lung cancer often appears as ground-glass nodules (GGNs). The diagnosis of GGN as preinvasive lesion (PIL) or invasive adenocarcinoma (IA) is very important for further treatment planning. This paper proposes an automatic GGNs' invasiveness classification algorithm for the adenocarcinoma. 1431 clinical cases and a total of 1624 GGNs (3-30 mm) were collected from Shanghai Cancer Center for the study. The data is in high-resolution computed tomography (HRCT) format. Firstly, the automatic GGN detector which is composed by a 3D U-Net and a 3D multi-receptive field (multi-RF) network detects the location of GGNs. Then, a deep 3D convolutional neural network (3D-CNN) called Attention-v1 is used to identify the GGNs' invasiveness. The attention mechanism was introduced to the 3D-CNN. This paper conducted a contract experiment to compare the performance of Attention-v1, ResNet, and random forest algorithm. ResNet is one of the most advanced convolutional neural network structures. The competition performance metrics (CPM) of automatic GGN detector reached 0.896. The accuracy, sensitivity, specificity, and area under curve (AUC) value of Attention-v1 structure are 85.2%, 83.7%, 86.3%, and 92.6%. The algorithm proposed in this paper outperforms ResNet and random forest in sensitivity, accuracy, and AUC value. The deep 3D-CNN's classification result is better than traditional machine learning method. Attention mechanism improves 3D-CNN's performance compared with the residual block. The automatic GGN detector with the addition of Attention-v1 can be used to construct the GGN invasiveness classification algorithm to help the patients and doctors in treatment.
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Affiliation(s)
- Yangfan Ni
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, Shanghai, 200083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanyuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, Shanghai, 200083, China
| | - Dezhong Zheng
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, Shanghai, 200083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhe Xie
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, Shanghai, 200083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Haozhe Huang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Weidong Wang
- The General Hospital of the People's Liberation Army, No. 28 Fuxing Road, Haidian District, Beijing, 100039, China.
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23
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Measurement of proptosis using computed tomography based three-dimensional reconstruction software in patients with Graves' orbitopathy. Sci Rep 2020; 10:14554. [PMID: 32883985 PMCID: PMC7471301 DOI: 10.1038/s41598-020-71098-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 08/10/2020] [Indexed: 11/30/2022] Open
Abstract
The evaluation of proptosis is essential for the diagnosis of orbital disease. We have developed a computed tomography (CT)-based three-dimensional (3D) reconstruction software to measure the degree of proptosis. To verify clinical usefulness and reliability, the degree of proptosis was measured in 126 patients with Graves’ orbitopathy (GO) with 3D reconstruction software and compared with those obtained with Hertel exophthalmometer and CT. The proptosis values measured by 3D reconstruction software, CT, and Hertel exophthalmometer were closely related to each other, but showed significant differences (p < 0.001). In contrast, the amount of change in proptosis after orbital decompression were not different among the three measurements (p = 0.153). The intra-observer correlation coefficients of the 3D reconstruction software, CT, and Hertel exophthalmometer measurements were 0.997, 0.942, and 0.953, respectively. In patients with strabismus, the intra-observer correlation coefficient of CT and Hertel exophthalmometer decreased to 0.895 and 0.920, respectively, but the intra-observer correlation coefficient of the 3D reconstruction software did not change to 0.996. The inter-observer correlation coefficients of CT and 3D reconstruction software for three different ophthalmologists were 0.742 and 0.846, respectively. In conclusion, the measurement of proptosis by 3D reconstruction software seems to be a reliable method, especially in the presence of eyeball deviation.
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24
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Xu F, Zhu W, Shen Y, Wang J, Xu R, Qutesh C, Song L, Gan Y, Pu C, Hu H. Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma. Front Oncol 2020; 10:872. [PMID: 32850301 PMCID: PMC7432133 DOI: 10.3389/fonc.2020.00872] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/04/2020] [Indexed: 01/15/2023] Open
Abstract
Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.
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Affiliation(s)
- Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Shen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University, Ningbo, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Rui Xu
- DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China.,DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian, China
| | - Chooah Qutesh
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijiang Song
- Department of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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25
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Yang HH, Lv YL, Fan XH, Ai ZY, Xu XC, Ye B, Hu DZ. Factors distinguishing invasive from pre-invasive adenocarcinoma presenting as pure ground glass pulmonary nodules. Radiat Oncol 2020; 15:186. [PMID: 32736567 PMCID: PMC7393870 DOI: 10.1186/s13014-020-01628-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 07/21/2020] [Indexed: 04/18/2023] Open
Abstract
Background To investigate predictors of pathological invasiveness and prognosis of lung adenocarcinoma in patients with pure ground-glass nodules (pGGNs). Methods Clinical and computed tomography (CT) features of invasive adenocarcinomas (IACs) and pre-IACs were retrospectively compared in 641 consecutive patients with pGGNs and confirmed lung adenocarcinomas who had undergone postoperative CT follow-up. Potential predictors of prognosis were investigated in these patients. Results Of 659 pGGNs in 641 patients, 258 (39.1%) were adenocarcinomas in situ, 265 (40.2%) were minimally invasive adenocarcinomas, and 136 (20.6%) were IACs. Respective optimal cutoffs for age, serum carcinoembryonic antigen concentration, maximal diameter, mean diameter, and CT density for distinguishing pre-IACs from IACs were 53 years, 2.19 ng/mL, 10.78 mm, 10.09 mm, and − 582.28 Hounsfield units (HU). Univariable analysis indicated that sex, age, maximal diameter, mean diameter, CT density, and spiculation were significant predictors of lung IAC. In multivariable analysis age, maximal diameter, and CT density were significant predictors of lung IAC. During a median follow-up of 41 months no pGGN IACs recurred. Conclusions pGGNs may be lung IACs, especially in patients aged > 55 years with lesions that are > 1 cm in diameter and exhibit CT density > − 600 HU. pGGN IACs of < 3 cm in diameter have good post-resection prognoses.
