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Bae J, Tan Z, Solomon E, Huang Z, Heacock L, Moy L, Knoll F, Kim SG. Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods. Magn Reson Med 2024; 92:1728-1742. [PMID: 38775077 DOI: 10.1002/mrm.30152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of theB 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters. RESULTS The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy ofv p $$ {v}_p $$ andPS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction forB 1 + $$ {\mathrm{B}}_1^{+} $$ field. CONCLUSION We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York, USA
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Zhengguo Tan
- Biomedical Engineering, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany
| | - Eddy Solomon
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Zhengnan Huang
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York, USA
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
| | - Florian Knoll
- Biomedical Engineering, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany
| | - Sungheon Gene Kim
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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Zhang J, Zheng Y, Li L, Wang R, Jiang W, Ai K, Gan T, Wang P. Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer. Magn Reson Imaging 2024; 113:110204. [PMID: 38971263 DOI: 10.1016/j.mri.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/14/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024]
Abstract
PURPOSE To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. METHODS This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. RESULTS Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05). CONCLUSION IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.
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Affiliation(s)
- Jing Zhang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China.
| | - Yurong Zheng
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Li Li
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Rui Wang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Weilong Jiang
- Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, Gansu 730000, China
| | - Kai Ai
- Philips Healthcare, Xi'an, China
| | - Tiejun Gan
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China
| | - Pengfei Wang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
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Li Z, Huang H, Zhao Z, Ma W, Mao H, Liu F, Yang Y, Wang D, Lu Z. Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer. Acad Radiol 2024:S1076-6332(24)00300-3. [PMID: 38816315 DOI: 10.1016/j.acra.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024]
Abstract
RATIONALE AND OBJECTIVES The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC. MATERIALS AND METHODS A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves. RESULTS Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. CONCLUSION The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.
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Affiliation(s)
- Zhiheng Li
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Huizhen Huang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Haijia Mao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Fang Liu
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Ye Yang
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.
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Zhou M, Huang H, Fan Y, Chen M, Li M, Wang Y. The application of quantitative perfusion analysis of golden-angle radial sparse parallel MRI and R2∗ value for predicting pathological prognostic factors in rectal cancer. Clin Radiol 2024; 79:124-132. [PMID: 38030505 DOI: 10.1016/j.crad.2023.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
AIM To investigate the diagnostic value of golden-angle radial sparse parallel magnetic resonance imaging (MRI) (GRASP) and R2∗ in predicting the prognostic factors of resectable rectal cancer. MATERIALS AND METHODS A total of 108 patients with rectal adenocarcinoma were included in this retrospective study. The volume transfer constant (Ktrans), rate constant (Kep), plasma volume fraction (Ve), and R2∗ were obtained. Univariate and multivariate logistic regression were conducted. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the imaging parameters. RESULTS The Ktrans was found to be significantly higher in rectal cancers with positive lymph node metastasis (LNM), higher tumour grade, positive lymphovascular invasion (LVI), and higher ki-67 (all p<0.05). The Kep was also significantly higher in the LNM-positive group (p<0.001), while the R2∗ was higher in rectal cancers with LNM-positive, higher tumour grade, LVI-positive, and higher ki-67 (all p<0.05). Combining the Ktrans and R2∗ provided the highest area under the ROC curve (AUC) for LNM-positive and higher ki-67 tumours differentiation (0.790 and 0.823, respectively). DISCUSSION Combining quantitative parameters of the Ktrans and R2∗ could be used to non-invasively predict pathological prognostic factors preoperatively.
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Affiliation(s)
- M Zhou
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - H Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Y Fan
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - M Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, China
| | - M Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Y Wang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
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Jiang W, Meng R, Cheng Y, Wang H, Han T, Qu N, Yu T, Hou Y, Xu S. Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer. J Magn Reson Imaging 2024; 59:613-625. [PMID: 37199241 DOI: 10.1002/jmri.28776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. PURPOSE To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. STUDY TYPE Retrospective. POPULATION Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). ASSESSMENT Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC. STATISTICAL TESTS Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram. RESULTS A total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. DATA CONCLUSION The constructed preoperative nomogram might effectively assess LVI. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Ruiqing Meng
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Yuan Cheng
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Tingting Han
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ning Qu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shu Xu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
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Zhang L, Fan M, Li L. Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:13-24. [PMID: 38343210 DOI: 10.1007/s10278-023-00915-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 03/02/2024]
Abstract
Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Computer and Information, Anqing Normal University, Anqing, 246133, China
| | - Ming Fan
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Zhang L, Fan M, Li L. Efficient estimation of pharmacokinetic parameters from breast dynamic contrast-enhanced MRI based on a convolutional neural network for predicting molecular subtypes. Phys Med Biol 2023; 68:245001. [PMID: 37983902 DOI: 10.1088/1361-6560/ad0e39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023]
Abstract
Objective. Tracer kinetic models allow for estimating pharmacokinetic (PK) parameters, which are related to pathological characteristics, from breast dynamic contrast-enhanced magnetic resonance imaging. However, existing tracer kinetic models subject to inaccuracy are time-consuming for PK parameters estimation. This study aimed to accurately and efficiently estimate PK parameters for predicting molecular subtypes based on convolutional neural network (CNN).Approach. A CNN integrating global and local features (GL-CNN) was trained using synthetic data where known PK parameters map was used as the ground truth, and subsequently used to directly estimate PK parameters (volume transfer constantKtransand flux rate constantKep) map. The accuracy assessed by the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and concordance correlation coefficient (CCC) was compared between the GL-CNN and Tofts-based PK parameters in synthetic data. Radiomic features were calculated from the PK parameters map in 208 breast tumors. A random forest classifier was constructed to predict molecular subtypes using a discovery cohort (n= 144). The diagnostic performance evaluated on a validation cohort (n= 64) using the area under the receiver operating characteristic curve (AUC) was compared between the GL-CNN and Tofts-based PK parameters.Main results. The average PSNR (48.8884), SSIM (0.9995), and CCC (0.9995) between the GL-CNN-basedKtransmap and ground truth were significantly higher than those between the Tofts-basedKtransmap and ground truth. The GL-CNN-basedKtransobtained significantly better diagnostic performance (AUCs = 0.7658 and 0.8528) than the Tofts-basedKtransfor luminal B and HER2 tumors. The GL-CNN method accelerated the computation by speed approximately 79 times compared to the Tofts method for the whole breast of all patients.Significance. Our results indicate that the GL-CNN method can be used to accurately and efficiently estimate PK parameters for predicting molecular subtypes.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
- School of Computer and Information, Anqing Normal University, Anqing, 246133, People's Republic of China
| | - Ming Fan
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
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Uncu UY, Aydin Aksu S. Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study. Diagnostics (Basel) 2023; 13:3260. [PMID: 37892081 PMCID: PMC10606869 DOI: 10.3390/diagnostics13203260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.
