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Zhang J, Yu X, Zhang X, Chen S, Song Y, Xie L, Chen Y, Ouyang H. Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions. BMC Med Imaging 2022; 22:139. [PMID: 35941559 PMCID: PMC9358891 DOI: 10.1186/s12880-022-00864-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/29/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively. METHODS One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models. RESULTS Stage IA EC showed lower ADC10th, ADC90th, ADCmin, ADCmax, ADCmean, ADCmedian, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADCmedian yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10-3 mm2/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADCmin and ADC10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05). CONCLUSIONS Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.
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Affiliation(s)
- Jieying Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaoduo Yu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xiaomiao Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Shuang Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yan Song
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, 100176, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Han Ouyang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
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Ram Kim B, Kang Y, Lee J, Choi D, Joon Lee K, Mo Ahn J, Lee E, Woo Lee J, Sik Kang H. Tumor grading of soft tissue sarcomas: assessment with whole-tumor histogram analysis of apparent diffusion coefficient. Eur J Radiol 2022; 151:110319. [DOI: 10.1016/j.ejrad.2022.110319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 11/28/2022]
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Clemente EJI, Navarro OM, Navallas M, Ladera E, Torner F, Sunol M, Garraus M, March JC, Barber I. Multiparametric MRI evaluation of bone sarcomas in children. Insights Imaging 2022; 13:33. [PMID: 35229206 PMCID: PMC8885969 DOI: 10.1186/s13244-022-01177-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 02/07/2022] [Indexed: 12/22/2022] Open
Abstract
Osteosarcoma and Ewing sarcoma are the most common bone sarcomas in children. Their clinical presentation is very variable depending on the age of the patient and tumor location. MRI is the modality of choice to assess these bone sarcomas and has an important function at diagnosis and also for monitoring recurrence or tumor response. Anatomic sequences include T1- and T2-weighted images and provide morphological assessment that is crucial to localize the tumor and describe anatomical boundaries. Multiparametric MRI provides functional information that helps in the assessment of tumor response to therapy by using different imaging sequences and biomarkers. This review manuscript illustrates the role of MRI in osteosarcoma and Ewing sarcoma in the pediatric population, with emphasis on a functional perspective, highlighting the use of diffusion-weighted imaging and dynamic contrast-enhanced MRI at diagnosis, and during and after treatment.
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Affiliation(s)
- Emilio J Inarejos Clemente
- Department of Diagnostic Imaging. Hospital Sant Joan de Déu, Av. Sant Joan de Déu, 2, CP:08950, Esplugues de Llobregat, Barcelona, Spain.
| | - Oscar M Navarro
- Department of Medical Imaging, Department of Diagnostic Imaging, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
| | - Maria Navallas
- Department of Diagnostic Imaging, Hospital 12 de Octubre, Madrid, Spain
| | - Enrique Ladera
- Department of Diagnostic Imaging. Hospital Sant Joan de Déu, Av. Sant Joan de Déu, 2, CP:08950, Esplugues de Llobregat, Barcelona, Spain
| | - Ferran Torner
- Department of Orthopaedics, Hospital Sant Joan de Déu. Av, Sant Joan de Déu, 2, CP:08950, Esplugues de Llobregat, Barcelona, Spain
| | - Mariona Sunol
- Department of Pathology, Hospital Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Moira Garraus
- Department of Oncology, Hospital Sant Joan de Déu. Av, Sant Joan de Déu, 2, CP:08950, Esplugues de Llobregat, Barcelona, Spain
| | - Jordi Català March
- Department of Radiology, Instituto de Resonancia Magnetica Guirado, C/Muntaner, 531, CP:08022, Barcelona, Spain
| | - Ignasi Barber
- Department of Diagnostic Imaging. Hospital Sant Joan de Déu, Av. Sant Joan de Déu, 2, CP:08950, Esplugues de Llobregat, Barcelona, Spain
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.
