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Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15:304-314. [PMID: 38058604 PMCID: PMC10696186 DOI: 10.4329/wjr.v15.i11.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking. AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients. METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis. RESULTS 3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%. CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.
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Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Mayur Virarkar
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Hagar S Mahmoud
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Vincenzo K Wong
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Carlos Ignacio Gonzalez Baerga
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Miti Parikh
- Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States
| | - Sherif B Elsherif
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Priya R Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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Qian W, Chen Q, Hu C. Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging. J Comput Assist Tomogr 2023; Publish Ahead of Print:00004728-990000000-00161. [PMID: 37380155 DOI: 10.1097/rct.0000000000001463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
OBJECTIVE To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer. METHODS Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADCmax, ADC90, ADCmedian, ADCmean, ADC10, ADCmin, ADCskewness, ADCkurtosis, and ADCentropy) was compared between SS-EPI and RS-EPI and between the 2 groups. RESULTS There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADCmax and ADCentropy but lower ADC10, ADCmin and ADCskewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC90 and higher ADCkurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC90 and higher ADCkurtosis and ADCentropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADCkurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%). CONCLUSIONS Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADCkurtosis held great potential in differentiating normal-sized LNM in cervical cancer.
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Affiliation(s)
| | - Qian Chen
- Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou City, Jiangsu Province, China
| | - Chunhong Hu
- From the Department of Radiology, the First Affiliated Hospital of Soochow University; and
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Prognostic value of textural features obtained from F-fluorodeoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) in patients with locally advanced cervical cancer undergoing concurrent chemoradiotherapy. Ann Nucl Med 2023; 37:44-51. [PMID: 36369325 DOI: 10.1007/s12149-022-01802-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 10/23/2022] [Indexed: 11/13/2022]
Abstract
OBJECTIVE To evaluate whether textural features obtained from F-18 FDG PET/CT offer clinical value that can predict the outcome of patients with locally advanced cervical cancer (LACC) receiving concurrent chemoradiotherapy (CCRT). METHODS We reviewed the records of 68 patients with stage IIB-IVA LACC who underwent PET/CT before CCRT. Conventional metabolic parameters, shape indices, and textural features of the primary tumor were measured on PET/CT. A Cox regression model was used to examine the effects of variables on overall survival (OS) and progression-free survival (PFS). RESULTS The patients included in this study were classified into two groups based on median value of PET/CT parameters. The high group of GLNU derived from GLRLM is only independent prognostic factor for PFS (HR 7.142; 95% CI 1.656-30.802; p = 0.008) and OS (HR 9,780; 95% CI 1.222-78.286; p = 0.031). In addition, GLNU derived from GLRLM (AUC 0.846, 95% CI 0.738-0.923) was the best predictor for recurrence among clinical prognostic factors and PET/CT parameters. CONCLUSION Our results demonstrated that high GLNU from GLRLM on pretreatment F-18 FDG PET/CT images, were significant prognostic factors for recurrence and death in patients with LACC receiving CCRT.
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Stamoulou E, Spanakis C, Manikis GC, Karanasiou G, Grigoriadis G, Foukakis T, Tsiknakis M, Fotiadis DI, Marias K. Harmonization Strategies in Multicenter MRI-Based Radiomics. J Imaging 2022; 8:303. [PMID: 36354876 PMCID: PMC9695920 DOI: 10.3390/jimaging8110303] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 08/13/2023] Open
Abstract
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.
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Affiliation(s)
- Elisavet Stamoulou
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Constantinos Spanakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Georgios C. Manikis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Georgia Karanasiou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Grigoris Grigoriadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Manolis Tsiknakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 451 15 Ioannina, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
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Wang W, Jiao Y, Zhang L, Fu C, Zhu X, Wang Q, Gu Y. Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage. Acta Radiol 2022; 63:847-856. [PMID: 33975448 DOI: 10.1177/02841851211014188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods. PURPOSE To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity. MATERIAL AND METHODS This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences. RESULTS Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC (P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone. CONCLUSION Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC.
