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Wang D, Shang Z, Chen R, Yang Y, Su Y, Jia P, Liu Y, Yang F. Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study. Medicine (Baltimore) 2024; 103:e39683. [PMID: 39312368 PMCID: PMC11419497 DOI: 10.1097/md.0000000000039683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 08/23/2024] [Indexed: 09/25/2024] Open
Abstract
To explore the feasibility and application value of texture analysis based on computed tomography (CT) for predicting the differentiation of esophageal squamous cell carcinoma (ESCC). Patients diagnosed with ESCC who underwent chest contrast-enhanced CT before treatment were selected. Based on the pathological results, the patients were stratified into poorly differentiated and moderately well-differentiated groups. FireVoxel software was used to analyze the region of interest based on venous phase CT images. Texture parameters including the mean, median, standard deviation (SD), inhomogeneity, skewness, kurtosis, and entropy were obtained automatically. Differences in the texture parameters and their relationship with the degree of differentiation between the 2 groups were analyzed. The value of CT texture parameters in identifying poor differentiation and moderate-well differentiation of esophageal cancer was analyzed using the ROC curve. A total of 48 patients with ESCC were included, including 24 patients in the poorly differentiated group and 24 patients in the moderate-well-differentiated group. There were negative correlations between SD, inhomogeneity, entropy, and the degree of differentiation of esophageal cancer (P < .05). The correlation of inhomogeneity was the highest (r = -0.505, P < .001). SD, inhomogeneity, and entropy could effectively distinguish between the poorly and moderately well-differentiated groups, with statistically significant differences between the 2 groups (P < .05). The best critical values for SD, inhomogeneity, and entropy were 17.538, 0.017, and 3.917, respectively. The areas under the ROC curve were 0.793, 0.792, and 0.729, respectively, with the SD and inhomogeneity being the best. The application of texture analysis on venous phase CT images holds promise as a method for forecasting the degree of differentiation in esophageal cancers, which could significantly contribute to the preoperative noninvasive evaluation of tumor differentiation.
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Affiliation(s)
- Dawei Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Zeyu Shang
- University College London, London, United Kingdom
| | - Rong Chen
- Department of Medicine, Hebei North University, Zhangjiakou, China
| | - Yue Yang
- Department of Medicine, Hebei North University, Zhangjiakou, China
| | - Yaying Su
- Department of Nuclear medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Peng Jia
- Department of Medical Imaging, Beijing Huairou Hospital, Beijing, China
| | - Yanfang Liu
- Department of Operating rooms, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Fei Yang
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
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Dehghani Firouzabadi F, Gopal N, Homayounieh F, Anari PY, Li X, Ball MW, Jones EC, Samimi S, Turkbey E, Malayeri AA. CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis. Clin Imaging 2023; 94:9-17. [PMID: 36459898 PMCID: PMC9812928 DOI: 10.1016/j.clinimag.2022.11.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma. METHODS From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575). RESULTS After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically. CONCLUSIONS According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.
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Affiliation(s)
| | - Nikhil Gopal
- Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Fatemeh Homayounieh
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Pouria Yazdian Anari
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, USA
| | - Mark W Ball
- Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Safa Samimi
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Ashkan A Malayeri
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
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Anai K, Hayashida Y, Ueda I, Hozuki E, Yoshimatsu Y, Tsukamoto J, Hamamura T, Onari N, Aoki T, Korogi Y. The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol 2022; 40:1156-1165. [PMID: 35727458 PMCID: PMC9616757 DOI: 10.1007/s11604-022-01298-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/28/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the radiologists' diagnostic performance with or without SVM. MATERIALS AND METHODS This retrospective study included 50 patients (20 patients with focal-type AIP and 30 patients with PD) who underwent dynamic contrast-enhanced CT. Sixty-two CT texture-based features were extracted from 2D images of the arterial and portal phase CTs. We conducted data compression and feature selections using principal component analysis (PCA) and produced the SVM classifier. Four readers participated in this observer performance study and the statistical significance of differences with and without the SVM was assessed by receiver operating characteristic (ROC) analysis. RESULTS The SVM performance indicated a high performance in differentiating focal-type AIP and PD (AUC = 0.920). The AUC for all 4 readers increased significantly from 0.827 to 0.911 when using the SVM outputs (p = 0.010). The AUC for inexperienced readers increased significantly from 0.781 to 0.905 when using the SVM outputs (p = 0.310). The AUC for experienced readers increased from 0.875 to 0.912 when using the SVM outputs, however, there was no significant difference (p = 0.018). CONCLUSION The use of SVM classifier using CT texture-based features improved the diagnostic performance for differentiating focal-type AIP and PD on CT.
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Affiliation(s)
- Kenta Anai
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yoshiko Hayashida
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Issei Ueda
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Eri Hozuki
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yuuta Yoshimatsu
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Jun Tsukamoto
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Toshihiko Hamamura
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Norihiro Onari
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yukunori Korogi
- Department of Radiology, Kyushu Rosai Hospital, Moji Medical Center, 3-1, Higashiminatomachi, Moji-ku, Kitakyushu, Fukuoka 801-8502 Japan
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Popova E, Tkachev S, Reshetov I, Timashev P, Ulasov I. Imaging Hallmarks of Sarcoma Progression Via X-ray Computed Tomography: Beholding the Flower of Evil. Cancers (Basel) 2022; 14:cancers14205112. [PMID: 36291896 PMCID: PMC9600487 DOI: 10.3390/cancers14205112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Sarcomas represent the largest group of rare solid tumors that arise from mesenchymal stem cells and are a leading cause of cancer death in individuals younger than 20 years of age. There is an immediate need for the development of an algorithm for the early accurate diagnosis of sarcomas due to the high rate of diagnostic inaccuracy, which reaches up to 30%. X-ray computed tomography is a non-invasive imaging technique used to obtain detailed internal images of the human or animal body in clinical practice and preclinical studies. We summarized the main imaging features of soft tissue and bone sarcomas, and noted the development of new molecular markers to reach tumor type-specific imaging. Also, we demonstrated the possibility of the use X-ray computed microtomography for non-destructive 3D visualization of sarcoma progression in preclinical studies. Finding correlations between X-ray computed tomography modalities and the results of the histopathological specimen examination may significantly increase the accuracy of diagnostics, which leads to the initiation of appropriate management in a timely manner and, consequently, to improved outcomes. Abstract Sarcomas are a leading cause of cancer death in individuals younger than 20 years of age and represent the largest group of rare solid tumors. To date, more than 100 morphological subtypes of sarcomas have been described, among which epidemiology, clinical features, management, and prognosis differ significantly. Delays and errors in the diagnosis of sarcomas limit the number of effective therapeutic modalities and catastrophically worsen the prognosis. Therefore, the development of an algorithm for the early accurate diagnosis of sarcomas seems to be as important as the development of novel therapeutic advances. This literature review aims to summarize the results of recent investigations regarding the imaging of sarcoma progression based on the use of X-ray computed tomography (CT) in preclinical studies and in current clinical practice through the lens of cancer hallmarks. We attempted to summarize the main CT imaging features of soft-tissue and bone sarcomas. We noted the development of new molecular markers with high specificity to antibodies and chemokines, which are expressed in particular sarcoma subtypes to reach tumor type-specific imaging. We demonstrate the possibility of the use of X-ray computed microtomography (micro-CT) for non-destructive 3D visualization of solid tumors by increasing the visibility of soft tissues with X-ray scattering agents. Based on the results of recent studies, we hypothesize that micro-CT enables the visualization of neovascularization and stroma formation in sarcomas at high-resolution in vivo and ex vivo, including the novel techniques of whole-block and whole-tissue imaging. Finding correlations between CT, PET/CT, and micro-CT imaging features, the results of the histopathological specimen examination and clinical outcomes may significantly increase the accuracy of soft-tissue and bone tumor diagnostics, which leads to the initiation of appropriate histotype-specific management in a timely manner and, consequently, to improved outcomes.
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Affiliation(s)
- Elena Popova
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Sergey Tkachev
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Igor Reshetov
- University Clinical Hospital No. 1, I. M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Peter Timashev
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Ilya Ulasov
- Group of Experimental Biotherapy and Diagnostic, Institute for Regenerative Medicine, World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
- Correspondence: ; Tel.: +7-901-797-5406
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Ungan G, Lavandier AF, Rouanet J, Hordonneau C, Chauveau B, Pereira B, Boyer L, Garcier JM, Mansard S, Bartoli A, Magnin B. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J Comput Assist Radiol Surg 2022; 17:1867-1877. [PMID: 35650345 DOI: 10.1007/s11548-022-02662-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy. METHODS We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification. RESULTS The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p-value 0.001). CONCLUSION This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.