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Fang S, Yang Y, Xu N, Tu Y, Yin Z, Zhang Y, Liu Y, Duan Z, Liu W, Wang S. An Update in Imaging Evaluation of Histopathological Grade of Soft Tissue Sarcomas Using Structural and Quantitative Imaging and Radiomics. J Magn Reson Imaging 2021; 55:1357-1375. [PMID: 34637568 DOI: 10.1002/jmri.27954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 12/22/2022] Open
Abstract
Over the past two decades, considerable efforts have been made to develop non-invasive methods for determining tumor grade or surrogates for predicting the biological behavior, aiding early treatment decisions, and providing prognostic information. The development of new imaging tools, such as diffusion-weighted imaging, diffusion kurtosis imaging, perfusion imaging, and magnetic resonance spectroscopy have provided leverage in the diagnosis of soft tissue sarcomas. Artificial intelligence is a new technology used to study and simulate human thinking and abilities, which can extract and analyze advanced and quantitative image features from medical images with high throughput for an in-depth characterization of the spatial heterogeneity of tumor tissues. This article reviews the current imaging modalities used to predict the histopathological grade of soft tissue sarcomas and highlights the advantages and limitations of each modality. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Shaobo Fang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Yanyu Yang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Nan Xu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Yun Tu
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhenzhen Yin
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Yu Zhang
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Yajie Liu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Zhiqing Duan
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Wenyu Liu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Shaowu Wang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
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Wu W, Zhou S, Hippe DS, Liu H, Wang Y, Mayr NA, Yuh WT, Xia L, Bowen SR. Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer. Acad Radiol 2021; 28:e27-e34. [PMID: 32102748 DOI: 10.1016/j.acra.2020.01.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/17/2020] [Accepted: 01/18/2020] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer. MATERIALS AND METHODS This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [Tmax] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs). RESULTS There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower Tmax (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either Tmax (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011). CONCLUSION This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.
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A Comparative Study of 2 Different Segmentation Methods of ADC Histogram for Differentiation Genetic Subtypes in Lower-Grade Diffuse Gliomas. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9549361. [PMID: 33062706 PMCID: PMC7539099 DOI: 10.1155/2020/9549361] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/03/2020] [Accepted: 09/15/2020] [Indexed: 01/04/2023]
Abstract
Background To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better. Materials and Methods We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDHwt, 16cases); IDH mutant with the intact 1p or 19q group (IDHmut/1p19q+, 18cases); and IDH mutant with the 1p/19q codeleted group (IDHmut/1p19q-, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent t test or Mann-Whitney U test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden's index. Area under the curve (AUC) results were compared using the method of Delong et al. Results The inhomogenity from the two different ROI methods for distinguishing IDHwt gliomas from IDHmut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDHmut/1p19q- tumors and IDHmut/1p19q+ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods. Conclusion ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas.
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Chen X, Lin L, Wu J, Yang G, Zhong T, Du X, Chen Z, Xu G, Song Y, Xue Y, Duan Q. Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging. Acta Radiol 2020; 61:1228-1239. [PMID: 31986895 DOI: 10.1177/0284185119898656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. PURPOSE To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. MATERIAL AND METHODS A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann-Whitney U test, Kruskal-Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. RESULTS Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index (P < 0.05). CONCLUSION The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.
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Affiliation(s)
- Xiaodan Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, PR China
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Jie Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Tianjin Zhong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Xiaoqiang Du
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Zhiyong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Ganggang Xu
- Department of Management Science, University of Miami, Coral Gables, FL, USA
| | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Qing Duan
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
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Valenzuela RF, Kundra V, Madewell JE, Costelloe CM. Advanced Imaging in Musculoskeletal Oncology: Moving Away From RECIST and Embracing Advanced Bone and Soft Tissue Tumor Imaging (ABASTI) - Part I - Tumor Response Criteria and Established Functional Imaging Techniques. Semin Ultrasound CT MR 2020; 42:201-214. [PMID: 33814106 DOI: 10.1053/j.sult.2020.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
According to the Revised Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, the majority of bone metastases are considered to be nonmeasurable disease. Traditional response criteria rely on physical measurements. New criteria would be valuable if they incorporated newly developed imaging features in order to provide a more comprehensive assessment of oncological status. Advanced magnetic resonance imaging (MRI) sequences such as diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) with dynamic contrast-enhanced (DCE) perfusion imaging are reviewed in the context of the initial and post-therapeutic assessment of musculoskeletal tumors. Particular attention is directed to the pseudoprogression phenomenon in which a successfully treated tumor enlarges from the pretherapeutic baseline, followed by regression without a change in therapy.