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Affiliation(s)
- Wei Wang
- Department of Radiology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
| | - YiNing Jiao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - LiChi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, PR China
| | - XiaoLi Zhu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
- Department of Pathology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, PR China
| | - Qian Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
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Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, El Naqa I, Bussink J, Gillies R, Wee L, Traverso A. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021; 20:69-75. [PMID: 34816024 PMCID: PMC8591412 DOI: 10.1016/j.phro.2021.10.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open
Abstract
Main factors impacting feature stability: Image acquisition, reconstruction, tumor segmentation, and interpolation. Textural features are less robust than morphological or statistical features. A checklist is provided including items that should be reported in a radiomic study.
Purpose Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years. Methods and materials Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study. Results Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features. Conclusions Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.
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Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ivan Zhovannik
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jan Bussink
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert Gillies
- Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Zhang B, Song L, Yin J. Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors. Front Oncol 2021; 11:688182. [PMID: 34307153 PMCID: PMC8299951 DOI: 10.3389/fonc.2021.688182] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/15/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose To evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors. Materials and Methods A total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves. Results In the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808-0.906), 0.860 (95% CI, 0.806-0.904), 0.934 (95% CI, 0.891-0.963), and 0.921 (95% CI, 0.876-0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747-0.908), 0.784 (95% CI, 0.601-0.798), 0.890 (95% CI, 0.806-0.946), and 0.865 (95% CI, 0.777-0.928), respectively. Conclusion The texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.
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Affiliation(s)
- Bin Zhang
- School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China
| | - Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Song L, Li C, Yin J. Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer. Front Oncol 2021; 11:675160. [PMID: 34168994 PMCID: PMC8217832 DOI: 10.3389/fonc.2021.675160] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/14/2021] [Indexed: 12/29/2022] Open
Abstract
Objective To evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer. Materials and Methods This study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis. Results Among the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SIslope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%). Conclusions Texture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.
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Affiliation(s)
- Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chunli Li
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer. J Comput Assist Tomogr 2021; 45:191-202. [PMID: 33273161 DOI: 10.1097/rct.0000000000001117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images. METHODS We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness. RESULTS High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram. CONCLUSIONS The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.
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Gui B, Autorino R, Miccò M, Nardangeli A, Pesce A, Lenkowicz J, Cusumano D, Russo L, Persiani S, Boldrini L, Dinapoli N, Macchia G, Sallustio G, Gambacorta MA, Ferrandina G, Manfredi R, Valentini V, Scambia G. Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040631. [PMID: 33807494 PMCID: PMC8066099 DOI: 10.3390/diagnostics11040631] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/25/2021] [Accepted: 03/27/2021] [Indexed: 02/07/2023] Open
Abstract
The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR―assessed on surgical specimen―was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome.
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Affiliation(s)
- Benedetta Gui
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Rosa Autorino
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Maura Miccò
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Alessia Nardangeli
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
- Correspondence:
| | - Adele Pesce
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Davide Cusumano
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Luca Russo
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Salvatore Persiani
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Luca Boldrini
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Nicola Dinapoli
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
| | - Gabriella Macchia
- Gemelli Molise Hospital, Università Cattolica del Sacro Cuore, 86100 Campobasso, Italy; (G.M.); (G.S.)
| | - Giuseppina Sallustio
- Gemelli Molise Hospital, Università Cattolica del Sacro Cuore, 86100 Campobasso, Italy; (G.M.); (G.S.)
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Gabriella Ferrandina
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
| | - Giovanni Scambia
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Roma, Italy; (B.G.); (R.A.); (M.M.); (J.L.); (D.C.); (L.B.); (N.D.); (M.A.G.); (G.F.); (R.M.); (V.V.); (G.S.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (A.P.); (L.R.); (S.P.)