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Affiliation(s)
- Gulnur Ungan
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Anne-Flore Lavandier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jacques Rouanet
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Constance Hordonneau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Benoit Chauveau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Bruno Pereira
- Biostatistics Unit, DRCI, CHU Clermont Ferrand, 58 rue Montalembert, 63000, Clermont-Ferrand, France
| | - Louis Boyer
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jean-Marc Garcier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France.,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Sandrine Mansard
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Adrien Bartoli
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Benoit Magnin
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France. .,Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France. .,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
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Chen B, Steinberger O, Fenioux R, Duverger Q, Lambrou T, Dodin G, Blum A, Gondim Teixeira PA. Grading of soft tissues sarcomas using radiomics models: Choice of imaging methods and comparison with conventional visual analysis. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 2:100009. [PMID: 39076836 PMCID: PMC11265381 DOI: 10.1016/j.redii.2022.100009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/24/2022] [Indexed: 07/31/2024]
Abstract
Purpose To determine which combination of imaging modalities/contrast, radiomics models, and how many features provides the best diagnostic performance for the differentiation between low- and high-grade soft tissue sarcomas (STS) using a radiomics approach. Methods MRI and CT from 39 patients with a histologically confirmed STS were prospectively analyzed. Images were evaluated both quantitatively by radiomics models and qualitatively by visual evaluation (used as reference) for grading (low-grade vs high-grade). In radiomics analysis, 120 radiomic features were extracted and contributed into three models: least absolute shrinkage and selection operator with logistic regression(LASSO-LR), recursive feature elimination and cross-validation (RFECV-SVC) and analysis of variance with SVC (ANOVA-SVC). Those were applied to different combinations of imaging modalities acquisition, with and without contrast medium administration, as well as selected number of features. Results Fat-saturated T2w (FS-T2w) MR images using RFECV-SVC radiomic models involving five features yielded the best results with mean sensitivity, specificity, and accuracy of 92% ± 10%, 78% ± 30%, and 89% ± 12%, respectively. The performance of radiomics was better than that of conventional analysis (67% accuracy) for STS grading. Combination of multiple contrast or imaging modalities did not increase the diagnostic performance. Conclusion FS-T2w MR images alone with a five-feature radiomics analysis usingh REFCV-SVC model may be able to provide sufficient diagnositic performance compared to conventional visual evaluation with multiple MRI contrast and CT imaging.
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Affiliation(s)
- Bailiang Chen
- IADI, Inserm 1254 Nancy, University of Lorraine, Nancy, France
- Inserm CIC-IT 1433, University of Lorraine, Nancy, France
| | - Olivier Steinberger
- Guilloz imaging department, Central Hospital, University Hospital Center of Nancy, 29 avenue du Maréchal de Lattre de Tassigny, Nancy cedex 54035, France
| | - Roman Fenioux
- IADI, Inserm 1254 Nancy, University of Lorraine, Nancy, France
| | | | - Tryphon Lambrou
- School of Natural and Computing Sciences, University of Aberdeen, Meston Building, Old Aberdeen Campus, Meston Walk, Aberdeen AB24 3UE, United Kingdom
| | - Gauthier Dodin
- Guilloz imaging department, Central Hospital, University Hospital Center of Nancy, 29 avenue du Maréchal de Lattre de Tassigny, Nancy cedex 54035, France
| | - Alain Blum
- Guilloz imaging department, Central Hospital, University Hospital Center of Nancy, 29 avenue du Maréchal de Lattre de Tassigny, Nancy cedex 54035, France
| | - Pedro Augusto Gondim Teixeira
- IADI, Inserm 1254 Nancy, University of Lorraine, Nancy, France
- Guilloz imaging department, Central Hospital, University Hospital Center of Nancy, 29 avenue du Maréchal de Lattre de Tassigny, Nancy cedex 54035, France
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Devoto L, Ganeshan B, Keller D, Groves A, Endozo R, Arulampalam T, Chand M. Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer. Eur J Radiol Open 2022; 9:100415. [PMID: 35340828 PMCID: PMC8942820 DOI: 10.1016/j.ejro.2022.100415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 03/06/2022] [Accepted: 03/15/2022] [Indexed: 12/24/2022] Open
Abstract
Background Aim Methods Results Conclusion
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Affiliation(s)
- Laurence Devoto
- Wellcome / EPSRC Centre, for Interventional and Surgical Sciences, University College London, 1st Floor, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom
- Correspondence to: Wellcome / EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom.
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, 5th floor, Tower, University College London Hospital, 235 Euston Road, London NW1 2BU, United Kingdom
| | - Deborah Keller
- Wellcome / EPSRC Centre, for Interventional and Surgical Sciences, University College London, 1st Floor, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom
| | - Ashley Groves
- Institute of Nuclear Medicine, 5th floor, Tower, University College London Hospital, 235 Euston Road, London NW1 2BU, United Kingdom
| | - Raymond Endozo
- Institute of Nuclear Medicine, 5th floor, Tower, University College London Hospital, 235 Euston Road, London NW1 2BU, United Kingdom
| | - Tan Arulampalam
- ICENI Centre, Colchester Hospital, Turner Rd, Mile End, Colchester CO4 5JL, United Kingdom
| | - Manish Chand
- Wellcome / EPSRC Centre, for Interventional and Surgical Sciences, University College London, 1st Floor, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom
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Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker. Diagnostics (Basel) 2021; 11:diagnostics11122252. [PMID: 34943489 PMCID: PMC8700536 DOI: 10.3390/diagnostics11122252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis.
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Bonnin A, Durot C, Barat M, Djelouah M, Grange F, Mulé S, Soyer P, Hoeffel C. CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma. Diagn Interv Imaging 2021; 103:97-102. [PMID: 34666945 DOI: 10.1016/j.diii.2021.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies. MATERIALS AND METHODS Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment. RESULTS A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.
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Affiliation(s)
- Angèle Bonnin
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Carole Durot
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Maxime Barat
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Manel Djelouah
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Florent Grange
- Department of Dermatology, Valence Hospital, 26000 Valence, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, APH-HP, 94000 Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Christine Hoeffel
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, Reims Champagne-Ardenne University, 51000 Reims, France.
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Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis. Eur Radiol 2021; 32:2426-2436. [PMID: 34643781 DOI: 10.1007/s00330-021-08303-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 08/10/2021] [Accepted: 08/25/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC. METHODS In this retrospective study, 215 patients (average age: 57 years (18-87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set. RESULTS Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively. CONCLUSION FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions. KEY POINTS • Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer. • Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy.
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Caruso D, Pucciarelli F, Zerunian M, Ganeshan B, De Santis D, Polici M, Rucci C, Polidori T, Guido G, Bracci B, Benvenga A, Barbato L, Laghi A. Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia. Radiol Med 2021; 126:1415-1424. [PMID: 34347270 PMCID: PMC8335460 DOI: 10.1007/s11547-021-01402-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/12/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. MATERIALS AND METHODS One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. RESULTS Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). CONCLUSIONS Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.
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Affiliation(s)
- Damiano Caruso
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
| | - Domenico De Santis
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Carlotta Rucci
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Tiziano Polidori
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Bracci
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Benvenga
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Gitto S, Cuocolo R, Albano D, Morelli F, Pescatori LC, Messina C, Imbriaco M, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 2021; 12:68. [PMID: 34076740 PMCID: PMC8172744 DOI: 10.1186/s13244-021-01008-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/05/2021] [Indexed: 02/07/2023] Open
Abstract
Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.
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Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
| | - Renato Cuocolo
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy.,Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | | | - Lorenzo Carlo Pescatori
- Assistance Publique - Hôpitaux de Paris (AP-HP), Service d'Imagerie Médicale, CHU Henri Mondor, Créteil, France
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Massimo Imbriaco
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients. Dig Dis Sci 2021; 66:1227-1232. [PMID: 32409951 DOI: 10.1007/s10620-020-06318-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 05/02/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology. AIMS The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorectal cancer, and evaluate its biomarker value. METHODS This is a retrospective study of 80 (46 men and 34 women; median age: 68.0 years) colorectal cancer patients who underwent DWI followed by curative surgery at the Chiba University Hospital between 2009 and 2011. Median follow-up time was 62.2 months. Histogram parameters including signal intensity of kurtosis and skewness of the tumor were measured on DWI at b = 1000, and mean apparent diffusion coefficient value (ADC) of the tumor was also measured on ADC map generated by DWIs at b = 0 and 1000. Associations of tumor parameters (kurtosis, skewness, and ADC) with pathological features were analyzed, and these parameters were also compared with overall survival (OS) and relapse-free survival (RFS) using Cox regression and Kaplan-Meier analysis. RESULTS ADC of the tumor did not have significant associations with any pathological factors, but kurtosis and skewness of signal intensity in the tumor was significantly different between tumors with distant metastases and those without (4.23 ± 1.31 vs. 3.24 ± 1.32, p = 0.04; 1.09 ± 0.39 vs. 0.57 ± 0.58, p = 0.03). Kurtosis of the tumor was significantly correlated with OS and RFS (p = 0.04, p = 0.03, respectively), and skewness was significantly correlated with OS (p = 0.03) in Cox regression analysis. Higher kurtosis or higher skewness of the tumor was associated with worse OS in Kaplan-Meier analysis (p = 0.01, p = 0.009, log-rank). In subset analysis, there were 50 patients (32 men and 18 women) of lymph node-negative colorectal cancers (≤ stage II); skewness of signal intensity in the tumor was associated with OS using univariate Cox regression analysis (p = 0.04). CONCLUSIONS Histogram analysis of DWI can be a prognostic biomarker for colorectal cancer.