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Affiliation(s)
- Huan-Huan Yang
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.,Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Yi-Lv Lv
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China
| | - Xing-Hai Fan
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.,Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Zhi-Yong Ai
- Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Xiu-Chun Xu
- Department of Respiratory Medicine, Shanghai Zhongye Hospital, Shanghai, 200941, China
| | - Bo Ye
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.
| | - Ding-Zhong Hu
- Department of ThoracicSurgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.
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26
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Hu X, Ye W, Li Z, Chen C, Cheng S, Lv X, Weng W, Li J, Weng Q, Pang P, Xu M, Chen M, Ji J. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis. Br J Radiol 2020; 93:20190762. [PMID: 32686958 DOI: 10.1259/bjr.20190762] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVES To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis. METHODS A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets. RESULTS Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively. CONCLUSION The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm). ADVANCES IN KNOWLEDGE Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.
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Affiliation(s)
- Xianghua Hu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Weichuan Ye
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Zhongxue Li
- Department of Radiology, Fuyuan Hospital of Yiwu, Jinhua 321000, China
| | - Chunmiao Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Shimiao Cheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Xiuling Lv
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Wei Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Jie Li
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | | | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.,Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China
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Lee JH, Park CM. Differentiation of persistent pulmonary subsolid nodules with a solid component smaller than 6 mm: to be invasive adenocarcinoma or not to be? J Thorac Dis 2020; 12:1754-1757. [PMID: 32642078 PMCID: PMC7330336 DOI: 10.21037/jtd-20-1645] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Jong Hyuk Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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28
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Lee JH, Lim WH, Hong JH, Nam JG, Hwang EJ, Kim H, Goo JM, Park CM. Growth and Clinical Impact of 6-mm or Larger Subsolid Nodules after 5 Years of Stability at Chest CT. Radiology 2020; 295:448-455. [PMID: 32181731 DOI: 10.1148/radiol.2020191921] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background It remains unclear whether 5 years of stability is sufficient to establish the benign behavior of subsolid nodules (SSNs) of the lung. There are no guidelines for the length of follow-up needed for these SSNs. Purpose To investigate the incidence of interval growth of pulmonary SSNs 6 mm or greater in diameter after 5 years of stability and their clinical outcome. Materials and Methods This retrospective study assessed SSNs 6 mm or greater that were stable for 5 years after detection (January 2002 to December 2018). The incidence of interval growth after 5 years of stability and the clinical and radiologic features of these SSNs were investigated. Clinical stage shifts of growing SSNs, presence of metastasis, and overall survival were assessed during the follow-up period. Subgroup analysis was performed in patients with nonenhanced thin-section (section thickness ≤1.5 mm) CT for interval growth after 5 years of stability. Results A total of 235 SSNs in 235 patients (mean age, 64 years ± 10 [standard deviation]; 132 women) were evaluated. There were 212 pure ground-glass nodules and 24 part-solid nodules. During follow-up (median, 112 months; range, 84-208 months), five of the 235 SSNs (2%; three primary ground-glass nodules and two part-solid nodules) showed interval growth. Three of these five growing SSNs were 10 mm or greater. Three of the five SSNs with interval growth had clinical stage shifts after growth (from Tis [in situ] to T1mi [minimally invasive] in one lesion; from T1mi to T1a in two lesions). There were no deaths or metastases from lung cancer during follow-up. Of 160 SSNs imaged with section thickness of 1.5 mm or less, two (1%) grew; both lesions were 10 mm or greater. Conclusion Only 2% of subsolid pulmonary nodules greater than or equal to 6 mm that had been stable for 5 years showed subsequent growth. At median follow-up of 9 years (after the initial 5-year period of stability), growth of those lung nodules had no clinical effect. © RSNA, 2020 See also the editorial by Naidich and Azour in this issue.
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Affiliation(s)
- Jong Hyuk Lee
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Woo Hyeon Lim
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Jung Hee Hong
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Ju Gang Nam
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Eui Jin Hwang
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Hyungjin Kim
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Jin Mo Goo
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
| | - Chang Min Park
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., W.H.L., J.H.H., J.G.N., E.J.H., H.K., J.M.G., C.M.P.); and Armed Forces Seoul District Hospital, Seoul, Korea (J.H.L.)