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11
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Mi H, Yuan M, Suo S, Cheng J, Li S, Duan S, Lu Q. Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women's cervix. Sci Rep 2020; 10:20407. [PMID: 33230228 PMCID: PMC7684312 DOI: 10.1038/s41598-020-76989-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 10/16/2020] [Indexed: 12/12/2022] Open
Abstract
MR Radiomics based on cervical lesions from one single scanner has achieved promising results. However, it is a challenge to achieve clinical translation. Considering multi-scanners and non-uniform scanning parameters from different centers in a real-world medical scenario, we should first identify the influence of such conditions on the robustness of MR radiomics features (RFs) based on the female cervix. In this study, 9 healthy female volunteers were enrolled and 3 kiwis were selected as references. Each of them underwent T2 weighted imaging in three different 3.0-T MR scanners with uniform acquisition parameters, and in one MR scanner with various scanning parameters. A total of 396 RFs were extracted from their images with and without decile intensity normalization. The RFs’ reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Representative features were selected using the hierarchical cluster analysis and their discrimination abilities were estimated by ROC analysis through retrospective comparison with the junctional zone and the outer muscular layer of healthy cervix in patients (n = 58) with leiomyoma. This study showed that only a few RFs were robust across different MR scanners and acquisition parameters based on females’ cervix, which might be improved by decile intensity normalization method.
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Affiliation(s)
- Honglan Mi
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Mingyuan Yuan
- Department of Radiology, Affiliated Zhoupu Hospital, Shanghai University of Medicine & Health Sciences College, 1500 Zhouyuan Road, PongDong New District, Shanghai, 201318, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Jiejun Cheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Suqin Li
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Shaofeng Duan
- GE Healthcare China, Pudong new town, No1, Huatuo road, Shanghai, 210000, China
| | - Qing Lu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China.
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12
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Qiu QT, Zhang J, Duan JH, Wu SZ, Ding JL, Yin Y. Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis. Chin Med J (Engl) 2020; 133:2653-2659. [PMID: 33009025 PMCID: PMC7647495 DOI: 10.1097/cm9.0000000000001113] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC. METHODS Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC). RESULTS The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946-1.000) and 0.948 (95% CI 0.903-0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively. CONCLUSIONS Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.
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Affiliation(s)
- Qing-Tao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Jing Zhang
- Department of Radiation Oncology, Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Jing-Hao Duan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Shi-Zhang Wu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Jia-Lin Ding
- School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
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13
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Song L, Yin J. Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer. Front Oncol 2020; 10:1364. [PMID: 32850437 PMCID: PMC7426518 DOI: 10.3389/fonc.2020.01364] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/29/2020] [Indexed: 12/18/2022] Open
Abstract
Objective: To investigate the value of texture features derived from T2-weighted magnetic resonance imaging (T2WI) for predicting preoperative lymph node invasion (N stage) in rectal cancer. Materials and Methods: One hundred and eighty-two patients with histopathologically confirmed rectal cancer and preoperative magnetic resonance imaging were retrospectively analyzed, who were divided into high (N1-2) and low N stage (N0). Texture features were calculated from histogram, gray-level co-occurrence matrix, and gray-level run-length matrix from sagittal fat-suppression and oblique axial T2WI. Independent sample t-test or Mann-Whitney U-test were used for statistical analysis. Multivariate logistic regression analysis was conducted to build the predictive models. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Results: Energy (ENE), entropy (ENT), information correlation (INC), long-run emphasis (LRE), and short-run low gray-level emphasis (SRLGLE) extracted from sagittal fat-suppression T2WI, and ENE, ENT, INC, low gray-level run emphasis (LGLRE), and SRLGLE from oblique axial T2WI were significantly different between stage N0 and stage N1-2 tumors. The multivariate analysis for features from sagittal fat-suppression T2WI showed that higher SRLGLE and lower ENE were independent predictors of lymph node invasion. The model reached an area under ROC curve (AUC) of 0.759. The analysis for features from oblique axial T2WI showed that higher INC and SRLGLE were independent predictors. The model achieved an AUC of 0.747. The analysis for all extracted features showed that lower ENE from sagittal fat-suppression T2WI and higher INC and SRLGLE from oblique axial T2WI were independent predictors. The model showed an AUC of 0.772. Conclusions: Texture features derived from T2WI could provide valuable information for identifying the status of lymph node invasion in rectal cancer.