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14
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CT based radiomic approach on first line pembrolizumab in lung cancer. Sci Rep 2021; 11:6633. [PMID: 33758304 PMCID: PMC7988058 DOI: 10.1038/s41598-021-86113-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 02/24/2021] [Indexed: 02/06/2023] Open
Abstract
Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.
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15
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Gennaro N, Reijers S, Bruining A, Messiou C, Haas R, Colombo P, Bodalal Z, Beets-Tan R, van Houdt W, van der Graaf WTA. Imaging response evaluation after neoadjuvant treatment in soft tissue sarcomas: Where do we stand? Crit Rev Oncol Hematol 2021; 160:103309. [PMID: 33757836 DOI: 10.1016/j.critrevonc.2021.103309] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 02/15/2021] [Accepted: 03/03/2021] [Indexed: 12/16/2022] Open
Abstract
Soft tissue sarcomas (STS) represent a broad family of rare tumours for which surgery with radiotherapy represents first-line treatment. Recently, neoadjuvant chemo-radiotherapy has been increasingly used in high-risk patients in an effort to reduce surgical morbidity and improve clinical outcomes. An adequate understanding of the efficacy of neoadjuvant therapies would optimise patient care, allowing a tailored approach. Although response evaluation criteria in solid tumours (RECIST) is the most common imaging method to assess tumour response, Choi criteria and functional and molecular imaging (DWI, DCE-MRI and 18F-FDG-PET) seem to outperform it in the discrimination between responders and non-responders. Moreover, the radiologic-pathology correlation of treatment-related changes remains poorly understood. In this review, we provide an overview of the imaging assessment of tumour response in STS undergoing neoadjuvant treatment, including conventional imaging (CT, MRI, PET) and advanced imaging analysis. Future directions will be presented to shed light on potential advances in pre-surgical imaging assessments that have clinical implications for sarcoma patients.
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Affiliation(s)
- Nicolò Gennaro
- Humanitas Research and Cancer Center, Dept. of Radiology, Rozzano, Italy; Humanitas University, Dept. of Biomedical Sciences, Pieve Emanuele, Italy; The Netherlands Cancer Institute, Dept. of Radiology, Amsterdam, the Netherlands.
| | - Sophie Reijers
- The Netherlands Cancer Institute, Dept. of Surgical Oncology, Amsterdam, the Netherlands
| | - Annemarie Bruining
- The Netherlands Cancer Institute, Dept. of Radiology, Amsterdam, the Netherlands
| | - Christina Messiou
- The Royal Marsden NHS Foundation Trust, Dept. Of Radiology Sarcoma Unit, Sutton, United Kingdom; The Institute of Cancer Research, Sutton, United Kingdom
| | - Rick Haas
- The Netherlands Cancer Institute, Dept. of Radiation Oncology, Amsterdam, the Netherlands; Leiden University Medical Center, Dept. of Radiation Oncology, the Netherlands
| | | | - Zuhir Bodalal
- The Netherlands Cancer Institute, Dept. of Radiology, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Regina Beets-Tan
- The Netherlands Cancer Institute, Dept. of Radiology, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Danish Colorectal Cancer Center South, Vejle University Hospital, Institute of Regional Health Research, University of Southern Denmark, Denmark
| | - Winan van Houdt
- The Netherlands Cancer Institute, Dept. of Surgical Oncology, Amsterdam, the Netherlands
| | - Winette T A van der Graaf
- The Netherlands Cancer Institute, Dept. of Medical Oncology, Amsterdam, the Netherlands; Erasmus MC Cancer Institute, Dept. of Medical Oncology, Erasmus University Medical Center, Rotterdam, the Netherlands
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Kalisvaart GM, Bloem JL, Bovée JVMG, van de Sande MAJ, Gelderblom H, van der Hage JA, Hartgrink HH, Krol ADG, de Geus-Oei LF, Grootjans W. Personalising sarcoma care using quantitative multimodality imaging for response assessment. Clin Radiol 2021; 76:313.e1-313.e13. [PMID: 33483087 DOI: 10.1016/j.crad.2020.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/17/2020] [Indexed: 01/18/2023]
Abstract
Over the last decades, technological developments in the field of radiology have resulted in a widespread use of imaging for personalising medicine in oncology, including patients with a sarcoma. New scanner hardware, imaging protocols, image reconstruction algorithms, radiotracers, and contrast media, enabled the assessment of the physical and biological properties of tumours associated with response to treatment. In this context, medical imaging has the potential to select sarcoma patients who do not benefit from (neo-)adjuvant treatment and facilitate treatment adaptation. Due to the biological heterogeneity in sarcomas, the challenge at hand is to acquire a practicable set of imaging features for specific sarcoma subtypes, allowing response assessment. This review provides a comprehensive overview of available clinical data on imaging-based response monitoring in sarcoma patients and future research directions. Eventually, it is expected that imaging-based response monitoring will help to achieve successful modification of (neo)adjuvant treatments and improve clinical care for these patients.
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Affiliation(s)
- G M Kalisvaart
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - J L Bloem
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - J V M G Bovée
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - M A J van de Sande
- Department of Orthopaedics, Leiden University Medical Center, Leiden, the Netherlands
| | - H Gelderblom
- Department of Medical Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - J A van der Hage
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - H H Hartgrink
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - A D G Krol
- Department of Radiation Oncology. Leiden University Medical Center, Leiden, the Netherlands
| | - L F de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Biomedical Photonic Imaging Group, University of Twente, Enschede, the Netherlands
| | - W Grootjans
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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Radiomics Analysis of MR Imaging with Gd-EOB-DTPA for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Investigation and Comparison of Different Hepatobiliary Phase Delay Times. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6685723. [PMID: 33506029 PMCID: PMC7810556 DOI: 10.1155/2021/6685723] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Purpose To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Method A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. Results The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. Conclusions Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.
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Shen JX, Zhou Q, Chen ZH, Chen QF, Chen SL, Feng ST, Li X, Wu TF, Peng S, Kuang M. Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation. Transl Oncol 2020; 14:100866. [PMID: 33074127 PMCID: PMC7569222 DOI: 10.1016/j.tranon.2020.100866] [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: 05/15/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES To develop a radiomics algorithm, improving the performance of detecting recurrence, based on posttreatment CT images within one month and at suspicious time during follow-up. MATERIALS AND METHODS A total of 114 patients with 228 images were randomly split (7:3) into training and validation cohort. Radiomics algorithm was trained using machine learning, based on difference-in-difference (DD) features extracted from tumor and liver regions of interest on posttreatment CTs within one month after resection or ablation and when suspected recurrent lesion was observed but cannot be confirmed as HCC during follow-up. The performance was evaluated by area under the receiver operating characteristic curve (AUC) and was compared among radiomics algorithm, change of alpha-fetoprotein (AFP) and combined model of both. Five-folded cross validation (CV) was used to present the training error. RESULTS A radiomics algorithm was established by 34 DD features selected by random forest and multivariable logistic models and showed a better AUC than that of change of AFP (0.89 [95% CI: 0.78, 1.00] vs 0.63 [95% CI: 0.42, 0.84], P = .04) and similar with the combined model in detecting recurrence in the validation set. Five-folded CV error in the validation cohort was 21% for the algorithm and 26% for the changes of AFP. CONCLUSIONS The algorithm integrated radiomic features of posttreatment CT showed superior performance to that of conventional AFP and may act as a potential marker in the early detecting recurrence of HCC.