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Sun Y, Li C, Jin L, Gao P, Zhao W, Ma W, Tan M, Wu W, Duan S, Shan Y, Li M. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. Eur Radiol 2020; 30:3650-3659. [PMID: 32162003 PMCID: PMC7305264 DOI: 10.1007/s00330-020-06776-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/14/2019] [Accepted: 02/24/2020] [Indexed: 12/18/2022]
Abstract
Objectives To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic–radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. Results Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic–radiomics model (AUC 0.77; 95% CI, 0.69–0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62–0.81) and Rad-score (AUC 0.72; 95% CI, 0.63–0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. Conclusions The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. Key Points • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic–radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up. Electronic supplementary material The online version of this article (10.1007/s00330-020-06776-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Cheng Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Wei Zhao
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China.,Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Weilan Wu
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | | | - Yuqing Shan
- Department of Radiology, The People's Hospital of Rizhao, Rizhao City, 276800, China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, China. .,Diagnosis and Treatment Center of Small Lung Nodules, Huadong Hospital, Shanghai, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
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Differences in lung cancer characteristics and mortality rate between screened and non-screened cohorts. Sci Rep 2019; 9:19386. [PMID: 31852960 PMCID: PMC6920422 DOI: 10.1038/s41598-019-56025-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 12/05/2019] [Indexed: 12/17/2022] Open
Abstract
Screening programs for lung cancer aim to allow diagnosis at the early stage, and therefore the decline in mortality rates. Thus, the aim of this retrospective cohort study was to the comparison of screened and non-screened lung cancer in terms of lung cancer characteristics, overdiagnosis and survival rate. A retrospective study in which 2883 patients with 2883 lung cancer diagnosed according to the hospital-based lung cancer register database between 2007 and 2017. A comparison was performed in term of clinical characteristics and outcomes of lung cancer between the screened and non-screening patient groups. 2883 subjects were identified (93 screened and 2790 non-screened). Screened group patients were younger (59.91 ± 8.14 versus 67.58 ± 12.95; p < 0.0001), and were more likely to be female than non-screened group (61.3% versus 36.8%; p < 0.0001). The screened group showed significantly better outcomes in overall mortality than the non-screened group (10.75% versus 79.06%; <0.0001). In a Cox proportional hazard model, lung cancer in the screened group proved to be an independent prognostic factor in lung cancer subjects. Our findings point to the improved survival outcome in the screened group and might underline the benefit of low-dose computed tomography (LDCT) screening program in Asian populations with the high prevalence of non-smoking-related lung cancer. Further study aimed at the LDCT mass screening program targeting at light smokers and non-smoker outside of existing screening criteria is warranted.
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Abstract
OBJECTIVE. The purpose of this study was to explore the value of FDG PET combined with high-resolution CT (HRCT) in predicting the pathologic subtypes and growth patterns of early lung adenocarcinoma. MATERIALS AND METHODS. A retrospective analysis was conducted on the PET/CT data on ground-glass nodules (GGNs) resected from patients with stage IA lung adenocarcinoma. The efficacy of PET maximum standardized uptake value (SUVmax) combined with HRCT signs in prediction of histopathologic subtype and growth pattern of lung adeno-carcinoma was evaluated. RESULTS. SUVmax was significantly higher in GGNs with invasive HRCT signs. The diameter of GGN (odds ratio, 1.660; p = 0.000) and the difference in attenuation value (odds ratio, 1.012; p = 0.011) between ground-glass components and adjacent lung tissues were independent predictors of FDG uptake by GGNs. SUVmax was higher in invasive adenocarcinoma than in adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) (median SUVmax, 2.0 vs 1.1; p = 0.008). An SUVmax of 2.0 was the optimal cutoff value for differentiating invasive adenocarcinoma from AIS-MIA. Acinar-papillary adenocarcinoma had a higher SUVmax than lepidic adenocarcinoma (median SUVmax, 2.1 vs 1.3; p = 0.037). An SUVmax of 1.4 was the optimal cutoff value for differentiating the growth pattern of adenocarcinoma. Use of PET/CT with HRCT significantly improved efficacy for differentiating invasive adeno-carcinoma from AIS-MIA. However, use of HRCT cannot significantly improve the diagnostic efficacy of FDG PET in the evaluation of tumors with different growth patterns. CONCLUSION. FDG PET can be used to predict the histopathologic subtypes and growth patterns of early lung adenocarcinoma. Combined with HRCT, it has value for predicting invasive histopathologic subtypes but no significance for predicting invasive growth patterns.
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Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules. J Comput Assist Tomogr 2019; 43:817-824. [PMID: 31343995 DOI: 10.1097/rct.0000000000000889] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The aim of this study was to investigate the differentiation of computed tomography (CT)-based entropy parameters between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions appearing as pulmonary subsolid nodules (SSNs). METHODS This study was approved by the institutional review board in our hospital. From July 2015 to November 2018, 186 consecutive patients with solitary peripheral pulmonary SSNs that were pathologically confirmed as pulmonary adenocarcinomas (74 MIA and 112 IAC lesions) were included and subdivided into the training data set and the validation data set. Chest CT scans without contrast enhancement were performed in all patients preoperatively. The subjective CT features of the SSNs were reviewed and compared between the MIA and IAC groups. Each SSN was semisegmented with our in-house software, and entropy-related parameters were quantitatively extracted using another in-house software developed in the MATLAB platform. Logistic regression analysis and receiver operating characteristic analysis were performed to evaluate the diagnostic performances. Three diagnostic models including subjective model, entropy model, and combined model were built and analyzed using area under the curve (AUC) analysis. RESULTS There were 119 nonsolid nodules and 67 part-solid nodules. Significant differences were found in the subjective CT features among nodule type, lesion size, lobulated shape, and irregular margin between the MIA and IAC groups. Multivariate analysis revealed that part-solid type and lobulated shape were significant independent factors for IAC (P < 0.0001 and P < 0.0001, respectively). Three entropy parameters including Entropy-0.8, Entropy-2.0-32, and Entropy-2.0-64 were identified as independent risk factors for the differentiation of MIA and IAC lesions. The median entropy model value of the MIA group was 0.266 (range, 0.174-0.590), which was significantly lower than the IAC group with value 0.815 (range, 0.623-0.901) (P < 0.0001). Multivariate analysis revealed that the combined model had an excellent diagnostic performance with sensitivity of 88.2%, specificity of 73.0%, and accuracy of 82.1%. The AUC value of the combined model was significantly higher (AUC, 0.869) than that of the subjective model (AUC, 0.809) or the entropy model alone (AUC, 0.836) (P < 0.0001). CONCLUSIONS The CT-based entropy parameters could help assess the aggressiveness of pulmonary adenocarcinoma via quantitative analysis of intratumoral heterogeneity. The MIA can be differentiated from IAC accurately by using entropy-related parameters in peripheral pulmonary SSNs.