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Affiliation(s)
- Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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14
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Park SH, Hahm MH, Bae BK, Chong GO, Jeong SY, Na S, Jeong S, Kim JC. Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis. Radiat Oncol 2020; 15:86. [PMID: 32312283 PMCID: PMC7171757 DOI: 10.1186/s13014-020-01502-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/19/2020] [Indexed: 01/08/2023] Open
Abstract
Background Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients. Methods We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31). Results For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset. Conclusions Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.
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Affiliation(s)
- Shin-Hyung Park
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
| | - Myong Hun Hahm
- Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of Korea
| | - Bong Kyung Bae
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Gun Oh Chong
- Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.,Department of Obstetrics and Gynecology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.,Molecular Diagnostics and Imaging Center, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Shin Young Jeong
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sungdae Na
- Department of Biomedical Engineering Center, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Sungmoon Jeong
- Bio-Medical Research Institute, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.,Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Jae-Chul Kim
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
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15
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Cattell R, Chen S, Huang C. Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2019; 2:19. [PMID: 32240418 PMCID: PMC7099536 DOI: 10.1186/s42492-019-0025-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 10/09/2019] [Indexed: 02/07/2023] Open
Abstract
Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image. These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine. However, the generalizability of the model is dependent on the robustness of these features. The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging. Additionally, a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions (signal to noise ratio, region of interest delineation, voxel size change and normalization methods) using intraclass correlation coefficients. The features extracted in this phantom study include first order, shape, gray level cooccurrence matrix and gray level run length matrix. Many features are found to be non-robust to changing parameters. Feature robustness assessment prior to feature selection, especially in the case of combining multi-institutional data, may be warranted. Further investigation is needed in this area of research.
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Affiliation(s)
- Renee Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Shenglan Chen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Chuan Huang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. .,Department of Radiology, Stony Brook Medicine, Stony Brook, NY, 11794, USA. .,Department of Psychiatry, Stony Brook Medicine, Stony Brook, NY, 11794, USA.
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16
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Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules. J Comput Assist Tomogr 2019; 43:817-824. [PMID: 31343995 DOI: 10.1097/rct.0000000000000889] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The aim of this study was to investigate the differentiation of computed tomography (CT)-based entropy parameters between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions appearing as pulmonary subsolid nodules (SSNs). METHODS This study was approved by the institutional review board in our hospital. From July 2015 to November 2018, 186 consecutive patients with solitary peripheral pulmonary SSNs that were pathologically confirmed as pulmonary adenocarcinomas (74 MIA and 112 IAC lesions) were included and subdivided into the training data set and the validation data set. Chest CT scans without contrast enhancement were performed in all patients preoperatively. The subjective CT features of the SSNs were reviewed and compared between the MIA and IAC groups. Each SSN was semisegmented with our in-house software, and entropy-related parameters were quantitatively extracted using another in-house software developed in the MATLAB platform. Logistic regression analysis and receiver operating characteristic analysis were performed to evaluate the diagnostic performances. Three diagnostic models including subjective model, entropy model, and combined model were built and analyzed using area under the curve (AUC) analysis. RESULTS There were 119 nonsolid nodules and 67 part-solid nodules. Significant differences were found in the subjective CT features among nodule type, lesion size, lobulated shape, and irregular margin between the MIA and IAC groups. Multivariate analysis revealed that part-solid type and lobulated shape were significant independent factors for IAC (P < 0.0001 and P < 0.0001, respectively). Three entropy parameters including Entropy-0.8, Entropy-2.0-32, and Entropy-2.0-64 were identified as independent risk factors for the differentiation of MIA and IAC lesions. The median entropy model value of the MIA group was 0.266 (range, 0.174-0.590), which was significantly lower than the IAC group with value 0.815 (range, 0.623-0.901) (P < 0.0001). Multivariate analysis revealed that the combined model had an excellent diagnostic performance with sensitivity of 88.2%, specificity of 73.0%, and accuracy of 82.1%. The AUC value of the combined model was significantly higher (AUC, 0.869) than that of the subjective model (AUC, 0.809) or the entropy model alone (AUC, 0.836) (P < 0.0001). CONCLUSIONS The CT-based entropy parameters could help assess the aggressiveness of pulmonary adenocarcinoma via quantitative analysis of intratumoral heterogeneity. The MIA can be differentiated from IAC accurately by using entropy-related parameters in peripheral pulmonary SSNs.