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Affiliation(s)
- Jing-Xian Shen
- State Key Laboratory of Oncology in Southern China, Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qian Zhou
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhi-Hang Chen
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qiao-Feng Chen
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shu-Ling Chen
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Ming Kuang
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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Ytre-Hauge S, Salvesen ØO, Krakstad C, Trovik J, Haldorsen IS. Tumour texture features from preoperative CT predict high-risk disease in endometrial cancer. Clin Radiol 2020; 76:79.e13-79.e20. [PMID: 32938538 DOI: 10.1016/j.crad.2020.07.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 07/15/2020] [Indexed: 01/31/2023]
Abstract
BACKGROUND To enable more individualised treatment of endometrial cancer, improved methods for preoperative tumour characterization are warranted. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in oncological imaging, but largely unexplored in endometrial cancer AIM: To explore whether tumour texture features from preoperative computed tomography (CT) are related to known prognostic histopathological features and to outcome in endometrial cancer patients. MATERIALS AND METHODS Preoperative pelvic contrast-enhanced CT was performed in 155 patients with histologically confirmed endometrial cancer. Tumour ROIs were manually drawn on the section displaying the largest cross-sectional tumour area, using dedicated texture analysis software. Using the filtration-histogram technique, the following texture features were calculated: mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis. These imaging markers were evaluated as predictors of histopathological high-risk features and recurrence- and progression-free survival using multivariable logistic regression and Cox regression analysis, including models adjusting for high-risk status based on preoperative biopsy, magnetic resonance imaging (MRI) findings, and age. RESULTS High tumour entropy independently predicted deep myometrial invasion (odds ratio [OR] 3.7, p=0.008) and cervical stroma invasion (OR 3.9, p=0.02). High value of MPP (MPP5 >24.2) independently predicted high-risk histological subtype (OR 3.7, p=0.01). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival (HR 1.1, p=0.06). CONCLUSION CT texture analysis yields promising imaging markers in endometrial cancer and may supplement other imaging techniques in providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies.
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Affiliation(s)
- S Ytre-Hauge
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Section for Radiology, Department of Clinical Medicine, University of Bergen, Norway.
| | - Ø O Salvesen
- Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - C Krakstad
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Norway
| | - J Trovik
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Norway
| | - I S Haldorsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Section for Radiology, Department of Clinical Medicine, University of Bergen, Norway
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Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M. Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J Radiol 2020; 132:109283. [PMID: 32980727 DOI: 10.1016/j.ejrad.2020.109283] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 08/29/2020] [Accepted: 09/08/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Sarcomas are a model for intra- and inter-tumoral heterogeneities making them particularly suitable for radiomics analyses. Our purposes were to review the aims, methods and results of radiomics studies involving sarcomas METHODS: Pubmed and Web of Sciences databases were searched for radiomics or textural studies involving bone, soft-tissues and visceral sarcomas until June 2020. Two radiologists evaluated their objectives, results and quality of their methods, imaging pre-processing and machine-learning workflow helped by the items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), Image Biomarker Standardization Initiative (IBSI) and 'Radiomics Quality Score' (RQS). Statistical analyses included inter-reader agreements, correlations between methodological assessments, scientometrics indices, and their changes over years, and between RQS, number of patients and models performance. RESULTS Fifty-two studies were included involving: soft-tissue sarcomas (29/52, 55.8 %), bone sarcomas (15/52, 28.8 %), gynecological sarcomas (6/52, 11.5 %) and mixed sarcomas (2/52, 3.8 %), mostly imaged with MRI (36/52, 69.2 %), for a total of distinct patients. Median RQS was 4.5 (28.4 % of the maximum, range: -7 - 17). Performances of predictive models and number of patients negatively correlated (p = 0.027). None of the studies detailed all the items from the IBSI guidelines. There was a significant increase in studies' impact factors since the establishing of the RQS in 2017 (p = 0.038). CONCLUSION Although showing promising results, further efforts are needed to make sarcoma radiomics studies reproducible with an acceptable level of evidence. A better knowledge of the RQS and IBSI reporting guidelines could improve the quality of sarcoma radiomics studies and accelerate clinical applications.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France; Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France; University of Bordeaux, F-33000, Bordeaux, France.
| | - David Fadli
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Antoine Italiano
- University of Bordeaux, F-33000, Bordeaux, France; Department of Medical Oncology, Institut Bergonie, F-33000, Bordeaux, France
| | - Olivier Saut
- Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
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Agarwal JP, Sinha S, Goda JS, Joshi K, Mhatre R, Kannan S, Laskar SG, Gupta T, Murthy V, Budrukkar A, Mummudi N, Ganeshan B. Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers. Br J Radiol 2020; 93:20190857. [PMID: 32101463 DOI: 10.1259/bjr.20190857] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE To study if pre-treatment CT texture features in locally advanced squamous cell carcinoma of laryngo-pharynx can predict long-term local control and laryngectomy free survival (LFS). METHODS Image texture features of 60 patients treated with chemoradiation (CTRT) within an ethically approved study were studied on contrast-enhanced images using a texture analysis research software (TexRad, UK). A filtration-histogram technique was used where the filtration step extracted and enhanced features of different sizes and intensity variations corresponding to a particular spatial scale filter (SSF): SSF = 0 (without filtration), SSF = 2 mm (fine texture), SSF = 3-5 mm (medium texture) and SSF = 6 mm (coarse texture). Quantification by statistical and histogram technique comprised mean intensity, standard-deviation, entropy, mean positive pixels, skewness and kurtosis. The ability of texture analysis to predict LFS or local control was determined using Kaplan-Meier analysis and multivariate cox model. RESULTS Median follow-up of patients was 24 months (95% CI:20-28). 39 (65%) patients were locally controlled at last follow-up. 10 (16%) had undergone salvage laryngectomy after CTRT. For both local control & LFS, threshold optimal cut-off values of texture features were analyzed. Medium filtered-texture feature that were associated with poorer laryngectomy free survival were entropy ≥4.54, (p = 0.006), kurtosis ≥4.18; p = 0.019, skewness ≤-0.59, p = 0.001, and standard deviation ≥43.18; p = 0.009). Inferior local control was associated with medium filtered features entropy ≥4.54; p 0.01 and skewness ≤ - 0.12; p = 0.02. Using fine filters, entropy ≥4.29 and kurtosis ≥-0.27 were also associated with inferior local control (p = 0.01 for both parameters). Multivariate analysis showed medium filter entropy as an independent predictor for LFS and local control (p < 0.001 & p = 0.001). CONCLUSION Medium texture entropy is a predictor for inferior local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancer and this can complement clinico-radiological factors in predicting prognosticating these tumors. ADVANCES IN KNOWLEDGE Texture features play an important role as a surrogate imaging biomarker for predicting local control and laryngectomy free survival in locally advanced laryngo-pharyngeal tumors treated with definitive chemoradiation.
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Affiliation(s)
- Jai Prakash Agarwal
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Shwetabh Sinha
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Jayant Sastri Goda
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Kishor Joshi
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Ritesh Mhatre
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Sadhana Kannan
- Department of Biostatistics Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Sarbani Ghosh Laskar
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Tejpal Gupta
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Vedang Murthy
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | | | - Naveen Mummudi
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India, 400012
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
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Fu J, Fang MJ, Dong D, Li J, Sun YS, Tian J, Tang L. Heterogeneity of metastatic gastrointestinal stromal tumor on texture analysis: DWI texture as potential biomarker of overall survival. Eur J Radiol 2020; 125:108825. [PMID: 32035324 DOI: 10.1016/j.ejrad.2020.108825] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 12/23/2019] [Accepted: 01/08/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To determine if texture features of diffusion weighted imaging (DWI) on MRI of metastatic gastrointestinal stromal tumor (mGIST) have correlation with overall survival (OS). METHOD Fifty-one GIST patients with metastatic lesions who received imatinib targeted therapy were included. Texture features of the largest metastatic lesion were analyzed using inhouse software. Three types of texture features were assessed: fractal features, gray-level co-occurrence matrix (GLCM) features, and gray-level run-length matrix (GLRLM) features. The features were extracted from the regions of interest (ROIs) on T2-weighted imaging (T2WI), DWI and apparent diffusion coefficient (ADC) maps. Histogram analysis was performed on ADC maps. Patients were followed up until death. Kaplan-Meier analysis was performed to determine the correlation of texture features with OS. The curves of the high- and low-risk groups were compared using log-rank test. The prognostic efficacy of the predictors was assessed by calculating the concordance probability. RESULTS The median survival time was 43.5 months (range, 3.97-120.90 m). Four DWI and three ADC texture features showed significant correlation with OS on univariate analysis (p < 0.05). DWI_L_GLCM_maximum_probability [hazard ratio (HR): 2.062 (1.357-3.131)], ADC_H_GLRLM_mean [HR: 2.174 (1.457-3.244)], and ADC_O_GLCM_cluster_shade [HR: 1.882 (1.324-2.674)] were identified as representative prognostic indicators. The optimum threshold levels for these three features were 1.19×100, 1.71×10 and 2.19×0.1, respectively. Neither histogram analysis values nor fractal features revealed significant correlation with survival status (p > 0.05). CONCLUSIONS Texture features of the mGIST on DWI exhibited correlation with overall survival. High-grade heterogeneity was associated with poor prognosis.