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Yang Y, Wang WW, Ren Y, Jin XQ, Zhu QD, Peng CT, Liu HQ, Zhang JH. Computerized texture analysis predicts histological invasiveness within lung adenocarcinoma manifesting as pure ground-glass nodules. Acta Radiol 2019; 60:1258-1264. [PMID: 30818977 DOI: 10.1177/0284185119826536] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Yang Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Wei-Wei Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Xian-Qiao Jin
- Department of Respiration, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Quan-Dong Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Cheng-Tao Peng
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, PR China
| | - Han-Qiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, PR China
- Academy for Engineering and Technology, Fudan University, Shanghai, PR China
| | - Jun-Hai Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
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Kay FU, Oz OK, Abbara S, Mortani Barbosa EJ, Agarwal PP, Rajiah P. Translation of Quantitative Imaging Biomarkers into Clinical Chest CT. Radiographics 2019; 39:957-976. [PMID: 31199712 DOI: 10.1148/rg.2019180168] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Quantitative imaging has been proposed as the next frontier in radiology as part of an effort to improve patient care through precision medicine. In 2007, the Radiological Society of North America launched the Quantitative Imaging Biomarkers Alliance (QIBA), an initiative aimed at improving the value and practicality of quantitative imaging biomarkers by reducing variability across devices, sites, patients, and time. Chest CT occupies a strategic position in this initiative because it is one of the most frequently used imaging modalities, anatomically encompassing the leading causes of mortality worldwide. To date, QIBA has worked on profiles focused on the accurate, reproducible, and meaningful use of volumetric measurements of lung lesions in chest CT. However, other quantitative methods are on the verge of translation from research grounds into clinical practice, including (a) assessment of parenchymal and airway changes in patients with chronic obstructive pulmonary disease, (b) analysis of perfusion with dual-energy CT biomarkers, and (c) opportunistic screening for coronary atherosclerosis and low bone mass by using chest CT examinations performed for other indications. The rationale for and the key facts related to the application of these quantitative imaging biomarkers in cardiothoracic chest CT are presented. ©RSNA, 2019 See discussion on this article by Buckler (pp 977-980).
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Affiliation(s)
- Fernando U Kay
- From the Department of Radiology, Cardiothoracic Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room E6.122H, Dallas, TX 75390-9316 (F.U.K., O.K.O., S.A., P.R.); the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.J.M.B.); and the Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (P.P.A.)
| | - Orhan K Oz
- From the Department of Radiology, Cardiothoracic Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room E6.122H, Dallas, TX 75390-9316 (F.U.K., O.K.O., S.A., P.R.); the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.J.M.B.); and the Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (P.P.A.)
| | - Suhny Abbara
- From the Department of Radiology, Cardiothoracic Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room E6.122H, Dallas, TX 75390-9316 (F.U.K., O.K.O., S.A., P.R.); the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.J.M.B.); and the Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (P.P.A.)
| | - Eduardo J Mortani Barbosa
- From the Department of Radiology, Cardiothoracic Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room E6.122H, Dallas, TX 75390-9316 (F.U.K., O.K.O., S.A., P.R.); the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.J.M.B.); and the Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (P.P.A.)
| | - Prachi P Agarwal
- From the Department of Radiology, Cardiothoracic Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room E6.122H, Dallas, TX 75390-9316 (F.U.K., O.K.O., S.A., P.R.); the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.J.M.B.); and the Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (P.P.A.)
| | - Prabhakar Rajiah
- From the Department of Radiology, Cardiothoracic Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room E6.122H, Dallas, TX 75390-9316 (F.U.K., O.K.O., S.A., P.R.); the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (E.J.M.B.); and the Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (P.P.A.)
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Digumarthy SR, Padole AM, Rastogi S, Price M, Mooradian MJ, Sequist LV, Kalra MK. Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? Cancer Imaging 2019; 19:36. [PMID: 31182167 PMCID: PMC6558852 DOI: 10.1186/s40644-019-0223-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 05/30/2019] [Indexed: 01/17/2023] Open
Abstract
Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Materials and methods The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. Results Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). Conclusions Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.