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Affiliation(s)
- Ulas Yalim Uncu
- Department of Radiology, Van Training and Research Hospital, University of Health Sciences, 65300 Van, Turkey
| | - Sibel Aydin Aksu
- Department of Radiology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, 34668 Istanbul, Turkey;
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Yue XN, He XY, Wu JJ, Fan W, Zhang HJ, Wang CW. Endometrioid adenocarcinoma: combined multiparametric MRI and tumour marker HE4 to evaluate tumour grade and lymphovascular space invasion. Clin Radiol 2023; 78:e574-e581. [PMID: 37183140 DOI: 10.1016/j.crad.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/06/2022] [Accepted: 04/17/2023] [Indexed: 05/16/2023]
Abstract
AIM To assess the value of semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and quantitative diffusion-weighted imaging parameters combined with human epididymis protein 4 (HE4) in predicting the pathological grade and lymphovascular space invasion (LVSI) of endometrioid adenocarcinoma (EAC). MATERIALS AND METHODS Between October 2018 and December 2021, 60 women (mean age, 55 [range, 32-77] years) with EAC underwent preoperative pelvic MRI and HE4 level measurements. The positive enhancement integral (PEI), time to peak, maximum slope of increase (MSI), and maximum slope of decrease were measured by manually drawing a region of interest on the neoplastic tissue. The receiver operating characteristic curve was used to calculate the diagnostic efficiency of the single parameter and combined factors. RESULTS Lower apparent diffusion coefficients (ADCs) were observed in high-grade tumours (G3) than in low-grade tumours (G1/G2). PEI, MSI, and HE4 levels were higher in the high-grade tumours than in the low-grade tumours (p<0.05). The area under the curve (AUC) for G3 diagnosis using multiparametric MRI combined with HE4 was 0.929. ADC values were significantly lower in the EAC with LVSI than in those without LVSI. Tumours with LVSI showed higher PEI and HE4 levels than those without LVSI (p<0.05). The AUC for LVSI-positive diagnosis using multiparametric MRI combined with HE4 was 0.814. CONCLUSION Semi-quantitative DCE-MRI, ADC values, and serum HE4 levels can be used to predict tumour grade and LVSI, and the prediction efficiency of multiparametric MRI combined with serum HE4 is better than that of any single factor.
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Affiliation(s)
- X N Yue
- Department of CT/MRI, First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, 832000, China
| | - X Y He
- Department of CT/MRI, First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, 832000, China
| | - J J Wu
- Department of CT/MRI, First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, 832000, China
| | - W Fan
- Department of CT/MRI, First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, 832000, China
| | - H J Zhang
- Department of Pathology, First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, 832000, China
| | - C W Wang
- Department of CT/MRI, First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, 832000, China.
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Huang H, Li Z, Xia Y, Zhao Z, Wang D, Jin H, Liu F, Yang Y, Shen L, Lu Z. Association between radiomics features of DCE-MRI and CD8 + and CD4 + TILs in advanced gastric cancer. Pathol Oncol Res 2023; 29:1611001. [PMID: 37342362 PMCID: PMC10277864 DOI: 10.3389/pore.2023.1611001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer. Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density. Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630-0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = -0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = -0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856). Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.
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Affiliation(s)
- Huizhen Huang
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhiheng Li
- Department of Radiology, Anhui Provincial Hospital, Hefei, China
| | - Yue Xia
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hongyan Jin
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Fang Liu
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ye Yang
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Liyijing Shen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
- The First Affiliated Hospital of Shaoxing University, Shaoxing, China
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Wu J, Liang Z, Deng X, Xi Y, Feng X, Yao Z, Shu Z, Xie Q. Glioma grade discrimination with dynamic contrast-enhanced MRI: An accurate analysis based on MRI guided stereotactic biopsy. Magn Reson Imaging 2023; 99:91-97. [PMID: 36803634 DOI: 10.1016/j.mri.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/05/2023] [Accepted: 02/06/2023] [Indexed: 02/17/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis. METHODS Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (Ktrans), volume of extravascular-extracellular space (ve), fractional plasma volume (fpv), and reflux transfer rate (kep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve. RESULTS Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in Ktrans and ve were observed between grades except ve between grade 2 and 3. Ktrans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). Ve indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively). CONCLUSION Our study had identified Ktrans, ve and the combination of parameters to be an accurate predictor for grading glioma.