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Affiliation(s)
- Jia Fu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, 100142, China; Department of Radiology, Civil Aviation General Hospital, No. 1 Chaoyang Road, Chaoyang District, Beijing, 100123, China
| | - Meng-Jie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 East Zhongguancun Road, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 East Zhongguancun Road, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jian Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Departments of Gastroenterology, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 East Zhongguancun Road, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Lei Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
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Bach Cuadra M, Favre J, Omoumi P. Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics. Semin Musculoskelet Radiol 2020; 24:50-64. [DOI: 10.1055/s-0039-3400268] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractAlthough still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.
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Affiliation(s)
- Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
- Centre d'Imagerie BioMédicale (CIBM), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Julien Favre
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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24
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Shi B, Zhang GMY, Xu M, Jin ZY, Sun H. Distinguishing metastases from benign adrenal masses: what can CT texture analysis do? Acta Radiol 2019; 60:1553-1561. [PMID: 30799636 DOI: 10.1177/0284185119830292] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Bing Shi
- Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Shenzhen, PR China
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Gu-Mu-Yang Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Min Xu
- CT Scientific Collaboration, Siemens Healthcare Limited, Shanghai, PR China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Hao Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, PR China
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Deng Y, Soule E, Cui E, Samuel A, Shah S, Lall C, Sundaram C, Sandrasegaran K. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol 2019; 75:108-115. [PMID: 31668402 DOI: 10.1016/j.crad.2019.09.131] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 09/12/2019] [Indexed: 12/22/2022]
Abstract
AIM To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters. MATERIALS AND METHODS A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy. RESULTS Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC. CONCLUSIONS Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.
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Affiliation(s)
- Y Deng
- Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - E Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - E Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - A Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - S Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - C Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - C Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - K Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Mayo Clinic, Phoenix, AZ, USA.
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Cheng J, Wei J, Tong T, Sheng W, Zhang Y, Han Y, Gu D, Hong N, Ye Y, Tian J, Wang Y. Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method. Ann Surg Oncol 2019; 26:4587-4598. [PMID: 31605342 DOI: 10.1245/s10434-019-07910-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model. METHODS Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients' sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor-liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types. RESULTS A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively). CONCLUSIONS A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.
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Affiliation(s)
- Jin Cheng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Tong Tong
- Department of Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weiqi Sheng
- Department of Pathology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yinli Zhang
- Department of Pathology, Peking University People's Hospital, Beijing, China
| | - Yuqi Han
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Yingjiang Ye
- Department of Gastrointestinal Surgery, Peking University People' Hospital, Beijing, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,Beijing Key Laboratory of Molecular Imaging, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China. .,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China. .,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, China.
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Ren J, Yuan Y, Shi Y, Tao X. Tumor heterogeneity in oral and oropharyngeal squamous cell carcinoma assessed by texture analysis of CT and conventional MRI: a potential marker of overall survival. Acta Radiol 2019; 60:1273-1280. [PMID: 30818979 DOI: 10.1177/0284185119825487] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Jiliang Ren
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yiqian Shi
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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28
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Esser M, Kloth C, Thaiss WM, Reinert CP, Kraus MS, Gast GC, Horger M. CT-morphologic and CT-textural patterns of response in inoperable soft tissue sarcomas treated with pazopanib-a preliminary retrospective cohort study. Br J Radiol 2019; 92:20190158. [PMID: 31509443 DOI: 10.1259/bjr.20190158] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE To analyze patterns of response in soft tissue sarcomas exposed to pazopanib using CT-morphologic and textural features and their suitability for evaluating therapeutic response. METHODS Retrospective evaluation of CT response and texture patterns in 33 patients (23 female; mean age: 61.2 years, range, 30-85 years) with soft tissue sarcomas treated with pazopanib from October 2008 to July 2017. Response evaluation was based on modified (m)CHOI-criteria and RECISTv.1.1 and classified as partial response (PR), stable disease (SD), progressive disease (PD). The following CT-texture (CTTA)-parameters were calculated: mean, entropy and uniformity of intensity/average/skewness/entropy of co-occurrence matrix and contrast of neighboring-gray-level-dependence-matrix. RESULTS Following mCHOI-criteria, 12 patients achieved PR, 7 SD and 14 PD. As per RECISTv.1.1 9 patients obtained PR, 9 SD and 15 PD. Frequent patterns of response were tumor liquefaction and necrosis (n=4/33, 12.1% each). Further patterns included shrinkage and cavitation (n=2/33, 6.1% each). In responders, differences in mean heterogeneity (p=0.01), intensity (p=0.03), average (p=0.03) and entropy of skewness (p=0.01) were found at follow-up whereas in non-responders, CTTA-parameters did not change significantly. Baseline-CTTA-features differed between responders and non-responders in terms of uniformity of skewness (p=0.045). Baseline-CTTA-parameters did not correlate with any morphologic response pattern. CONCLUSION Most frequent patterns of response to pazopanib were tumor liquefaction and necrosis. Single CT-textural features show strong association with the response to pazopanib-although limited in relation to specific response patterns. ADVANCES IN KNOWLEDGE Tumor liquefication and necrosis are important patterns of response to pazopanib. CT-texture analysis has limited associations with specific response patterns.
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Affiliation(s)
- Michael Esser
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany
| | - Cristopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany
| | - Christian P Reinert
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany
| | - Mareen S Kraus
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany
| | - Gabriel Cc Gast
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany
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Feng ST, Jia Y, Liao B, Huang B, Zhou Q, Li X, Wei K, Chen L, Li B, Wang W, Chen S, He X, Wang H, Peng S, Chen ZB, Tang M, Chen Z, Hou Y, Peng Z, Kuang M. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol 2019; 29:4648-4659. [PMID: 30689032 DOI: 10.1007/s00330-018-5935-8] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 11/01/2018] [Accepted: 11/29/2018] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. RESULTS The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively. CONCLUSIONS We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. KEY POINTS • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.
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Affiliation(s)
- Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yingmei Jia
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bing Liao
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bingsheng Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Qian Zhou
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | - Kaikai Wei
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lili Chen
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bin Li
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wei Wang
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuling Chen
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaofang He
- Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Haibo Wang
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ze-Bin Chen
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Mimi Tang
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhihang Chen
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Yang Hou
- Jinan University, Guangzhou, China
| | - Zhenwei Peng
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.
| | - Ming Kuang
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.
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Meyer HJ, Renatus K, Höhn AK, Hamerla G, Schopow N, Fakler J, Josten C, Surov A. Texture analysis parameters derived from T1-and T2-weighted magnetic resonance images can reflect Ki67 index in soft tissue sarcoma. Surg Oncol 2019; 30:92-97. [PMID: 31500794 DOI: 10.1016/j.suronc.2019.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 04/23/2019] [Accepted: 06/21/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND OBJECTIVES Texture analysis derived from morphological magnetic resonance (MR) images might be associated with histopathology in tumors. The present study sought to elucidate possible associations between texture features derived from T1-and T2-weighted images with proliferation index Ki67 in soft tissue sarcomas. METHODS Overall, 29 patients (n = 13, 44.8% female) with a median age of 52 years were included into this retrospective study. Several soft tissue sarcomas were investigated. Texture analysis was performed on pre-contrast T1-weighted and T2-weighted images using the free available Mazda software. RESULTS The best correlation coefficients with Ki67 index were identified for the following parameters: T1-weighted images "45dgr_RLNonUni (p = 0.50, P = 0.006), T2-weighted images "S (4,0)SumAverg" (p = -0.45, P = 0.02). A ROC analysis was performed for Ki67-index with a threshold of 10%. The highest area under the curve (AUC) was found for the parameter "T1_WavEnHL_s-7" with an AUC of 0.90. For the threshold of Ki67 = 20% the highest AUC was identified for the parameter "T2_S (1,1)Entropy" with an AUC of 0.77. CONCLUSION Several texture features derived from T1-and T2-weighted images correlated with proliferation index Ki67 and might be used as valuable novel biomarkers in soft tissue sarcomas.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Katharina Renatus
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | | | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Nikolas Schopow
- Department of Orthopaedics, Trauma Surgery and Plastic Surgery, University of Leipzig, Leipzig, Germany
| | - Johannes Fakler
- Department of Orthopaedics, Trauma Surgery and Plastic Surgery, University of Leipzig, Leipzig, Germany
| | - Christoph Josten
- Department of Orthopaedics, Trauma Surgery and Plastic Surgery, University of Leipzig, Leipzig, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Texture features of colorectal liver metastases on pretreatment contrast-enhanced CT may predict response and prognosis in patients treated with bevacizumab-containing chemotherapy: a pilot study including comparison with standard chemotherapy. Radiol Med 2019; 124:877-886. [DOI: 10.1007/s11547-019-01046-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 05/13/2019] [Indexed: 02/06/2023]
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Durot C, Mulé S, Soyer P, Marchal A, Grange F, Hoeffel C. Metastatic melanoma: pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with pembrolizumab. Eur Radiol 2019; 29:3183-3191. [PMID: 30645669 DOI: 10.1007/s00330-018-5933-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/11/2018] [Accepted: 11/29/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE To determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images can predict overall survival (OS) and progression-free survival (PFS) in patients with metastatic malignant melanoma (MM) treated with an anti-PD-1 monoclonal antibody, pembrolizumab. MATERIALS AND METHODS This institutional-approved retrospective study included 31 patients with metastatic MM treated with pembrolizumab. Texture analysis of 74 metastatic lesions was performed on CT scanners obtained within 1 month before treatment. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent predictors of OS and PFS. RESULTS Median OS and PFS were 357 days (range 42-1355) and 99 days (range 35-1185), respectively. Skewness at coarse texture scale (SSF = 6; HR (CI 95%) = 6.017 (1.39, 26.056), p = 0.016), Response evaluation criteria in solid tumors (RECIST) conclusion (HR (CI 95%) = 3.41 (1.17, 9.89), p = 0.024), and body weight (HR (CI 95%) = 0.96 (0.92, 0.995), p = 0.026) were independent predictors of OS. Skewness at coarse texture scale (SSF = 6; HR (CI 95%) = 4.55 (1.46, 14.13), p = 0.0089) and RECIST conclusion (HR (CI 95%) = 10.63 (3.11, 36.29), p = 0.00016) were independent predictors of PFS. Skewness values above - 0.55 at coarse texture scale were significantly associated with both lower OS and lower PFS after administration of pembrolizumab. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness may act as predictive biomarker of OS and PFS in patients with metastatic MM treated with pembrolizumab. KEY POINTS • Pretreatment skewness at coarse texture scale in metastases from malignant melanoma was an independent predictor of overall survival and progression-free survival. • Skewness values above -0.55 at coarse texture scale were significantly associated with both lower OS and lower PFS after administration of pembrolizumab. • In patients with metastatic MM, texture analysis performed on pretreatment CT may act as a useful tool to select the best candidates for pembrolizumab therapy.