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Affiliation(s)
- Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. .,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Court, Suite 236, Boston, MA, 02114, USA.
| | - Atul M Padole
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Shivam Rastogi
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Melissa Price
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Meghan J Mooradian
- Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Lecia V Sequist
- Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
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Invasive Pulmonary Adenocarcinomas Versus Preinvasive Lesions Appearing as Pure Ground-Glass Nodules: Differentiation Using Enhanced Dual-Source Dual-Energy CT. AJR Am J Roentgenol 2019; 213:W114-W122. [PMID: 31082273 DOI: 10.2214/ajr.19.21245] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE. The objective of our study was to investigate the potentials of enhanced dual-source dual-energy CT (DECT) and three-planar measurements for differentiating invasive pulmonary adenocarcinomas (IPAs) from preinvasive lesions appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS. Thirty-nine patients with 53 pGGNs who underwent enhanced dual-source DECT were included in this retrospective study. All pGGNs were pathologically confirmed and categorized into two groups: preinvasive lesions or IPAs. The traditional CT features of the pGGNs were evaluated on unenhanced images. Quantitative parameters were measured on iodine-enhanced images of dual-source DECT in three planes, and both intra- and interobserver reproducibility analyses were performed to assess the measurement reproducibility of quantitative parameters. To identify significant factors for differentiating IPAs from preinvasive lesions, we performed logistic regression analysis and ROC curve analysis. RESULTS. For traditional CT features, only lesion size and unenhanced CT attenuation value showed significant differences between preinvasive lesions and IPAs (p < 0.05). Preinvasive lesions and IPAs exhibited significant differences in attenuation on virtual images, so-called "virtual HU" or "VHU," and the modified normalized iodine concentration (NIC) (p < 0.05), and both intra- and interobserver agreement for the quantitative measurements were excellent. Multivariate logistic regression analysis revealed that larger lesion size (adjusted odds ratio [OR], 3.65) and higher modified NIC (adjusted OR, 19.01) were significant differentiators of IPAs from preinvasive lesions (p < 0.05). ROC curve analysis revealed that modified NIC showed excellent performance (AUC, 0.924) and significantly higher performance than lesion size (AUC, 0.711) for differentiating IPAs from preinvasive lesions. CONCLUSION. In pGGNs, a lesion with a modified NIC value of more than 0.29 can be a very specific discriminator of IPAs from preinvasive lesions, and IPAs can be accurately and reliably differentiated from preinvasive lesions using enhanced dual-source DECT and three-planar measurements.
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Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules. Sci Rep 2019; 9:6009. [PMID: 30979926 PMCID: PMC6461662 DOI: 10.1038/s41598-019-42340-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 03/27/2019] [Indexed: 02/07/2023] Open
Abstract
109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.
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Effect of CT Reconstruction Algorithm on the Diagnostic Performance of Radiomics Models: A Task-Based Approach for Pulmonary Subsolid Nodules. AJR Am J Roentgenol 2019; 212:505-512. [DOI: 10.2214/ajr.18.20018] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Zhang T, Pu XH, Yuan M, Zhong Y, Li H, Wu JF, Yu TF. Histogram analysis combined with morphological characteristics to discriminate adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma appearing as pure ground-glass nodule. Eur J Radiol 2019; 113:238-244. [PMID: 30927953 DOI: 10.1016/j.ejrad.2019.02.034] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 01/10/2019] [Accepted: 02/25/2019] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To construct a predictive model to discriminate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs) using computed tomography (CT) histogram analysis combined with morphological characteristics and to evaluate its diagnostic performance. MATERIALS AND METHODS Two hundred eighty-nine patients with surgically resected solitary pGGN and pathologically diagnosed with AIS, MIA, or IAC in our institution from January 2014 to May 2018 were enrolled in our study. Two hundred twenty-six pGGNs (79 AIS, 84 MIA, and 63 IAC) were randomly selected and assigned to a model-development cohort, and the remaining 63 pGGNs (11 AIS, 29 MIA and 23 IAC) were assigned to a validation cohort. The morphological characteristics were established as model A and histogram parameters as model B. The diagnostic performances of model A, model B, and model A + B were evaluated and compared via receiver operating curve (ROC) analysis and logistic regression analysis. RESULTS Entropy (odd ratio [OR] = 23.25, 95%CI: 6.83-79.15, p < 0.001), microvascular sign (OR = 8.62, 95%CI: 3.72-19.98, p < 0.001) and the maximum diameter (OR = 4.37, 95%CI: 2.44-7.84, p < 0.001) were identified as independent predictors in the IAC group. The area under the ROC (Az value), accuracy, sensitivity and specificity of model A + B were 0.896, 88.1%, 79.4% and 91.4%, respectively, exhibiting a significantly higher Az value than either model A or model B alone (0.785 vs 0.896, p < 0.001; 0.849 vs 0.896, p = 0.029). Model A + B also conveyed a good diagnostic performance in the validation cohort, with an Az value of 0.851. CONCLUSION Histogram analysis combined with morphological characteristics exhibit a superior diagnostic performance in discriminating AIS-MIA from IAC appearing as pGGNs.