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Affiliation(s)
- Juan Wu
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China
| | - Zonghui Liang
- Department of Radiology, Jing'an District Centre Hospital, Fudan University, NO. 266 Xikang Road, Shanghai 200040, PR China
| | - Xiaofei Deng
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China
| | - Yan Xi
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China
| | - Xiaoyuan Feng
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai 200040, PR China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai 200040, PR China.
| | - Zheng Shu
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China.
| | - Qian Xie
- Department of Radiology, Jing'an District Centre Hospital, Fudan University, NO. 266 Xikang Road, Shanghai 200040, PR China.
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Cao J, Pickup S, Rosen M, Zhou R. Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer. Mol Imaging Biol 2023:10.1007/s11307-023-01824-7. [PMID: 37166575 DOI: 10.1007/s11307-023-01824-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 05/12/2023]
Abstract
PURPOSE We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known. PROCEDURES Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment. RESULTS Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining. CONCLUSION Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.
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Affiliation(s)
- Jianbo Cao
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Current address: Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rong Zhou
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Liu Y, Wang S, Qu J, Tang R, Wang C, Xiao F, Pang P, Sun Z, Xu M, Li J. High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4. BMC Med Imaging 2023; 23:58. [PMID: 37076817 PMCID: PMC10116788 DOI: 10.1186/s12880-023-01015-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.
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Affiliation(s)
- Yufeng Liu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingjing Qu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Rui Tang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chundan Wang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Fengchun Xiao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Peipei Pang
- GE Healthcare, Precision Health Institution, Hangzhou, China
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
| | - Jiaying Li
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases. Eur Radiol 2023:10.1007/s00330-023-09437-y. [PMID: 36735042 DOI: 10.1007/s00330-023-09437-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To investigate the correlation of conventional MRI, DCE-MRI and clinical features with pain response after stereotactic body radiotherapy (SBRT) in patients with spinal metastases and establish a pain response prediction model. METHODS Patients with spinal metastases who received SBRT in our hospital from July 2018 to April 2022 consecutively were enrolled. All patients underwent conventional MRI and DCE-MRI before treatment. Pain was assessed before treatment and in the third month after treatment, and the patients were divided into pain-response and no-pain-response groups. A multivariate logistic regression model was constructed to obtain the odds ratio and 95% confidence interval (CI) for each variable. C-index was used to evaluate the model's discrimination performance. RESULTS Overall, 112 independent spinal lesions in 89 patients were included. There were 73 (65.2%) and 39 (34.8%) lesions in the pain-response and no-pain-response groups, respectively. Multivariate analysis showed that the number of treated lesions, pretreatment pain score, Karnofsky performance status score, Bilsky grade, and the DCE-MRI quantitative parameter Ktrans were independent predictors of post-SBRT pain response in patients with spinal metastases. The discrimination performance of the prediction model was good; the C index was 0.806 (95% CI: 0.721-0.891), and the corrected C-index was 0.754. CONCLUSION Some imaging and clinical features correlated with post-SBRT pain response in patients with spinal metastases. The model based on these characteristics has a good predictive value and can provide valuable information for clinical decision-making. KEY POINTS • SBRT can accurately irradiate spinal metastases with ablative doses. • Predicting the post-SBRT pain response has important clinical implications. • The prediction models established based on clinical and MRI features have good performance.
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Wang W, Fan X, Yang J, Wang X, Gu Y, Chen M, Jiang Y, Liu L, Zhang M. Preliminary MRI Study of Extracellular Volume Fraction for Identification of Lymphovascular Space Invasion of Cervical Cancer. J Magn Reson Imaging 2023; 57:587-597. [PMID: 36094153 DOI: 10.1002/jmri.28423] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Lymphovascular space invasion (LVSI) is a risk factor for poor prognosis of cervical cancer. Preoperative identification of LVSI is very difficult. PURPOSE To evaluate the potential of extracellular volume (ECV) fraction based on T1 mapping in preoperative identification of LVSI in cervical cancer compared with dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE Retrospective. SUBJECTS A total of 79 patients (median age 54 years) with cervical cancer were classified into LVSI group (n = 29) and without LVSI group (n = 50) according to postoperative pathology. FIELD STRENGTH/SEQUENCE A 3-T, noncontrast and contrast-enhanced T1 mapping performed with volume interpolated breath hold examination (VIBE) sequence, DCE-MRI applied with 3D T1-weighted VIBE sequence. ASSESSMENT Regions of interest along the medial edge of the lesion were drawn on slices depicting the maximum cross-section of the tumor. The noncontrast and contrast-enhanced T1 value of the tumor and arteries in the same slice were measured, and ECV was calculated from T1 values. The parametric maps (Ktrans , kep , and ve ) derived from DCE-MRI standard Toft's model were evaluated. STATISTICAL TESTS ECV, Ktrans , kep , and ve between groups with and without LVSI were compared using Student's t-test. The receiver operating characteristic (ROC) curve and DeLong test were used to evaluate and compare the diagnostic performance of ECV, Ktrans , kep , and ve for differentiating LVSI. P < 0.05 was considered statistically significant. RESULTS The ECV and Ktrans of the LVSI group were significantly higher than that of non-LVSI group (52.86% vs. 36.77%, 0.239 vs. 0.176, respectively), and no significant differences in Kep or ve values were observed (P = 0.071 and P = 0.168, respectively). The ECV fraction showed significantly higher area under ROC curve than Ktrans for differentiating LVSI (0.874 vs. 0.655, respectively). DATA CONCLUSION ECV measurements based on T1 mapping might improve the discrimination between patients with and without LVSI in cervical cancer, showing better performance for this purpose than DCE-MRI. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Wei Wang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Xiaofei Fan
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Jie Yang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Xuemei Wang
- Department of Pathology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Yu Gu
- Department of Pathology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Mingxin Chen
- Inpatient Service Center, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Yueluan Jiang
- MR Scientific Marketing, Diagnostic Imaging, Siemens Healthineers Ltd., Beijing, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Mengchao Zhang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
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Mattusch C, Bick U, Michallek F. Development and validation of a four-dimensional registration technique for DCE breast MRI. Insights Imaging 2023; 14:17. [PMID: 36701001 PMCID: PMC9880129 DOI: 10.1186/s13244-022-01362-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/19/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI. RESULTS After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient Ktrans as surrogate parameter for motion artifacts. Mean Ktrans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001). CONCLUSIONS Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.