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Affiliation(s)
- Carole Durot
- Department of Radiology, Reims University Hospital, 45 rue Cognacq-jay, 51092, Reims, France.
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin University Hospital, Paris, France
| | - Aude Marchal
- Department of Biopathology, Reims University Hospital, Reims, France
| | - Florent Grange
- Department of Dermatology, Reims University Hospital, Reims, France
| | - Christine Hoeffel
- Department of Radiology, Reims University Hospital, 45 rue Cognacq-jay, 51092, Reims, France
- CRESTIC, University of Reims Champagne-Ardenne, Reims, France
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Deng Y, Soule E, Samuel A, Shah S, Cui E, Asare-Sawiri M, Sundaram C, Lall C, Sandrasegaran K. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 2019; 29:6922-6929. [PMID: 31127316 DOI: 10.1007/s00330-019-06260-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/01/2019] [Accepted: 04/30/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade. METHODS A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis. RESULTS A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful. CONCLUSION Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.
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Affiliation(s)
- Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Erik Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Aster Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sakhi Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Enming Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Oncology, Hope Regional Cancer Center, Panama, FL, USA
| | - Chandru Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Kumaresan Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.
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Cahalane AM, Kilcoyne A, Tabari A, McDermott S, Gee MS. Computed tomography texture features can discriminate benign from malignant lymphadenopathy in pediatric patients: a preliminary study. Pediatr Radiol 2019; 49:737-745. [PMID: 30741316 DOI: 10.1007/s00247-019-04350-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 11/26/2018] [Accepted: 01/24/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND Differentiation of benign from malignant lymphadenopathy remains challenging in pediatric radiology. Textural analysis (TA) quantitates heterogeneity of tissue signal intensities and has been applied to analysis of CT images. OBJECTIVE The purpose of this study was to establish whether CT textural analysis of enlarged lymph nodes visualized on pediatric CT can distinguish benign from malignant lymphadenopathy. MATERIALS AND METHODS We retrospectively identified enlarged lymph nodes measuring 10-20 mm on contrast-enhanced CTs of patients age 18 years and younger that had been categorized as benign or malignant based on the known diagnoses. We placed regions of interest (ROIs) over lymph nodes of interest and performed textural analysis with and without feature size filtration. We then calculated test performance characteristics for TA features, along with multivariate logistic regression modeling using Akaike Information Criterion (AIC) minimization, to determine the optimal thresholds for distinguishing benign from malignant lymphadenopathy. RESULTS We identified 34 enlarged malignant nodes and 29 benign nodes from 63 patients within the 10- to 20-mm size range. Filtered image TA exhibited 82.4% sensitivity, 86.2% specificity and 84.1% accuracy for detecting malignant lymph nodes using mean and entropy parameters, whereas unfiltered TA exhibited 88.2% sensitivity, 72.4% specificity and 81.0% accuracy using mean and mean value of positive pixels parameters. CONCLUSION This preliminary study demonstrates that the use of TA features improves the utility of pediatric CT to distinguish benign from malignant lymphadenopathy. The addition of TA to pediatric CT protocols has great potential to aid the characterization of indeterminate lymph nodes. If definitive differentiation between benign and malignant lymphadenopathy is possible by TA, it has the potential to reduce the need for follow-up imaging and tissue sampling, with reduced associated radiation exposure. However future studies are needed to confirm the clinical applicability of TA in distinguishing benign from malignant lymphadenopathy.
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Affiliation(s)
- Alexis M Cahalane
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
| | - Aoife Kilcoyne
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
| | - Shaunagh McDermott
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
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Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019; 50:1773-1788. [DOI: 10.1002/jmri.26753] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 03/31/2019] [Accepted: 04/02/2019] [Indexed: 12/20/2022] Open
Affiliation(s)
- Amandine Crombé
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
- University of BordeauxIMB, UMR CNRS 5251, INRIA Project Team Monc Talence France
| | - Olivier Saut
- University of BordeauxIMB, UMR CNRS 5251, INRIA Project Team Monc Talence France
| | - Jerome Guigui
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
| | - Antoine Italiano
- Department of Medical OncologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
| | - Xavier Buy
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
| | - Michèle Kind
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
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Crombé A, Périer C, Kind M, De Senneville BD, Le Loarer F, Italiano A, Buy X, Saut O. T 2 -based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J Magn Reson Imaging 2018; 50:497-510. [PMID: 30569552 DOI: 10.1002/jmri.26589] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 11/13/2018] [Accepted: 11/13/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Standard of care for patients with high-grade soft-tissue sarcoma (STS) are being redefined since neoadjuvant chemotherapy (NAC) has demonstrated a positive effect on patients' outcome. Yet response evaluation in clinical trials still relies on RECIST criteria. PURPOSE To investigate the added value of a Delta-radiomics approach for early response prediction in patients with STS undergoing NAC. STUDY TYPE Retrospective. POPULATION Sixty-five adult patients with newly-diagnosed, locally-advanced, histologically proven high-grade STS of trunk and extremities. All were treated by anthracycline-based NAC followed by surgery and had available MRI at baseline and after two chemotherapy cycles. FIELD STRENGTH/SEQUENCE Pre- and postcontrast enhanced T1 -weighted imaging (T1 -WI), turbo spin echo T2 -WI at 1.5 T. ASSESSMENT A threshold of <10% viable cells on surgical specimens defined good response (Good-HR). Two senior radiologists performed a semantic analysis of the MRI. After 3D manual segmentation of tumors at baseline and early evaluation, and standardization of voxel-sizes and intensities, absolute changes in 33 texture and shape features were calculated. STATISTICAL TESTS Classification models based on logistic regression, support vector machine, k-nearest neighbors, and random forests were elaborated using crossvalidation (training and validation) on 50 patients ("training cohort") and was validated on 15 other patients ("test cohort"). RESULTS Sixteen patients were good-HR. Neither RECIST status (P = 0.112) nor semantic radiological variables were associated with response (range of P-values: 0.134-0.490) except an edema decrease (P = 0.003), although 14 shape and texture features were (range of P-values: 0.002-0.037). On the training cohort, the highest diagnostic performances were obtained with random forests built on three features: Δ_Histogram_Entropy, Δ_Elongation, Δ_Surrounding_Edema, which provided: area under the curve the receiver operating characteristic = 0.86, accuracy = 88.1%, sensitivity = 94.1%, and specificity = 66.3%. On the test cohort, this model provided an accuracy of 74.6% but 3/5 good-HR were systematically ill-classified. DATA CONCLUSION A T2 -based Delta-radiomics approach might improve early response assessment in STS patients with a limited number of features. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:497-510.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, Regional Comprehensive Cancer Center, Bordeaux, France.,University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project team Monc, Talence, France
| | - Cynthia Périer
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project team Monc, Talence, France
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, Regional Comprehensive Cancer Center, Bordeaux, France
| | | | - François Le Loarer
- Department of Pathology, Institut Bergonie, Regional Comprehensive Cancer Center, Bordeaux, France
| | - Antoine Italiano
- Department of Medical Oncology, Institut Bergonie, Regional Comprehensive Cancer Center, Bordeaux, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, Regional Comprehensive Cancer Center, Bordeaux, France
| | - Olivier Saut
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project team Monc, Talence, France
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Baliyan V, Kordbacheh H, Parameswaran B, Ganeshan B, Sahani D, Kambadakone A. Virtual monoenergetic imaging in rapid kVp-switching dual-energy CT (DECT) of the abdomen: impact on CT texture analysis. Abdom Radiol (NY) 2018. [PMID: 29541830 DOI: 10.1007/s00261-018-1527-y] [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] [Indexed: 02/06/2023]
Abstract
PURPOSE To study the impact of keV levels of virtual monoenergetic images generated from rapid kVp-switching dual-energy CT (rsDECT) on CT texture analysis (CTTA). METHODS This study included 30 consecutive patients (59.3 ± 12 years; range 34-77 years; 17M:13F) who underwent portal venous phase abdominal CT on a rsDECT scanner. Axial 5-mm monoenergetic images at 5 energy levels (40/50/60/70/80 keV) were created and CTTA of liver was performed. CTTA comprised a filtration-histogram technique with different spatial scale filter (SSF) values (0-6). CTTA quantification at each SSF value included histogram-based statistical parameters such as mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. The values were compared using repeated measures ANOVA. RESULTS Among the different CTTA metrics, mean intensity (at SSF > 0), skewness, and kurtosis did not show variability whereas entropy, MPP, and SD varied with different keV levels. There was no change in skewness and kurtosis values for all 6 filters (p > 0.05). Mean intensity showed no change for filters 2-6 (p > 0.05). Mean intensity at SSF = 0 i.e., mean attenuations were 91.2 ± 2.9, 108.7 ± 3.6, 136.1 ± 4.7, 179.8 ± 6.9, and 250.5 ± 10.1 HU for 80, 70, 60, 50, and 40 keV images, respectively demonstrating significant variability (decrease) with increasing keV levels (p < 0.001). Entropy, MPP, and SD values showed a statistically significant decrease with increasing keV of monoenergetic images on all 6 filters (p < 0.001). CONCLUSION The energy levels of monoenergetic images have variable impact on the different CTTA parameters, with no significant change in skewness, kurtosis, and filtered mean intensity whereas significant decrease in mean attenuation, entropy, MPP, and SD values with increasing energy levels.