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17
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Schick U, Lucia F, Dissaux G, Visvikis D, Badic B, Masson I, Pradier O, Bourbonne V, Hatt M. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol 2019; 92:20190105. [PMID: 31538516 DOI: 10.1259/bjr.20190105] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.
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Affiliation(s)
- Ulrike Schick
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - François Lucia
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Gurvan Dissaux
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Bogdan Badic
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Department of General and Digestive Surgery, University Hospital, Brest, France
| | - Ingrid Masson
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Olivier Pradier
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Vincent Bourbonne
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
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18
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Kiser KJ, Smith BD, Wang J, Fuller CD. "Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era. Front Oncol 2019; 9:983. [PMID: 31632914 PMCID: PMC6779062 DOI: 10.3389/fonc.2019.00983] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/16/2019] [Indexed: 12/17/2022] Open
Abstract
Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.
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Affiliation(s)
- Kendall J Kiser
- John P. and Kathrine G. McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States.,School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.,Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin D Smith
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jihong Wang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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19
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Lee J, Kim CK, Park SY. Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 33:283-292. [DOI: 10.1007/s10334-019-00777-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/29/2019] [Accepted: 09/16/2019] [Indexed: 10/25/2022]
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20
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Tixier F, Um H, Young RJ, Veeraraghavan H. Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features. Med Phys 2019; 46:3582-3591. [PMID: 31131906 DOI: 10.1002/mp.13624] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 05/21/2019] [Accepted: 05/21/2019] [Indexed: 12/22/2022] Open
Abstract
PURPOSE The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features. METHOD Fluid-attenuated inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1WICE ) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray-level co-occurrence matrix, gray-level size-zone matrix (GLSZM), edge maps, and shape) using intra-class correlation coefficient (ICC) and Bland and Altman analysis. RESULTS Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SDdiffs ) ≤ 30%. Features computed from the histogram and co-occurrence matrices were found to be the most robust (ICC ≥ 0.8 and SDdiffs ≤ 30% for most features in these groups). Features from GLSZM were shown to have mixed robustness. Edge, shape, and GLSZM features were the most impacted by the choice of segmentation method with the interactive method resulting in more robust features than the semiautomatic method. Finally, features computed from T1WICE and FLAIR images were found to have similar robustness when computed with the interactive segmentation method. CONCLUSION Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.
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Affiliation(s)
- Florent Tixier
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Hyemin Um
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother Oncol 2019; 135:107-114. [DOI: 10.1016/j.radonc.2019.03.001] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 03/02/2019] [Accepted: 03/04/2019] [Indexed: 02/05/2023]
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Yu YY, Zhang R, Dong RT, Hu QY, Yu T, Liu F, Luo YH, Dong Y. Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB-IIA cervical squamous cell carcinoma. Br J Radiol 2019; 92:20180986. [PMID: 30888846 DOI: 10.1259/bjr.20180986] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB-IIA cervical squamous cell carcinoma (CSCC). METHODS A total of 153 stage IB-IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson's correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. RESULTS Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95% CI, 0.782 - 0.924) and the validation group (AUC 0.870; 95% CI, 0.747 - 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. CONCLUSION The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. ADVANCES IN KNOWLEDGE A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
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Affiliation(s)