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Affiliation(s)
- Raul Fernando Valenzuela
- The University of Texas MD Anderson Cancer Center, Department of Musculoskeletal Imaging, Houston, Texas.
| | - Vikas Kundra
- The University of Texas MD Anderson Cancer Center, Department of Musculoskeletal Imaging, Houston, Texas
| | - John E Madewell
- The University of Texas MD Anderson Cancer Center, Department of Musculoskeletal Imaging, Houston, Texas
| | - Colleen M Costelloe
- The University of Texas MD Anderson Cancer Center, Department of Musculoskeletal Imaging, Houston, Texas
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Inarejos Clemente EJ, Navallas M, Barber Martínez de la Torre I, Suñol M, Munuera Del Cerro J, Torner F, Garraus M, Navarro OM. MRI of Rhabdomyosarcoma and Other Soft-Tissue Sarcomas in Children. Radiographics 2020; 40:791-814. [PMID: 32243230 DOI: 10.1148/rg.2020190119] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Soft-tissue sarcomas in children comprise a heterogeneous group of entities with variable manifestation depending on the age of the patient and the location of the tumor. MRI is the modality of choice for evaluating musculoskeletal soft-tissue tumors and plays a paramount role in both initial diagnosis and assessment of tumor response during and after treatment. Conventional MRI sequences, such as T1- and T2-weighted imaging, offer morphologic information, which is important for localizing the lesion and describing anatomic relationships but not accurate for determining its malignant or benign nature and may be limited in differentiating tumor response from therapy-related changes. Advanced multiparametric MRI offers further functional information that can help with these tasks by using different imaging sequences and biomarkers. The authors present the role of MRI in rhabdomyosarcoma and other soft-tissue sarcomas in children, emphasizing a multiparametric approach with focus on the utility and potential added value of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI in characterization and staging, determination of pretreatment extent, and evaluation of tumor response and recurrence after treatment. ©RSNA, 2020.
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Affiliation(s)
- Emilio J Inarejos Clemente
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - María Navallas
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - Ignasi Barber Martínez de la Torre
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - Mariona Suñol
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - Josep Munuera Del Cerro
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - Ferran Torner
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - Moira Garraus
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
| | - Oscar M Navarro
- From the Departments of Diagnostic Imaging (E.J.I.C., M.N., I.B.M.d.l.T., J.M.d.C.), Pathology (M.S.), Orthopaedics (F.T.), and Oncology and Haematology (M.G.), Hospital Sant Joan de Déu, Av Sant Joan de Déu 2, 08950 Esplugues de Llobregat (Barcelona), Spain; Department of Medical Imaging, University of Toronto, Toronto, Ont, Canada (O.M.N.); and Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ont, Canada (O.M.N.)
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Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters. Acad Radiol 2020; 27:342-353. [PMID: 31151902 DOI: 10.1016/j.acra.2019.04.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/08/2019] [Accepted: 04/16/2019] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the usefulness of intravoxel incoherent motion (IVIM) histogram analysis for differentiating low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs). MATERIALS AND METHODS Fifty-nine patients with pathologically confirmed meningiomas (45 LGMs and 14 HGMs) underwent IVIM MR imaging. Maps of IVIM parameters (perfusion fraction, f; true diffusion coefficient, D; and pseudo diffusion coefficient, D*), as well as of the apparent diffusion coefficient (ADC), were generated. Histogram analysis was performed using parametric values from all voxels in regions-of-interest manually drawn to encompass the whole tumor. The histogram results of ADC and IVIM parameters were compared using the Mann-Whitney U test. Area under the receiver operating characteristic curve (AUC) values were generated to evaluate how well each parameter could differentiate LGMs from HGMs. Spearman's rank correlation coefficients were used to evaluate correlations between histogram parameters and Ki-67 expression. RESULTS Compared to LGM, HGM showed significantly higher standard deviation (SD), variance, and coefficient of variation (CV) of ADC (p< 0.006-0.028; AUC, 0.693-0.748), D (p< 0.004-0.032; AUC, 0.670-0.752), and significantly higher CV of f (p< 0.005-0.024; AUC = 0.737). Means and percentiles of ADC and IVIM parameters did not differ significantly between LGM and HGM. Significant positive correlations were identified between Ki-67 and histogram parameters of ADC (SD, variance, kurtosis, skewness, and CV) and D (SD, variance, kurtosis, and CV), whereas no significant correlation with Ki-67 was shown for mean or percentiles of ADC and IVIM parameters. CONCLUSION Heterogeneity histogram parameters of ADC, D, and f may be useful for differentiating LGMs from HGMs.