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Brenet Defour L, Mulé S, Tenenhaus A, Piardi T, Sommacale D, Hoeffel C, Thiéfin G. Hepatocellular carcinoma: CT texture analysis as a predictor of survival after surgical resection. Eur Radiol 2018; 29:1231-1239. [PMID: 30159621 DOI: 10.1007/s00330-018-5679-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 07/03/2018] [Accepted: 07/24/2018] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To determine whether image texture parameters analysed on pre-operative contrast-enhanced computed tomography (CT) can predict overall survival and recurrence-free survival in patients with hepatocellular carcinoma (HCC) treated by surgical resection. METHODS We retrospectively included all patients operated for HCC who had liver contrast-enhanced CT within 3 months prior to treatment in our centre between 2010 and 2015. The following texture parameters were evaluated on late-arterial and portal-venous phases: mean grey-level, standard deviation, kurtosis, skewness and entropy. Measurements were made before and after spatial filtration at different anatomical scales (SSF) ranging from 2 (fine texture) to 6 (coarse texture). Lasso penalised Cox regression analyses were performed to identify independent predictors of overall survival and recurrence-free survival. RESULTS Forty-seven patients were included. Median follow-up time was 345 days (interquartile range [IQR], 176-569). Nineteen patients had a recurrence at a median time of 190 days (IQR, 141-274) and 13 died at a median time of 274 days (IQR, 96-411). At arterial CT phase, kurtosis at SSF = 4 (hazard ratio [95% confidence interval] = 3.23 [1.35-7.71] p = 0.0084) was independent predictor of overall survival. At portal-venous phase, skewness without filtration (HR [CI 95%] = 353.44 [1.31-95102.23], p = 0.039), at SSF2 scale (HR [CI 95%] = 438.73 [2.44-78968.25], p = 0.022) and SSF3 (HR [CI 95%] = 14.43 [1.38-150.51], p = 0.026) were independently associated with overall survival. No textural feature was identified as predictor of recurrence-free survival. CONCLUSIONS In patients with resectable HCC, portal venous phase-derived CT skewness is significantly associated with overall survival and may potentially become a useful tool to select the best candidates for resection. KEY POINTS • HCC heterogeneity as evaluated by texture analysis of contrast-enhanced CT images may predict overall survival in patients treated by surgical resection. • Among texture parameters, skewness assessed at different anatomical scales at portal-venous phase CT is an independent predictor of overall survival after resection. • In patients with HCC, CT texture analysis may have the potential to become a useful tool to select the best candidates for resection.
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Affiliation(s)
- Lucie Brenet Defour
- Service d'Hépato-Gastroentérologie et de Cancérologie Digestive, Centre Hospitalier Universitaire de Reims, 51092, Reims, France
| | - Sébastien Mulé
- Service d'Imagerie Médicale, Centre Hospitalier Universitaire de Reims, Reims, France
| | - Arthur Tenenhaus
- Laboratoire des Signaux et Systèmes, CentraleSupélec, Université Paris-Saclay, Gif sur Yvette, France
| | - Tullio Piardi
- Service de Chirurgie Générale, Digestive et Endocrine, Centre Hospitalier Universitaire de Reims, Reims, France
| | - Daniele Sommacale
- Service de Chirurgie Générale, Digestive et Endocrine, Centre Hospitalier Universitaire de Reims, Reims, France
| | - Christine Hoeffel
- Service d'Imagerie Médicale, Centre Hospitalier Universitaire de Reims, Reims, France.,CReSTIC, Université de Reims Champagne-Ardenne, Reims, France
| | - Gérard Thiéfin
- Service d'Hépato-Gastroentérologie et de Cancérologie Digestive, Centre Hospitalier Universitaire de Reims, 51092, Reims, France.
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Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2018; 29:1067-1073. [PMID: 30116961 DOI: 10.1007/s00330-018-5662-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/15/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVES We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer. METHODS Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals. RESULTS The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003-0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan-Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively). CONCLUSIONS CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis can determine prognosis in patients with unresectable pancreas cancer. • The best predictors of poor prognosis were high kurtosis and MPP.
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Affiliation(s)
- Kumar Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.
| | - Yuning Lin
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Department of Medical Imaging, Fuzhou General Hospital, Fuzhou, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Hope Radiation Cancer, Panama City, FL, USA
| | - Tai Taiyini
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
| | - Mark Tann
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
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Role of Oxidative and Nitro-Oxidative Damage in Silver Nanoparticles Cytotoxic Effect against Human Pancreatic Ductal Adenocarcinoma Cells. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2018; 2018:8251961. [PMID: 30186549 PMCID: PMC6116403 DOI: 10.1155/2018/8251961] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 06/26/2018] [Accepted: 07/05/2018] [Indexed: 01/04/2023]
Abstract
Pancreatic ductal adenocarcinoma is one of the most aggressive human malignancies, where the 5-year survival rate is less than 4% worldwide. Successful treatment of pancreatic cancer is a challenge for today's oncology. Several studies showed that increased levels of oxidative stress may cause cancer cells damage and death. Therefore, we hypothesized that oxidative as well as nitro-oxidative stress is one of the mechanisms inducing pancreatic cancer programmed cell death. We decided to use silver nanoparticles (AgNPs) (2.6 and 18 nm) as a key factor triggering the reactive oxygen species (ROS) and reactive nitrogen species (RNS) in pancreatic ductal adenocarcinoma cells (PANC-1). Previously, we have found that AgNPs induced PANC-1 cells death. Furthermore, it is known that AgNPs may induce an accumulation of ROS and alteration of antioxidant systems in different type of tumors, and they are indicated as promising agents for cancer therapy. Then, the aim of our study was to evaluate the implication of oxidative and nitro-oxidative stress in this cytotoxic effect of AgNPs against PANC-1 cells. We determined AgNP-induced increase of ROS level in PANC-1 cells and pancreatic noncancer cell (hTERT-HPNE) for comparison purposes. We found that the increase was lower in noncancer cells. Reduction of mitochondrial membrane potential and changes in the cell cycle were also observed. Additionally, we determined the increase in RNS level: nitric oxide (NO) and nitric dioxide (NO2) in PANC-1 cells, together with increase in family of nitric oxide synthases (iNOS, eNOS, and nNOS) at protein and mRNA level. Disturbance of antioxidant enzymes: superoxide dismutase (SOD1, SOD2, and SOD3), glutathione peroxidase (GPX-4) and catalase (CAT) were proved at protein and mRNA level. Moreover, we showed cells ultrastructural changes, characteristic for oxidative damage. Summarizing, oxidative and nitro-oxidative stress and mitochondrial disruption are implicated in AgNPs-mediated death in human pancreatic ductal adenocarcinoma cells.