- Yan Yan Yu
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China.,2 Graduate School of Dalian Medical University , Dalian, Liaoning , China
| | - Rui Zhang
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
| | - Rui Tong Dong
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
| | - Qi Yun Hu
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
| | - Tao Yu
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
| | - Fan Liu
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
| | - Ya Hong Luo
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
| | - Yue Dong
- 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China
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Lucia F, Visvikis D, Vallières M, Desseroit MC, Miranda O, Robin P, Bonaffini PA, Alfieri J, Masson I, Mervoyer A, Reinhold C, Pradier O, Hatt M, Schick U. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 2018; 46:864-877. [DOI: 10.1007/s00259-018-4231-9] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/27/2018] [Indexed: 12/22/2022]
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Meyer HJ, Purz S, Sabri O, Surov A. Relationships between histogram analysis of ADC values and complex 18F-FDG-PET parameters in head and neck squamous cell carcinoma. PLoS One 2018; 13:e0202897. [PMID: 30188926 PMCID: PMC6126801 DOI: 10.1371/journal.pone.0202897] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 08/10/2018] [Indexed: 11/26/2022] Open
Abstract
Purpose Histogram analysis is an emergent imaging technique to further analyze radiological images and to obtain imaging biomarker. In head and neck cancer, MRI and PET are routinely used in clinical practice. The aim of this study was to analyze associations between histogram based ADC parameters and complex FDG-PET derived parameters in head and neck squamous cell carcinoma (HNSCC). Methods 34 patients (26% female, mean age, 56.7 ± 10.2 years) with primary HNSCC were prospectively included into the study. ADC histogram parameters were calculated by inhouse made matlab software using a whole lesion measurement. For each tumor, maximum and mean standardized uptake values (SUVmax, SUVmean), Total Lesion Glycolysis (TLG) and Metabolic Tumor Volume (MTV) were determined on PET-images. Spearman's correlation coefficient (ρ) was used to analyze associations between investigated parameters. Benjamini-Hochberg correction was used to adjust for multiple testing. Mann-Whitney test was used for group discrimination. P-values < 0.05 were taken to indicate statistical significance. Results The correlation analysis in the whole tumor group revealed a statistically significant correlation between entropy and MTV as well as TLG (ρ = 0.67, P<0.0001 and ρ = 0.61, P = 0.0002 respectively). There were statistically significant differences between T1/2 and T3/4 tumors in the following parameters: entropy (2.07 ± 0.36 vs 2.61 ± 0.43, P = 0.007), SUVmax (10.79 ± 4.13 vs 17.93 ± 5.89, P = 0.007), SUVmean (6.39 ± 2.48 vs 9.81 ± 4.49, P = 0.01), SUVmin (4.09 ± 1.57 vs 6.34 ± 2.59, P = 0.03), MTV (9.50 ± 7.92 vs 20.36 ± 13.30, P = 0.02), TGU (55.97 ± 39.09 vs 212.3 ± 186.3, P = 0.002). Conclusion This study showed that entropy derived from ADC maps is strongly associated with MTV and TLG in HNSCC. Entropy, SUVmax, SUVmean, TLG and MTV were statistically significant higher in T3/4 tumors in comparison to T1/2 carcinomas.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
- * E-mail:
| | - Sandra Purz
- Department of Nuclear Medicine, University of Leizig, Leipzig, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University of Leizig, Leipzig, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Whole-lesion Apparent Diffusion Coefficient First- and Second-Order Texture Features for the Characterization of Rectal Cancer Pathological Factors. J Comput Assist Tomogr 2018; 42:642-647. [PMID: 29613992 DOI: 10.1097/rct.0000000000000731] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE The objective of this study was to explore the value of whole-volume apparent diffusion coefficient (ADC) features in characterizing pathologic features of rectal cancer. METHODS A total of 50 patients who were diagnosed with rectal cancer via biopsy underwent 3-T pretreatment diffusion-weighted imaging. Apparent diffusion coefficient features, including mean, 10th-90th percentile, Entropy and Entropy(H), derived from whole-lesion volumes were compared between pathologic T1-2 and T3 stages, perineural invasion (PNI) present and absent, lymphangiovascular invasion present and absent, and pathological N0 and N+ stage groups. RESULTS Entropy and Entropy(H) were significantly lower in rectal cancers at T1-2 stages than T3. The 90th percentile of rectal cancers with PNI was significantly lower than that of those without PNI. All P < 0.05. CONCLUSIONS Whole-lesion ADC Entropy and Entropy(H) have potential in evaluating different T stages, and 90th percentile can be helpful for determining PNI presence of rectal cancers.