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Affiliation(s)
- Chiara Mattusch
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Ulrich Bick
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Florian Michallek
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117 Berlin, Germany ,grid.260026.00000 0004 0372 555XDepartment of Radiology, Mie University Graduate School of Medicine, Tsu, Japan
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Zhang J, Zhang Z, Mao N, Zhang H, Gao J, Wang B, Ren J, Liu X, Zhang B, Dou T, Li W, Wang Y, Jia H. Radiomics nomogram for predicting axillary lymph node metastasis in breast cancer based on DCE-MRI: A multicenter study. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:247-263. [PMID: 36744360 DOI: 10.3233/xst-221336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
OBJECTIVES This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. METHODS This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwent DCE-MRI examination before surgery in two hospitals. All patients had a defined ALN status based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets. RESULTS ALNM rates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone. CONCLUSIONS The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner.
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Affiliation(s)
- Jiwen Zhang
- Department of First Clinical Medicine, Shanxi Medical University, Taiyuan, China
| | - Zhongsheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Jing Gao
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Bin Wang
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jianlin Ren
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xin Liu
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Binyue Zhang
- Department of First Clinical Medicine, Shanxi Medical University, Taiyuan, China
| | - Tingyao Dou
- Department of First Clinical Medicine, Shanxi Medical University, Taiyuan, China
| | - Wenjuan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Yanhong Wang
- Department of Microbiology and immunology, Shanxi Medical University, Taiyuan, China
| | - Hongyan Jia
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
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Tang WJ, Yao W, Jin Z, Kong QC, Hu WK, Liang YS, Chen LX, Chen SY, Zhang QQ, Wei XH, Xu XD, Guo Y, Jiang XQ. Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging. J Magn Reson Imaging 2022; 56:1912-1923. [PMID: 35499275 DOI: 10.1002/jmri.28215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/20/2022] [Accepted: 04/20/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity. PURPOSE To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model. STUDY TYPE Prospective. ANIMAL MODEL A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC. FIELD STRENGTH/SEQUENCE A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm2 ) and T1-twist DCE acquisition series. ASSESSMENT DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists. STATISTICAL TESTS The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant. RESULTS The apparent diffusion coefficient (ADC), mean diffusivity (MD), Ktrans and Kep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and Ve values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = -0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The Ktrans and Kep values were significantly correlated with the CD31 level (Ktrans , r = 0.820; Kep , r = 0.683). DATA CONCLUSION DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Wen-Jie Tang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Wang Yao
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Zhe Jin
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Qing-Cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China
| | - Wen-Ke Hu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Yun-Shi Liang
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Lei-Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Si-Yi Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Qiong-Qiong Zhang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Xin-Hua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Xiang-Dong Xu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Xin-Qing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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Shen J, Zhang W, Zhu JJ, Xue L, Yuan M, Xu H, Xu XQ, Yu TF, Wu FY. Multiparametric Magnetic Resonance Imaging for Assessing Thymic Epithelial Tumors: Correlation With Pathological Subtypes and Clinical Stages. J Magn Reson Imaging 2022; 56:1487-1496. [PMID: 35417074 DOI: 10.1002/jmri.28198] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND World Health Organization classification and Masaoka-Koga stage are widely used for thymic epithelial tumors (TETs). Reduced field-of-view (rFOV) diffusion-weighed imaging (DWI) proved to improve the image quality. Dynamic contrast-enhanced (DCE) MRI was commonly used in evaluating tumors. PURPOSE To investigate the value of multiparametric MRI in evaluating TETs. STUDY TYPE Retrospective. SUBJECTS Eighty-seven participants including 38 low risk (52.08 ± 14.19 years), 30 high risk (52.40 ± 11.35 years), and 19 thymic carcinoma patients (59.76 ± 10.78 years). FIELD STRENGTH/SEQUENCE A 3 T, turbo spin echo imaging, echo planar imaging, volumetric interpolated breath-hold examination with radial acquisition trajectory. ASSESSMENT DCE-MRI and apparent diffusion coefficient (ADC) variables were compared. Diagnostic performances of single significant factor and combined model were compared. STATISTICAL TESTS Parameters were compared using one-way ANOVA or independent-samples t test. Logistic regression was employed to investigate the combined model. Receiver operating curves (ROC) and DeLong's test were used to compare the diagnostic efficiency. RESULTS ADC, Ktrans , and kep values were significantly different among low-risk, high-risk and carcinoma group (ADC, 1.279 ± 0.345 × 10-3 mm2 /sec, 0.978 ± 0.260 × 10-3 mm2 /sec, 0.661 ± 0.134 × 10-3 mm2 /sec; Ktrans 0.167 ± 0.071 min-1 , 0.254 ± 0.136 min-1 , 0.393 ± 0.110 min-1 ; kep 0.345 ± 0.113 min-1 , 0.560 ± 0.269 min-1 , 0.872 ± 0.149 min-1 ). They were significantly different for early stage and advanced stage (ADC, 1.270 ± 0.356 × 10-3 mm2 /sec vs. 0.845 ± 0.251 × 10-3 mm2 /sec; Ktrans 0.179 ± 0.092 min-1 vs. 0.304 ± 0.142 min-1 ; kep 0.370 ± 0.181 min-1 vs. 0.674 ± 0.362 min-1 ). The combination of them had highest diagnostic efficiency for WHO classification (AUC, 0.925; sensitivity, 83.7%; specificity, 89.5%), clinical stage (AUC, 0.879; sensitivity, 80.9%; specificity, 82.5%). DATA CONCLUSION Multiparametric MRI model may be useful for discriminating WHO classification and clinical stage of TETs. EVIDENCE LEVEL 4 TECHNICAL EFFICIENCY: Stage 2.