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Li LM, Feng LY, Chen XH, Liang P, Li J, Gao JB. Gastric heterotopic pancreas and stromal tumors smaller than 3 cm in diameter: clinical and computed tomography findings. Cancer Imaging 2018; 18:26. [PMID: 30086800 PMCID: PMC6081935 DOI: 10.1186/s40644-018-0161-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/29/2018] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Identifying gastric heterotopic pancreas and stromal tumors is difficult. Few studies have reported computed tomography (CT) findings for differentiating lesions less than 3 cm in diameter. In this study, we aimed to identify clinical characteristics and CT findings that can differentiate gastric heterotopic pancreatic lesions from stromal tumors less than 3 cm in diameter. METHODS A total of 132 patients with pathologically confirmed gastric heterotopic pancreas (n = 66) and stromal tumors (n = 66) were included. Each group was divided into primary (n = 50) and validation cohort (n = 16). Clinical characteristics and CT findings were retrospectively reviewed. CT findings included location, border, contour, growth pattern, enhancement pattern and grade, the enhancement value of tumor, enhancement ratio of tumor, and enhancement ratio of tumor to pancreas in venous phase. The findings in the two groups were compared using the Pearson χ2 test or Student t-test. Receiver operating characteristic curves were used to determine areas under the curve and optimal cut-offs. RESULTS Significant differences were observed between heterotopic pancreas and stromal tumors in the distribution of tumor location, border, contour (all P < 0.001), enhancement values (P < 0.001), enhancement ratios of tumors (P < 0.001), and enhancement ratios of tumors to pancreas (P < 0.001). No significant differences existed in growth pattern (P = 0.203). The area under the curve differed significantly between enhancement ratio of tumor to pancreas and enhancement ratio (P = 0.030). There were significant differences in above characteristics between two groups in validation cohort. CONCLUSIONS Heterotopic pancreas has characteristic CT features differentiating it from stromal tumors.
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Affiliation(s)
- Li-Ming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Lei-Yu Feng
- Department of Internal Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Xiao-Hua Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Jing Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
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Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuzé S, Schernberg A, Paragios N, Deutsch E, Ferté C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 2018; 28:1191-1206. [PMID: 28168275 DOI: 10.1093/annonc/mdx034] [Citation(s) in RCA: 457] [Impact Index Per Article: 76.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.
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Affiliation(s)
- E J Limkin
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Radiotherapy, Gustave Roussy, Paris-Saclay University, Villejuif
| | - R Sun
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Radiotherapy, Gustave Roussy, Paris-Saclay University, Villejuif.,Faculty of Medicine, Paris Sud University, Kremlin-Bicetre
| | - L Dercle
- Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy, Paris-Saclay University, Villejuif
| | - E I Zacharaki
- Center for Visual Computing, CentraleSupelec/Paris-Saclay University/Inria, Châtenay-Malabry
| | - C Robert
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Radiotherapy, Gustave Roussy, Paris-Saclay University, Villejuif.,Faculty of Medicine, Paris Sud University, Kremlin-Bicetre
| | - S Reuzé
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Radiotherapy, Gustave Roussy, Paris-Saclay University, Villejuif.,Faculty of Medicine, Paris Sud University, Kremlin-Bicetre
| | - A Schernberg
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Radiotherapy, Gustave Roussy, Paris-Saclay University, Villejuif.,Faculty of Medicine, Paris Sud University, Kremlin-Bicetre
| | - N Paragios
- Center for Visual Computing, CentraleSupelec/Paris-Saclay University/Inria, Châtenay-Malabry.,TheraPanacea, Paris
| | - E Deutsch
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Radiotherapy, Gustave Roussy, Paris-Saclay University, Villejuif
| | - C Ferté
- Radiomics team, INSERM U1030, Gustave Roussy.,Department of Head and Neck Oncology, Gustave Roussy, Paris-Saclay University, Villejuif, France
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Differentiating pheochromocytoma from lipid-poor adrenocortical adenoma by CT texture analysis: feasibility study. Abdom Radiol (NY) 2017; 42:2305-2313. [PMID: 28357529 DOI: 10.1007/s00261-017-1118-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To investigate the feasibility of using CT texture analysis (CTTA) to differentiate pheochromocytoma from lipid-poor adrenocortical adenoma (lp-ACA). METHODS Ninety-eight pheochromocytomas and 66 lp-ACAs were included in this retrospective study. CTTA was performed on unenhanced and enhanced images. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different for the objective. Diagnostic accuracies were evaluated using the cutoff values of texture parameters with the highest AUCs. RESULTS Compared to lp-ACAs, pheochromocytomas had significantly higher mean gray-level intensity (Mean), entropy, and mean of positive pixels (MPP), but lower skewness and kurtosis on unenhanced images (P < 0.001). On enhanced images, these texture-quantifiers followed a similar trend where Mean, entropy, and MPP were higher, but skewness and kurtosis were lower in pheochromocytomas. Standard deviation (SD) was also significantly higher in pheochromocytomas on enhanced images. Mean and MPP quantified from no filtration on unenhanced CT images yielded the highest AUC of 0.86 ± 0.03 (95% CI 0.81-0.91) at a cutoff value of 34.0 for Mean and MPP, respectively (sensitivity = 79.6%, specificity = 83.3%, accuracy = 81.1%). CONCLUSIONS It was feasible to use CTTA to differentiate pheochromocytoma from lp-ACA.
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Abbasian Ardakani A, Rajaee J, Khoei S. Diagnosis of human prostate carcinoma cancer stem cells enriched from DU145 cell lines changes with microscopic texture analysis in radiation and hyperthermia treatment using run-length matrix. Int J Radiat Biol 2017; 93:1248-1256. [PMID: 28738712 DOI: 10.1080/09553002.2017.1359429] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
PURPOSE Hyperthermia and radiation have the ability to induce structural and morphological changes on both macroscopic and microscopic level. Normal and damage cells have a different texture but may be perceived by human eye, as having the same texture. MATERIALS AND METHODS To explore the potential of texture analysis based on run-length matrix, a total of 32 sphere images for each group and treatment regime were used in this study. Cells were subjected to the treatment with different doses of 6 MeV electron radiation (0 2, 4 and 6 Gy), hyperthermia (at 43° C in 0, 30, 60 and 90 min) and radiation + hyperthermia (at 43 °C in 30 min with 2, 4 and 6 Gy dose), respectively. Twenty run-length matrix (RLM) features were extracted as descriptors for each selected region of interest for texture analysis. Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. RESULTS The features were classified by the first nearest neighbor classifier. RLM features represented the best performance with sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 100% between 0 and 6 Gy radiation, 0 and 6 Gy radiation + hyperthermia, 0 and 90 min and 30 and 90 min hyperthermia groups. The area under receiver operating characteristic curve was 1 for these groups. CONCLUSION RLM features have a high potential to characterize cell changes during different treatment regimes.
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Affiliation(s)
- Ali Abbasian Ardakani
- a Medical Physics Department, School of Medicine , Iran University of Medical Sciences , Tehran , Iran
| | - Jila Rajaee
- a Medical Physics Department, School of Medicine , Iran University of Medical Sciences , Tehran , Iran
| | - Samideh Khoei
- a Medical Physics Department, School of Medicine , Iran University of Medical Sciences , Tehran , Iran.,b Razi Drug Research Center , Iran University of Medical Sciences , Tehran , Iran
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Giardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J, Rakow-Penner R, Patel MJ, Subramaniam RM, Ganeshan D. Role of Imaging in the Era of Precision Medicine. Acad Radiol 2017; 24:639-649. [PMID: 28131497 DOI: 10.1016/j.acra.2016.11.021] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 11/07/2016] [Accepted: 11/29/2016] [Indexed: 12/17/2022]
Abstract
Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.
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Affiliation(s)
- Angela Giardino
- Department of Imaging, Dana-Farber Cancer Institute, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Supriya Gupta
- Department of Radiology and Imaging, Medical College of Georgia, 1120 15th St, Augusta, GA 30912.
| | - Emmi Olson
- Radiology Resident, University of California San Diego, San Diego, California
| | | | - Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jana Ivanidze
- Department of Diagnostic Radiology, Weill Cornell Medicine, New York, New York
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, San Diego, California
| | - Midhir J Patel
- Department of Radiology, University of South Florida, Tampa, Florida
| | - Rathan M Subramaniam
- Cyclotron and Molecular Imaging Program, Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
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