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Cervical Cancer: Associations between Metabolic Parameters and Whole Lesion Histogram Analysis Derived from Simultaneous 18F-FDG-PET/MRI. CONTRAST MEDIA & MOLECULAR IMAGING 2018; 2018:5063285. [PMID: 30154687 PMCID: PMC6098855 DOI: 10.1155/2018/5063285] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/12/2018] [Accepted: 06/25/2018] [Indexed: 01/16/2023]
Abstract
Multimodal imaging has been increasingly used in oncology, especially in cervical cancer. By using a simultaneous positron emission (PET) and magnetic resonance imaging (MRI, PET/MRI) approach, PET and MRI can be obtained at the same time which minimizes motion artefacts and allows an exact imaging fusion, which is especially important in anatomically complex regions like the pelvis. The associations between functional parameters from MRI and 18F-FDG-PET reflecting different tumor aspects are complex with inconclusive results in cervical cancer. The present study correlates histogram analysis and 18F-FDG-PET parameters derived from simultaneous FDG-PET/MRI in cervical cancer. Overall, 18 female patients (age range: 32–79 years) with histopathologically confirmed squamous cell cervical carcinoma were retrospectively enrolled. All 18 patients underwent a whole-body simultaneous 18F-FDG-PET/MRI, including diffusion-weighted imaging (DWI) using b-values 0 and 1000 s/mm2. Apparent diffusion coefficient (ADC) histogram parameters included several percentiles, mean, min, max, mode, median, skewness, kurtosis, and entropy. Furthermore, mean and maximum standardized uptake values (SUVmean and SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were estimated. No statistically significant correlations were observed between SUVmax or SUVmean and ADC histogram parameters. TLG correlated inversely with p25 (r=−0.486, P=0.041), p75 (r=−0.490, P=0.039), p90 (r=−0.513, P=0.029), ADC median (r=−0.497, P=0.036), and ADC mode (r=−0.546, P=0.019). MTV also showed significant correlations with several ADC parameters: mean (r=−0.546, P=0.019), p10 (r=−0.473, P=0.047), p25 (r=−0.569, P=0.014), p75 (r=−0.576, P=0.012), p90 (r=−0.585, P=0.011), ADC median (r=−0.577, P=0.012), and ADC mode (r=−0.597, P=0.009). ADC histogram analysis and volume-based metabolic 18F-FDG-PET parameters are related to each other in cervical cancer.
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Traverso A, Wee L, Dekker A, Gillies R. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int J Radiat Oncol Biol Phys 2018; 102:1143-1158. [PMID: 30170872 PMCID: PMC6690209 DOI: 10.1016/j.ijrobp.2018.05.053] [Citation(s) in RCA: 474] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 05/15/2018] [Accepted: 05/20/2018] [Indexed: 12/15/2022]
Abstract
Purpose: An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or “radiomic features.” Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature. Methods and Materials: The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features. Results: Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible. Conclusions: Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features. The repeatability and reproducibility of radiomic features are sensitive at various degrees to image quality and to software used to extract radiomic features. Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. No consensus was found regarding the most repeatable and reproducible features with respect to different settings.
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Affiliation(s)
- Alberto Traverso
- Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.
| | - Leonard Wee
- Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
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Lucia F, Visvikis D, Desseroit MC, Miranda O, Malhaire JP, Robin P, Pradier O, Hatt M, Schick U. Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 2017; 45:768-786. [PMID: 29222685 DOI: 10.1007/s00259-017-3898-7] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 11/22/2017] [Indexed: 02/07/2023]
Abstract
PURPOSE The aim of this study is to determine if radiomics features from 18fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer. METHODS One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control. RESULTS In the training cohort, median follow-up was 3.0 years (range, 0.43-6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I-II vs. III-IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non UniformityGLRLM in PET and EntropyGLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50-60% for clinical parameters). CONCLUSIONS In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.
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Affiliation(s)
- François Lucia
- Radiation Oncology Department, University Hospital, Brest, France. .,Service de Radiothérapie, CHRU Morvan, 2 Avenue Foch, 29609, Cedex, Brest, France.