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Affiliation(s)
- Teng Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Xue-Hui Pu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Yan Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | | | - Tong-Fu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Shi Z, Deng J, She Y, Zhang L, Ren Y, Sun W, Su H, Dai C, Jiang G, Sun X, Xie D, Chen C. Quantitative features can predict further growth of persistent pure ground-glass nodule. Quant Imaging Med Surg 2019; 9:283-291. [PMID: 30976552 DOI: 10.21037/qims.2019.01.04] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background To evaluate whether quantitative features of persistent pure ground-glass nodules (PGGN) on the initial computed tomography (CT) scans can predict further nodule growth. Methods This retrospective study included 59 patients with 101 PGGNs from 2011 to 2012, who received regular CT follow-up for lung nodule surveillance. Nineteen quantitative image features consisting of 8 volumetric and 11 histogram parameters were calculated to detect lung nodule growth. For the extraction of the quantitative features, semi-automatic GrowCut segmentation was implemented on chest CT images in 3D slicer platform. Univariate and multivariate analyses were performed to identify risk factors for nodule growth. Results With a median follow-up of 52 months, nodule growth was detected in 10 nodules by radiological assessment and in 16 nodules by quantitative features. In univariate analysis, 3D maximum diameter (MD), volume, mass, surface area, 90% percentile, and standard deviation value (SD) of PGGN on the initial CT scan were significantly different between stable nodules and nodules with further growth. In multivariate analysis, MD [hazard ratio (HR), 3.75; 95% confidence interval (CI), 2.14-6.55] and SD (HR, 2.06; 95% CI, 1.35-3.14) were independent predictors of further nodule growth. Also, the area under the curve was 0.896 (95% CI: 0.820-0.948) and 0.813 (95% CI: 0.723-0.883) for MD with a cut-off value of 10.2mm and SD of 50.0 Hounsfield Unit (HU). Besides, the growth rate was 55.6% (n=15) of PGGNs with MD >10.2 mm and SD >50.0 HU. Conclusions Based on the initial CT scan, the quantitative features can predict PGGN growth more precisely. PGGN with MD >10.2 mm and SD >50.0 HU may require close follow-up or surgical intervention for the high incidence of growth.
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Affiliation(s)
- Zhe Shi
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Weiyan Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Hang Su
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Chenyang Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China
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Liu H, Jing B, Han W, Long Z, Mo X, Li H. A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma. J Med Syst 2019; 43:59. [PMID: 30707369 DOI: 10.1007/s10916-019-1175-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 01/21/2019] [Indexed: 12/22/2022]
Abstract
The purpose of the study was to compare the texture based discriminative performances between non-contrast enhanced computed tomography (NECT) and contrast-enhanced computed tomography (CECT) images in differentiating lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) patients. Eighty-seven lung cancer subjects were enrolled in the study, including pathologically proved 47 ADC patients and 40 SCC patients, and 261 texture features were extracted from the manually delineated region of interests on CECT and NECT images respectively. Fisher score was then used to select the effective discriminative texture features between groups, and the selected texture features were adopted to differentiate ADC from SCC using Support Vector Machine and Leave-one-out cross-validation. Both NECT and CECT images could achieve the same best classification accuracy of 95.4%, and most of the informative features were from the gray-level co-occurrence matrix. In addition, CECT images were found with enhanced texture features compared with NECT images, and combining texture features of CECT and NECT images together could further improve the prediction accuracy. Besides the texture feature, the tumor location information also contributed to the differential diagnosis between ADC and SCC.
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Affiliation(s)
- Han Liu
- School of Biomedical Engineering, Capital Medical University, Fengtai District, Beijing, 100069, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Fengtai District, Beijing, 100069, China
| | - Wenjuan Han
- Department of Radiology, the General Hospital of Chinese People's Armed Police Forces, Beijing, 100039, China
| | - Zhuqing Long
- School of Biomedical Engineering, Capital Medical University, Fengtai District, Beijing, 100069, China
| | - Xiao Mo
- School of Biomedical Engineering, Capital Medical University, Fengtai District, Beijing, 100069, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Fengtai District, Beijing, 100069, China.
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Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules. Eur J Radiol 2019; 112:161-168. [PMID: 30777206 DOI: 10.1016/j.ejrad.2019.01.021] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 11/20/2022]
Abstract
The aim of the present study was to develop and validate a radiomics-based nomogram for differentiation of pre-invasive lesions from invasive lesions that appearing as ground-glass opacity nodules (GGNs) ≤10 mm (sub-centimeter) in diameter at CT. A total of 542 consecutive patients with 626 pathologically confirmed pulmonary subcentimeter GGNs were retrospectively studied from October 2011 to September 2017. All the GGNs were divided into a training set (n = 334) and a validation set (n = 292). Researchers extracted 475 radiomics features from the plain CT images; a radiomics signature was constructed with the least absolute shrinkage and selection operator (LASSO) based on multivariable regression in the training set. Based on the multivariable logistic regression model, a radiomics nomogram was developed in the training set. The performance of the nomogram was evaluated with respect to its calibration, discrimination, and clinical-utility and this was assessed in the validation set. The constructed radiomics signature, which consisted of 15 radiomics features, was significantly associated with the invasiveness of subcentimeter GGNs (P < 0.0001 for both training set and validation set). To build the nomogram model, radiomics signature and mean CT value were used. The nomogram model demonstrated good discrimination and calibration in both training set (C-index, 0.716 [95% CI, 0.632 to 0.801]) and validation set (C-index, 0.707 [95% CI, 0.625 to 0.788]). Decision curve analysis (DCA) indicated that radiomics-based nomogram was clinically useful. A radiomics-based nomogram that incorporates both radiomics signature and mean CT value is constructed in the study, which can be conveniently used to facilitate the preoperative individualized prediction of the invasiveness in patients with subcentimeter GGNs.