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Meyer HJ, Gundermann P, Höhn AK, Hamerla G, Surov A. Associations between whole tumor histogram analysis parameters derived from ADC maps and expression of EGFR, VEGF, Hif 1-alpha, Her-2 and Histone 3 in uterine cervical cancer. Magn Reson Imaging 2018; 57:68-74. [PMID: 30367998 DOI: 10.1016/j.mri.2018.10.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 09/23/2018] [Accepted: 10/22/2018] [Indexed: 12/09/2022]
Abstract
OBJECTIVE Diffusion weighted imaging (DWI) can be quantified by apparent diffusion coefficient (ADC) and can predict tissue microstructure. The aim of the present study was to analyze possible associations between ADC histogram based parameters with different histopathological parameters in cervical squamous cell carcinoma. MATERIALS AND METHODS 18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled. In all cases, pelvic MRI was performed with a DWI (b-values 0 and 1000 s/mm2). Histogram analysis was performed as a whole lesion measurement. Histopathological parameters included expression of EGFR, VEGF, Hif1-alpha, Her2 and Histone 3. Spearman's correlation coefficient was used to analyze associations between investigated parameters. RESULTS Analyze of the investigated ADC histogram parameters showed a good interreader variability, ranging from 0.705 for entropy to 0.959 for ADCmedian. EGFR expression correlated statistically significant with several histogram parameters. The highest correlation was observed for p75 (p = -0.562, P = 0.015). There were several correlations with histone 3, the highest with p25 (p = -0.610, P = 0.007). None of the ADC related parameters correlated statistically significant with expression of VEGF, Hif1-alpha and Her2. CONCLUSION Histogram analysis showed a good interreader agreement. ADC histogram parameters might be able to reflect expression of EGFR and histone 3 in cervical squamous cell carcinomas, but not expression of VEGF, Hif1-alpha and Her2.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstraße 20, D-04103 Leipzig, Germany.
| | - Peter Gundermann
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstraße 20, D-04103 Leipzig, Germany.
| | - Anne Kathrin Höhn
- Department of Pathology, University of Leipzig, Liebigstraße 20, D-04103 Leipzig, Germany.
| | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstraße 20, D-04103 Leipzig, Germany.
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstraße 20, D-04103 Leipzig, Germany.
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Meyer HJ, Pazaitis N, Surov A. ADC histogram analysis of muscle lymphoma-correlation with histopathology in a rare entity. Br J Radiol 2018; 91:20180291. [PMID: 29927638 DOI: 10.1259/bjr.20180291] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE: Diffusion-weighted imaging is able to reflect histopathology architecture. A novel imaging approach, namely histogram analysis, is used to further characterize lesion on MRI. To correlate histogram parameters derived from apparent diffusion coefficient (ADC) maps with histopathology parameters in muscle lymphoma. METHODS: Eight patients (mean age 64.8 years, range 45-72 years) with histopathologically confirmed muscle lymphoma were retrospectively identified. Cell count, total nucleic and average nucleic areas were estimated using ImageJ. Additionally, Ki67-index was calculated. Diffusion-weightedimaging was obtained on a 1.5 T scanner by using the b-values of 0 and 1000 s mm-2. Histogram analysis was performed as a whole lesion measurement by using a custom-made Matlab-based application. RESULTS: All ADC parameters showed a good to excellent interreader variability. Cell count correlated well with ADCmean (ρ = -0.76, p = 0.03) and ADCp75 (ρ =-0.79, p = 0.02). Kurtosis and entropy correlated with average nucleic area (ρ = -0.81, p = 0.02, ρ =0.88, p = 0.007, respectively). None of the analyzed ADC parameters correlated with total nucleic area and with Ki67-index. CONCLUSION: ADC histogram analysis parameters can reflect cellularity in muscle lymphoma. ADVANCES IN KNOWLEDGE: Histogram parameters derived from ADC maps can reflect several different cellularity parameters in muscle lymphoma.
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Affiliation(s)
- Hans-Jonas Meyer
- 1 Department of Diagnostic and Interventional Radiology, University of Leipzig , Leipzig , Germany
| | - Nikolaos Pazaitis
- 2 Department of Pathology, Martin-Luther-University Halle-Wittenberg , Halle , Germany
| | - Alexey Surov
- 1 Department of Diagnostic and Interventional Radiology, University of Leipzig , Leipzig , Germany
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