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | | | - Omar Miranda
- Radiation Oncology Department, University Hospital, Brest, France
| | | | - Philippe Robin
- Nuclear Medicine Department, University Hospital, Brest, France
| | - Olivier Pradier
- Radiation Oncology Department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
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Meng J, Zhu L, Zhu L, Xie L, Wang H, Liu S, Yan J, Liu B, Guan Y, He J, Ge Y, Zhou Z, Yang X. Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget 2017; 8:92442-92453. [PMID: 29190929 PMCID: PMC5696195 DOI: 10.18632/oncotarget.21374] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 08/28/2017] [Indexed: 01/30/2023] Open
Abstract
Purpose To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram and texture analysis in predicting tumor recurrence of advanced cervical cancer treated with concurrent chemo-radiotherapy (CCRT). Methods 36 women with pathologically confirmed advanced cervical squamous carcinomas were enrolled in this prospective study. 3.0 T pelvic MR examinations including diffusion weighted imaging (b = 0, 800 s/mm2) were performed before CCRT (pre-CCRT) and at the end of 2nd week of CCRT (mid-CCRT). ADC histogram and texture features were derived from the whole volume of cervical cancers. Results With a mean follow-up of 25 months (range, 11 ∼ 43), 10/36 (27.8%) patients ended with recurrence. Pre-CCRT 75th, 90th, correlation, autocorrelation and mid-CCRT ADCmean, 10th, 25th, 50th, 75th, 90th, autocorrelation can effectively differentiate the recurrence from nonrecurrence group with area under the curve ranging from 0.742 to 0.850 (P values range, 0.001 ∼ 0.038). Conclusions Pre- and mid-treatment whole-lesion ADC histogram and texture analysis hold great potential in predicting tumor recurrence of advanced cervical cancer treated with CCRT.
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Affiliation(s)
- Jie Meng
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Lijing Zhu
- The Comprehensive Cancer Centre of Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Li Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Li Xie
- The Comprehensive Cancer Centre of Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Huanhuan Wang
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Jing Yan
- The Comprehensive Cancer Centre of Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Baorui Liu
- The Comprehensive Cancer Centre of Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Yue Guan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, China
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, China
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia 30322, USA
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Liu S, Zheng H, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, Zhou Z. Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness. J Magn Reson Imaging 2017; 47:168-175. [PMID: 28471511 DOI: 10.1002/jmri.25752] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 04/13/2017] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To explore the role of whole-volume apparent diffusion coefficient (ADC)-based entropy parameters in the preoperative assessment of gastric cancer's aggressiveness. MATERIALS AND METHODS In all, 64 patients with gastric cancers who underwent 3.0T magnetic resonance imaging (MRI) were retrospectively included. Regions of interest were drawn manually using in-house software, around gastric cancer lesions on each slice of the diffusion-weighted images and ADC maps. Entropy-related parameters based on ADC maps were calculated automatically: (1) first-order entropy; (2-5) second-order entropies, including entropy(H)0 , entropy(H)45 , entropy(H)90 , and entropy(H)135 ; (6) entropy(H)mean ; and (7) entropy(H)range . Correlations between entropy-related parameters and pathological characteristics were analyzed with the Spearman correlation test. The parameters were compared among different pathological characteristics with independent-samples Kruskal-Wallis or Mann-Whitney U-test. Additionally, diagnostic performances of parameters in differentiating different pathological characteristics were analyzed by receiver operating characteristic (ROC) curve analysis. RESULTS All the entropy-related parameters significantly correlated with T, N, and overall stages, especially the first-order entropy (r = 0.588, 0.585, and 0.677, respectively, all P < 0.05). All the entropy-related parameters showed significant differences in gastric cancers at different T, N, and overall stages, as well as at different status of vascular invasion (P < 0.001-0.027). And four parameters, including entropy, entropy(H)0 , entropy(H)45 , and entropy(H)90 , showed significant differences between gastric cancers with and without perineural invasion (P 0.006-0.040). CONCLUSION Entropy-related parameters derived from whole-volume ADC texture analysis could help assess the aggressiveness of gastric cancers via analyzing intratumoral heterogeneity quantitatively, especially the first-order entropy. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:168-175.
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Affiliation(s)
- Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Huanhuan Zheng
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Yujuan Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Ling Chen
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Wenxian Guan
- Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Yue Guan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, P.R. China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, P.R. China
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
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ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases—a Preliminary Study. Mol Imaging Biol 2017; 19:953-962. [DOI: 10.1007/s11307-017-1073-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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