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Mannil M, Burgstaller JM, Thanabalasingam A, Winklhofer S, Betz M, Held U, Guggenberger R. Texture analysis of paraspinal musculature in MRI of the lumbar spine: analysis of the lumbar stenosis outcome study (LSOS) data. Skeletal Radiol 2018; 47:947-954. [PMID: 29497775 DOI: 10.1007/s00256-018-2919-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 02/12/2018] [Accepted: 02/16/2018] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To evaluate association of fatty infiltration in paraspinal musculature with clinical outcomes in patients suffering from lumbar spinal stenosis (LSS) using qualitative and quantitative grading in magnetic resonance imaging (MRI). MATERIALS AND METHODS In this retrospective study, texture analysis (TA) was performed on postprocessed axial T2 weighted (w) MR images at level L3/4 using dedicated software (MaZda) in 62 patients with LSS. Associations in fatty infiltration between qualitative Goutallier and quantitative TA findings with two clinical outcome measures, Spinal stenosis measure (SSM) score and walking distance, at baseline and regarding change over time were assessed using machine learning algorithms and multiple logistic regression models. RESULTS Quantitative assessment of fatty infiltration using the histogram TA feature "mean" showed higher interreader reliability (ICC 0.83-0.97) compared to the Goutallier staging (κ = 0.69-0.93). No correlation between Goutallier staging and clinical outcome measures was observed. Among 151 TA features, only TA feature "mean" of the spinotransverse group showed a significant but weak correlation with worsened SSM (p = 0.046). TA feature "S(3,3) entropy" showed a significant but weak association with worsened WD over 12 months (p = 0.046). CONCLUSION MR TA is a reproducible tool to quantitatively assess paraspinal fatty infiltration, but there is no clear association with the clinical outcome in asymptomatic LSS patients.
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Affiliation(s)
- Manoj Mannil
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland
| | - Jakob M Burgstaller
- Horten Centre for Patient Oriented Research and Knowledge Transfer, University of Zurich, Pestalozzistrasse 24, 8032, Zurich, Switzerland
| | - Arjun Thanabalasingam
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland
| | - Sebastian Winklhofer
- Department of Neuroradiology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Michael Betz
- Department of Orthopaedics, Balgrist University Hospital University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland
| | - Ulrike Held
- Horten Centre for Patient Oriented Research and Knowledge Transfer, University of Zurich, Pestalozzistrasse 24, 8032, Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland.
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She Y, Zhang L, Zhu H, Dai C, Xie D, Xie H, Zhang W, Zhao L, Zou L, Fei K, Sun X, Chen C. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol 2018; 28:5121-5128. [PMID: 29869172 DOI: 10.1007/s00330-018-5509-9] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 03/19/2018] [Accepted: 04/20/2018] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are assumed to be indolent lung adenocarcinoma with excellent prognosis. We aim to identify these lesions from invasive adenocarcinoma (IA) by a radiomics approach. METHODS This retrospective study was approved by institutional review board with a waiver of informed consent. Pathologically confirmed lung adenocarcinomas manifested as lung nodules less than 3 cm were retrospectively identified. In-house software was used to quantitatively extract 60 CT-based radiomics features quantifying nodule's volume, intensity and texture property through manual segmentation. In order to differentiate AIS/MIA from IA, least absolute shrinkage and selection operator (LASSO) logistic regression was used for feature selection and developing radiomics signatures. The predictive performance of the signature was evaluated via receiver operating curve (ROC) and calibration curve, and validated using an independent cohort. RESULTS 402 eligible patients were included and divided into the primary cohort (n = 207) and the validation cohort (n = 195). Using the primary cohort, we developed a radiomics signature based on five radiomics features. The signature showed good discrimination between MIA/AIS and IA in both the primary and validation cohort, with AUCs of 0.95 (95% CI, 0.91-0.98) and 0.89 (95% CI, 0.84-0.93), respectively. Multivariate logistic analysis revealed that the signature (OR, 13.3; 95% CI, 6.2-28.5; p < 0.001) and gender (OR, 3.5; 95% CI, 1.2-10.9; p = 0.03) were independent predictors of indolent lung adenocarcinoma. CONCLUSION The signature based on radiomics features helps to differentiate indolent from invasive lung adenocarcinoma, which might be useful in guiding the intervention choice for patients with pulmonary nodules. KEY POINTS • Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.
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Affiliation(s)
- Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China
| | - Huiyuan Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China
| | - Chenyang Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Wei Zhang
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Lilan Zhao
- Department of General Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Liling Zou
- Department of Medical Statistics, Tongji university School of Medicine, Shanghai, People's Republic of China.,Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY, USA
| | - Ke Fei
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200433, People's Republic of China.