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Affiliation(s)
- Jie Shen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jia-Jia Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xue
- Department of Thoracic surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tong-Fu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Wang W, Lv S, Xun J, Wang L, Zhao F, Wang J, Zhou Z, Chen Y, Sun Z, Zhu L. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer. Eur J Radiol 2022; 154:110392. [DOI: 10.1016/j.ejrad.2022.110392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 11/16/2022]
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Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022; 12:980793. [PMID: 36119479 PMCID: PMC9471147 DOI: 10.3389/fonc.2022.980793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/04/2022] [Indexed: 12/27/2022] Open
Abstract
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
- *Correspondence: Meredith A. Jones,
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Rozwat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
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Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3156093. [PMID: 35915805 PMCID: PMC9338867 DOI: 10.1155/2022/3156093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/19/2022] [Accepted: 06/18/2022] [Indexed: 11/17/2022]
Abstract
Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group (
), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group (
). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference (
). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.
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Yu Q, Zhu Y, Huang R, Li Y, Song L, Zhang X, Tang M, Gu Q, Li P, Zhou N, Li Y. Diagnosis and differential diagnosis of dermatofibrosarcoma protuberans: Utility of high-resolution dynamic contrast-enhanced (DCE) MRI. Skin Res Technol 2022; 28:651-663. [PMID: 35639715 PMCID: PMC9907642 DOI: 10.1111/srt.13164] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 05/03/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Dermatofibrosarcoma protuberans (DFSP) is a kind of low-grade malignant spindle cell neoplasm, the diagnosis, and treatment, which have markedly attracted clinicians' attention for its repeated recurrence. High-resolution magnetic resonance imaging (HR-MRI) has shown unique capabilities in diagnosis of various cutaneous tumors. MATERIALS AND METHODS Data of 29 patients with clinically suspected DFSPs and undergoing dynamic contrast-enhanced (DCE) HR-MRI preoperatively were prospectively collected. The HR-MRI qualitative features were evaluated and compared. The DCE-associated quantitative parameters and the time-signal intensity curve (TIC) types were provided using DCE sequences. RESULTS A total of 7 DFSPs, nine dermatofibromas (DF, including four cases of cellular variant [CDF]), 12 keloids, and one nodular fasciitis were enrolled. DFSP showed the largest major diameter and the deepest depth. Five DFSPs (71.4%) showed ill-defined margins as well as infiltration of peripheral adipose. All DFSPs showed irregular shape. Most DFSPs presented hyperintensity on T2 WI (71.4%) and iso-intensity on T1 WI (85.7%). Six cases (85.7%) had significant enhancement, and six cases (85.7%) had homogeneous enhancement. There were significant differences of Ktrans , Kep , Ve and iAUC values among DFSPs, DFs, and keloids, and DFSP had the highest values for these parameters. Six DFSPs (85.7%) and four CDFs (100%) showed type-III TICs, while the other lesions showed type-Ⅰor type-Ⅱ TICs. CONCLUSIONS DCE-HR-MRI could show the growth characteristics of DFSPs, which was of great value for the diagnosis and differential diagnosis of DFSPs and was helpful for the determination of treatment options, thereby to improve the prognosis of patients.
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Affiliation(s)
- Qiuyu Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yueqian Zhu
- Department of Dermatology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Renjun Huang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yan Li
- Department of Dermatology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Linyi Song
- Department of Dermatology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaoping Zhang
- Department of Dermatology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Mengxiao Tang
- Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qinghua Gu
- Department of Radiology, Suzhou Yongding Hospital, Suzhou, China
| | - Ping Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Naihui Zhou
- Department of Dermatology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Lin CY, Yen YT, Huang LT, Chen TY, Liu YS, Tang SY, Huang WL, Chen YY, Lai CH, Fang YHD, Chang CC, Tseng YL. An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors. Diagnostics (Basel) 2022; 12:diagnostics12040889. [PMID: 35453937 PMCID: PMC9026802 DOI: 10.3390/diagnostics12040889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/18/2022] [Accepted: 03/31/2022] [Indexed: 12/10/2022] Open
Abstract
This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.
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Affiliation(s)
- Chia-Ying Lin
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (C.-Y.L.); (L.-T.H.); (Y.-S.L.)
| | - Yi-Ting Yen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
- Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Li-Ting Huang
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (C.-Y.L.); (L.-T.H.); (Y.-S.L.)
| | - Tsai-Yun Chen
- Division of Hematology and Oncology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan;
| | - Yi-Sheng Liu
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (C.-Y.L.); (L.-T.H.); (Y.-S.L.)
| | - Shih-Yao Tang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, Taiwan;
| | - Wei-Li Huang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
| | - Ying-Yuan Chen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
| | - Chao-Han Lai
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan;
| | - Yu-Hua Dean Fang
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Correspondence: (Y.-H.D.F.); (C.-C.C.)
| | - Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
- Correspondence: (Y.-H.D.F.); (C.-C.C.)
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
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Roytman M, Kim S, Glynn S, Thomas C, Lin E, Feltus W, Magge RS, Liechty B, Schwartz TH, Ramakrishna R, Karakatsanis NA, Pannullo SC, Osborne JR, Knisely JPS, Ivanidze J. PET/MR Imaging of Somatostatin Receptor Expression and Tumor Vascularity in Meningioma: Implications for Pathophysiology and Tumor Outcomes. Front Oncol 2022; 11:820287. [PMID: 35155210 PMCID: PMC8832502 DOI: 10.3389/fonc.2021.820287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Meningiomas, the most common primary intracranial tumor, are vascular neoplasms that express somatostatin receptor-2 (SSTR2). The purpose of this investigation was to evaluate if a relationship exists between tumor vascularity and SSTR2 expression, which may play a role in meningioma prognostication and clinical management. Materials and Methods Gallium-68-DOTATATE PET/MRI with dynamic contrast-enhanced (DCE) perfusion was prospectively performed. Clinical and demographic patient characteristics were recorded. Tumor volumes were segmented and superimposed onto parametric DCE maps including flux rate constant (Kep), transfer constant (Ktrans), extravascular volume fraction (Ve), and plasma volume fraction (Vp). Meningioma PET standardized uptake value (SUV) and SUV ratio to superior sagittal sinus (SUVRSSS) were recorded. Pearson correlation analyses were performed. In a random subset, analysis was repeated by a second investigator, and intraclass correlation coefficients (ICCs) were determined. Results Thirty-six patients with 60 meningiomas (20 WHO-1, 27 WHO-2, and 13 WHO-3) were included. Mean Kep demonstrated a strong significant positive correlation with SUV (r = 0.84, p < 0.0001) and SUVRSSS (r = 0.81, p < 0.0001). When stratifying by WHO grade, this correlation persisted in WHO-2 (r = 0.91, p < 0.0001) and WHO-3 (r = 0.92, p = 0.0029) but not WHO-1 (r = 0.26, p = 0.4, SUVRSSS). ICC was excellent (0.97–0.99). Conclusion DOTATATE PET/MRI demonstrated a strong significant correlation between tumor vascularity and SSTR2 expression in WHO-2 and WHO-3, but not WHO-1 meningiomas, suggesting biological differences in the relationship between tumor vascularity and SSTR2 expression in higher-grade meningiomas, the predictive value of which will be tested in future work.
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Affiliation(s)
- Michelle Roytman
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Sean Kim
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Shannon Glynn
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Charlene Thomas
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Eaton Lin
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Whitney Feltus
- Departments of Radiology, New York-Presbyterian Hospital/Columbia University Medical Center, New York, NY, United States
| | - Rajiv S. Magge
- Department of Neurology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Benjamin Liechty
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Theodore H. Schwartz
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Rohan Ramakrishna
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Nicolas A. Karakatsanis
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Susan C. Pannullo
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Joseph R. Osborne
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Jonathan P. S. Knisely
- Department of Radiation Oncology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Jana Ivanidze
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
- *Correspondence: Jana Ivanidze,
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Ya G, Wen F, Xing-ru L, Zhuan-zhuan G, Jun-qiang L. Difference of DCE-MRI Parameters at Different Time Points and Their Predictive Value for Axillary Lymph Node Metastasis of Breast Cancer. Acad Radiol 2022; 29 Suppl 1:S79-S86. [PMID: 33504446 DOI: 10.1016/j.acra.2021.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/09/2021] [Accepted: 01/11/2021] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES To assess differences of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) parameters at different postcontrast time points (TPs), and to explore the predictive value of DCE-MRI parameters for axillary lymph node (ALN) metastasis of breast cancer. MATERIALS AND METHODS A total of 107 breast cancer patients were included retrospectively, and 50 phases were collected on DCE-MRI for each patient. DCE-MRI parameters Ktrans, Kep, Ve, TTP, Peak, Washin, Washout, and AUC were extracted from the images at 67.8 seconds, 128.5 seconds, 189.2 seconds, 249.9 seconds, and 310.5 seconds (regard as TP1, 2, 3, 4, and 5). Wilcoxon signed rank test was used to compare DCE-MRI parameters at different postcontrast TPs. Logistic regression was performed to analyze the predictive value of DCE-MRI parameters for ALN metastasis of breast cancer, and receiver operating characteristic (ROC) curve was constructed to evaluate the predictive performance. RESULTS The difference of DCE-MRI parameters between TP1, 2, 3, 4, and 5 was statistically significant (p < 0.01) in breast cancer. The TPs are considered as the optimal TPs when DCE-MRI parameters values reach the maximum. The optimal TPs of Ktrans, Kep, and Ve were respectively at TP2, TP2, and TP4 (Ktrans2, Kep2, and Ve4). The optimal TPs of TTP, Peak, and AUC were at TP5 (TTP5, Peak5, and AUC5). AUC5 showed the ability to predict ALN metastasis of breast cancer (area under ROC curve = 0.656, p < 0.05). CONCLUSIONS DCE-MRI parameters values were different at different postcontrast TPs. AUC5 may be an independent predictor of ALN metastasis in breast cancer.