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Wang S, Wang R, Zhang S, Li R, Fu Y, Sun X, Li Y, Sun X, Jiang X, Guo X, Zhou X, Chang J, Peng W. 3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT. Quant Imaging Med Surg 2018; 8:491-499. [PMID: 30050783 DOI: 10.21037/qims.2018.06.03] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background Identification of pre-invasive lesions (PILs) and invasive adenocarcinomas (IACs) can facilitate treatment selection. This study aimed to develop an automatic classification framework based on a 3D convolutional neural network (CNN) to distinguish different types of lung cancer using computed tomography (CT) data. Methods The CT data of 1,545 patients suffering from pre-invasive or invasive lung cancer were collected from Fudan University Shanghai Cancer Center. All of the data were preprocessed through lung mask extraction and 3D reconstruction to adapt to different imaging scanners or protocols. The general flow for the classification framework consisted of nodule detection and cancer classification. The performance of our classification algorithm was evaluated using a receiver operating characteristic (ROC) analysis, with diagnostic results from three experienced radiologists. Results The sensitivity, specificity, accuracy, and AUC (area under the ROC curve) values of our proposed automatic classification method were 88.5%, 80.1%, 84.0%, and 89.2%, respectively. The results of the CNN classification method were compared to those of three experienced radiologists. The AUC value of our method (AUC =0.892) was higher than those of all radiologists (radiologist 1: 80.5%; radiologist 2: 83.9%; and radiologist 3: 86.7%). Conclusions The 3D CNN-based classification algorithm is a promising tool for the diagnosis of pre-invasive and invasive lung cancer and for the treatment choice decision.
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Affiliation(s)
- Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Rui Wang
- Tencent Youtu Lab, Shanghai 200050, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ruimin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Fu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xiangjie Sun
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.,Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Yuan Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.,Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Xing Sun
- Tencent Youtu Lab, Shanghai 200050, China
| | | | | | - Xuan Zhou
- Tencent Youtu Lab, Shanghai 200050, China
| | - Jia Chang
- Tencent Youtu Lab, Shanghai 200050, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
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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.
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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
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Gu Y, She Y, Xie D, Dai C, Ren Y, Fan Z, Zhu H, Sun X, Xie H, Jiang G, Chen C. A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma. Ann Thorac Surg 2018; 106:214-220. [PMID: 29550204 DOI: 10.1016/j.athoracsur.2018.02.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 02/02/2018] [Accepted: 02/12/2018] [Indexed: 10/17/2022]
Abstract
BACKGROUND Some clinical N0 lung adenocarcinomas have been pathologically diagnosed as N1 or N2. To improve the preoperative diagnostic accuracy of lymph node disease, we developed a prediction model for lymph node metastasis in cT1 N0 M0 lung adenocarcinoma based on computed tomography texture analysis and clinical characteristics to estimate the probability of lymph node metastasis. METHODS The records of 501 consecutive patients with cT1 N0 M0 lung adenocarcinoma who underwent computed tomography scan and pulmonary resection with systematic lymph nodes dissection or lymph nodes sampling were reviewed. Each nodule was manually segmented, and its computerized texture features were extracted. Multivariate logistic regression with fivefold validation was used to estimate independent predictors and build the prediction model. The prediction model was then externally validated. A nomogram was developed based on logistic regression results. RESULTS Among 501 patients, 41 were diagnosed with positive lymph nodes (8.18%). Four independent predictors were identified: the skewness and 90th percentile of computed tomography number, nodule compactness, and carcinoembryonic antigen level. This model showed good calibration (Hosmer-Lemeshow test, p = 0.337), with an area under the curve of 0.883 (95% confidence interval, 0.842 to 0.924; p < 0.001). The area under the curve was 0.808 (95% confidence interval, 0.735 to 0.880) when validated with independent data. CONCLUSIONS A model based on computerized textures and carcinoembryonic antigen level can assess the lymph node status of patients with cT1 N0 M0 lung adenocarcinoma preoperatively, which could assist surgeons in making subsequent clinical decisions.
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Affiliation(s)
- Yawei Gu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chenyang Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Ziwen Fan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Huiyuan Zhu
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
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HRCT texture analysis for pure or part-solid ground-glass nodules: distinguishability of adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma. Jpn J Radiol 2017; 36:113-121. [PMID: 29273964 DOI: 10.1007/s11604-017-0711-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 11/23/2017] [Indexed: 12/23/2022]
Abstract
PURPOSE To distinguish between adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) showing pure or part-solid ground-glass nodules (GGNs) by high-resolution computed tomography (HRCT) texture analysis. MATERIALS AND METHODS This retrospective study included 101 consecutive patients with 115 pure or part-solid GGNs ≤ 3 cm diameter, which were surgically resected and pathologically diagnosed with AIS, MIA, or IAC (48 AIS-MIA and 67 IAC) between April 2011 and March 2015. Each tumor was manually segmented on axial CT images, and the following texture features were calculated: volume, mass, mean CT value, variance, skewness, kurtosis, entropy, uniformity, and percentile CT numbers (10th, 25th, 50th, 75th, 90th, 95th percentiles). The differences between AIS-MIA and IAC were statistically evaluated using univariate, multivariate, and receiver operating characteristic analysis. RESULTS Compared with IAC, AIS-MIA had significantly greater skewness, kurtosis, and uniformity, whereas in the other parameters, AIS-MIA demonstrated significantly lower values than those of IAC. Multivariate analysis revealed that independent differentiators were the 90th percentile CT numbers (P < 0.001) and entropy (P = 0.005) with an excellent accuracy (area under the curve, 0.90). CONCLUSIONS The 90th percentile CT numbers and entropy can accurately distinguish AIS-MIA from IAC.
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