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Albano D, Bruno F, Agostini A, Angileri SA, Benenati M, Bicchierai G, Cellina M, Chianca V, Cozzi D, Danti G, De Muzio F, Di Meglio L, Gentili F, Giacobbe G, Grazzini G, Grazzini I, Guerriero P, Messina C, Micci G, Palumbo P, Rocco MP, Grassi R, Miele V, Barile A. Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging. Jpn J Radiol 2021; 40:341-366. [PMID: 34951000 DOI: 10.1007/s11604-021-01223-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.
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Affiliation(s)
- Domenico Albano
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata, Sezione Di Scienze Radiologiche, Università Degli Studi Di Palermo, via Vetoio 1L'Aquila, 67100, Palermo, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy.
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
| | - Andrea Agostini
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Clinical, Special and Dental Sciences, Department of Radiology, University Politecnica delle Marche, University Hospital "Ospedali Riuniti Umberto I - G.M. Lancisi - G. Salesi", Ancona, Italy
| | - Salvatore Alessio Angileri
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Radiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Massimo Benenati
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Dipartimento di Diagnostica per Immagini, Fondazione Policlinico Universitario A. Gemelli IRCCS, Oncologia ed Ematologia, RadioterapiaRome, Italy
| | - Giulia Bicchierai
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, Ospedale Fatebenefratelli, Milan, Italy
| | - Vito Chianca
- Ospedale Evangelico Betania, Naples, Italy
- Clinica Di Radiologia, Istituto Imaging Della Svizzera Italiana - Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Diletta Cozzi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Letizia Di Meglio
- Postgraduation School in Radiodiagnostics, University of Milan, Milan, Italy
| | - Francesco Gentili
- Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Giuliana Giacobbe
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Irene Grazzini
- Department of Radiology, Section of Neuroradiology, San Donato Hospital, Arezzo, Italy
| | - Pasquale Guerriero
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | | | - Giuseppe Micci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata, Sezione Di Scienze Radiologiche, Università Degli Studi Di Palermo, via Vetoio 1L'Aquila, 67100, Palermo, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Abruzzo Health Unit 1, Department of diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, L'Aquila, Italy
| | - Maria Paola Rocco
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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Frankhouser DE, Dietze E, Mahabal A, Seewaldt VL. Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging. FRONTIERS IN RADIOLOGY 2021; 1:735567. [PMID: 37492179 PMCID: PMC10364989 DOI: 10.3389/fradi.2021.735567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/11/2021] [Indexed: 07/27/2023]
Abstract
Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.
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Affiliation(s)
- David E. Frankhouser
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Eric Dietze
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Ashish Mahabal
- Department of Astronomy, Division of Physics, Mathematics, and Astronomy, California Institute of Technology (Caltech), Pasadena, CA, United States
| | - Victoria L. Seewaldt
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
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Liu L, Mei N, Yin B, Peng W. Correlation of DCE-MRI Perfusion Parameters and Molecular Biology of Breast Infiltrating Ductal Carcinoma. Front Oncol 2021; 11:561735. [PMID: 34722229 PMCID: PMC8548684 DOI: 10.3389/fonc.2021.561735] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/21/2021] [Indexed: 12/14/2022] Open
Abstract
Objective We aimed to investigate the correlation of the perfusion parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with the molecular biological expression of breast infiltrating ductal carcinoma (IDC) in order to guide appropriate therapeutic advice and clinical outcome prediction. Materials and Methods In a prospective analysis of 67 patients with breast IDC, preoperative DCE-MRI and routine MRI images were obtained. The double-chamber model (extended Tofts model) was employed to calculate the perfusion parameters. Postoperative pathological immunohistochemistry was examined, including human epidermal growth factor receptor 2 (HER-2), estrogen receptor (ER), progesterone receptor (PR), cell nuclear-associated antigen (Ki-67), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR). Statistical analysis was applied to explore the relationship between the perfusion parameters and the molecular biomarkers of breast cancer. Results A total of 67 lesions were included in our study. The mean maximum diameter of lesions was 4.48 ± 1.73 cm. Perfusion parameters had no correlation with tumor diameters (p > 0.05). The volume transfer constant (K trans) and the rate constant (k ep) had positive correlations with Ki-67 (p < 0.05). The plasma volume ratio (v p) had a statistical difference between CK5/6 positivity and CK5/6 negativity. The maximum rising slope (MAX Slope) was higher in HER-2-enriched tumors than that in luminal A or B tumors (p < 0.05). k ep was higher in HER-2-enriched tumors than that in luminal A tumors (p < 0.05). The extravascular extracellular space volume fraction (v e) was higher in triple-negative tumors than that in HER-2-enriched and in luminal A and B tumors (p < 0.05). The time to peak enhancement (TTP) was lower in HER-2-enriched tumors than that in luminal A and B tumors (p < 0.05). Maximum concentration (MAX Conc) was higher in triple-negative tumors than that in luminal B tumors (p < 0.05). Conclusion DCE-MRI perfusion parameters can behave as a noninvasive tool to assess the molecular biological expression and the molecular subtypes of breast IDC. They may aid in predicting breast IDC invasiveness, metastasis, and prognosis.
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Affiliation(s)
- Li Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
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Cui Q, Sun L, Zhang Y, Zhao Z, Li S, Liu Y, Ge H, Qin D, Zhao Y. Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1677. [PMID: 34988186 PMCID: PMC8667137 DOI: 10.21037/atm-21-5441] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/04/2021] [Indexed: 12/14/2022]
Abstract
Background The Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions is categorized into 4A, 4B, and 4C, which reflect an increasing malignancy potential from low (2–10%) moderate (10–50%) and high (50–95%). Determining the benign and malignant of BI-RADS category 4 breast lesions is very important for accurate diagnosis and follow-up treatment. This study aimed to explore the value of breast magnetic resonance imaging (MRI) omics features and clinical characteristics in the assessment of BI-RADS category 4 breast lesions. Methods This retrospective study analyzed 96 lesions (39 benign and 57 malignant) from 92 patients diagnosed with MRI BI-RADS category 4 lesions in the Second Affiliated Hospital of Dalian Medical University between May 2017 and December 2019. The lesions were sub-categorized as BI-RADS 4A, 4B, or 4C based on the MRI findings. An imaging omics analysis model was applied to extract the MRI features. The positive predictive value (PPV) of each subcategory was calculated, and the area under the curve (AUC) was used to describe the efficiency for different diagnoses. Moreover, we analyzed 17 clinical indicators to assess their diagnostic value for BI-RADS category 4 breast lesions. Results The PPVs of BI-RADS 4A, 4B, and 4C were 7.1% (2/28), 41.2% (7/17), and 94.1% (48/51), respectively. The AUC, sensitivity, and specificity were 0.919, 84.2%, and 92.3%, respectively. The combination of T1-weighted images (T1WI) with dynamic contrast-enhanced (DCE) MRI yielded the best diagnostic results among all dual sequences. Two clinical indicators [progesterone receptor (PR) and Ki-67 expression] achieved an AUC almost equal to 1.0. The radiomics and redundancy reduction methods reduced the clinical data features from 1,233 to 14. Conclusions High diagnostic performance can be achieved in distinguishing malignant breast BI-RADS category 4 lesions using the combination of T1WI and DCE in MRI. Combining the PR and Ki-67 expression variables can further improve MRI accuracy for breast BI-RADS category 4 lesions.
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Affiliation(s)
- Qian Cui
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Liang Sun
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Yu Zhang
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Zimu Zhao
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shuo Li
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yajie Liu
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Hongwei Ge
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Dongxue Qin
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yiping Zhao
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
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Yi Z, Xie M, Shi G, Cheng Z, Zeng H, Jiang N, Wu Z. Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor. Eur Radiol 2021; 32:1601-1610. [PMID: 34491383 DOI: 10.1007/s00330-021-08232-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/18/2021] [Accepted: 07/22/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To investigate whether quantitative DCE-MRI (qDCE-MRI) could help distinguish breast phyllodes tumor (PT) grades. MATERIALS AND METHODS This retrospective study included 67 breast PTs (26 benign lesions, 25 borderline lesions, and 16 malignant lesions) from April 2016 to July 2020. MRI was performed with a 1.5-T MR system. Perfusion parameters (Ktrans, kep, ve, iAUC60) derived from qDCE-MRI, tumor size, and the mean ADC value were correlated with histologic grades using Spearman's rank correlation coefficient. Ktrans, kep, ve, and iAUC60 of three histologic grades were also calculated and compared. RESULTS The Spearman correlation coefficient with histologic grade of the tumor size was 0.578 (p < 0.001); the ADC value was not correlated with histologic grades of breast PT (p = 0.059). The Ktrans, kep, ve, and iAUC60 of benign breast PTs were significantly lower than those of borderline breast PTs (p < 0.001) and lower than those of malignant breast PTs (p < 0.001). In comparison, the Ktrans, ve, and iAUC60 of borderline breast PTs were significantly lower than those of malignant breast PTs (p < 0.001, p < 0.001, p = 0.007, respectively). For ROC analysis, AUCs of Ktrans, ve, and iAUC60 were higher than tumor size and ADC value for differentiating three PT grades. CONCLUSION Quantitative and semi-quantitative perfusion parameters (Ktrans, ve, and iAUC60, especially Ktrans) derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs. Therefore, qDCE-MRI may be helpful for preoperative differentiating breast PT grades. KEY POINTS • Quantitative dynamic contrast-enhanced MRI can be used as a complementary noninvasive method to improve the differential diagnosis of breast PT. • Ktrans, ve, and iAUC60 derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs.
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Affiliation(s)
- Zhilong Yi
- Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, China.,Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Mingwei Xie
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Guangzi Shi
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Ziliang Cheng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Ningyi Jiang
- Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.
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Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization. Cancers (Basel) 2020; 12:cancers12123763. [PMID: 33327532 PMCID: PMC7765071 DOI: 10.3390/cancers12123763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Confirming whether a breast lesion is benign or malignant usually involves an invasive tissue sample with an image-guided breast biopsy, which may cause substantial inconvenience to the patient. The purpose of this study was to investigate whether imaging biomarkers obtained from noninvasive dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can help differentiate benign from malignant lesions and characterize breast cancers to the same extent as a biopsy. In a sample of 37 patients with suspicious findings on mammography or ultrasound, we found that the radiologists’ diagnostic accuracy was improved when subjective Breast Imaging-Reporting and Data System (BI-RADS) evaluation was augmented with the use of pharmacokinetic markers. This study serves as a starting point for future collaborative research with the potential of providing valuable noninvasive tools for improved breast cancer diagnosis. Abstract The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.
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