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Wang H, Sang L, Xu J, Huang C, Huang Z. Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer. Heliyon 2024; 10:e29875. [PMID: 38720718 PMCID: PMC11076642 DOI: 10.1016/j.heliyon.2024.e29875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Objective To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC). Methods Patients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH. The 3D VOI was drawn on MR images by two radiologists. ADC, T2WI, and DCE images were analyzed separately to extract features (n = 1906). L1 regularization, F-test, and other methods were used to reduce dimensionality. Binary radiomics prediction models using features from single or combined imaging sequences were constructed using logistic regression (LR) classifier then and validated on a validation dataset. To build a radiomics nomogram, multivariate LR analysis was conducted to identify independent indicators. An evaluation of the model's predictive efficacy was made using AUC. Results On the basis of combined ADC, T2WI, and DCE images, ten radiomic features were extracted following feature dimensionality reduction. There was superior diagnostic efficiency of radiomic signature using all three sequences compared to either one or two sequences (AUC for training group: 0.883; AUC for validation group: 0.816). Based on multivariate LR analysis, radiomic signature and peritumoral edema were independent predictors for identifying HER-2 2 +. In both training and validation datasets, nomograms combining peritumoral edema and radiomics signature demonstrated an effective discrimination (AUCs were respectively 0.966 and 0. 884). Conclusion The nomogram that incorporated peritumoral edema and multiparametric MRI-based radiomic signature can be used to effectively predict the HER-2 2+ status of BC.
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Affiliation(s)
- Haili Wang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Li Sang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co.Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co.Ltd, Beijing, China
| | - Zhaoqin Huang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
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Fei X, Yong W, Zhang D, Cui J. Advances in fibreoptic ductoscopy for the diagnosis and treatment of pathologic papillary overflow. Heliyon 2024; 10:e23211. [PMID: 38163111 PMCID: PMC10754873 DOI: 10.1016/j.heliyon.2023.e23211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024] Open
Abstract
Fibreoptic mammography is widely recognised as the first screening method for pathologic papillary overflow due to its significant advantages in the diagnosis of ductal dilatation, intraductal papilloma and intraductal carcinoma. The use of fibreoptic ductoscopic excisional biopsy techniques, such as biopsy needles, vacuum negative pressure aspiration, biopsy forceps and grasping baskets, has not been promoted largely due to their existing deficiencies. The imaging effect of fibreoptic ductoscopy compared with electronic ductoscopy is also one of the important factors limiting the progress of microscopic excisional biopsy techniques. Finding a more suitable operating space for electronic fibreoptic ductoscopy and the use of electrosurgical excision biopsy techniques should be the focus of research in view of achieving accurate diagnoses in electronic fibreoptic ductoscopy and microscopic excision biopsy. In this review, the development history, clinical application and existing problems of fibreoptic ductoscopy are reviewed and assessed to provide references for the clinical diagnosis and treatment of pathologic papillary overflow.
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Affiliation(s)
- Xiang Fei
- Department of Thyroid and Breast Surgery, People's Hospital of China Medical University, China
| | - Wei Yong
- Department of Thyroid and Breast Surgery, Chengdu Seventh People's Hospital (Cancer Hospital Affiliated to Chengdu Medical College), China
| | - Dongxiao Zhang
- Department of Breast, Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, China
| | - Jianchun Cui
- Department of Thyroid and Breast Surgery, People's Hospital of China Medical University, China
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3
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Cheung SM, Wu WS, Senn N, Sharma R, McGoldrick T, Gagliardi T, Husain E, Masannat Y, He J. Towards detection of early response in neoadjuvant chemotherapy of breast cancer using Bayesian intravoxel incoherent motion. Front Oncol 2023; 13:1277556. [PMID: 38125950 PMCID: PMC10731248 DOI: 10.3389/fonc.2023.1277556] [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: 08/14/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction The early identification of good responders to neoadjuvant chemotherapy (NACT) holds a significant potential in the optimal treatment of breast cancer. A recent Bayesian approach has been postulated to improve the accuracy of the intravoxel incoherent motion (IVIM) model for clinical translation. This study examined the prediction and early sensitivity of Bayesian IVIM to NACT response. Materials and methods Seventeen female patients with breast cancer were scanned at baseline and 16 patients were scanned after Cycle 1. Tissue diffusion and perfusion from Bayesian IVIM were calculated at baseline with percentage change at Cycle 1 computed with reference to baseline. Cellular proliferative activity marker Ki-67 was obtained semi-quantitatively with percentage change at excision computed with reference to core biopsy. Results The perfusion fraction showed a significant difference (p = 0.042) in percentage change between responder groups at Cycle 1, with a decrease in good responders [-7.98% (-19.47-1.73), n = 7] and an increase in poor responders [10.04% (5.09-28.93), n = 9]. There was a significant correlation between percentage change in perfusion fraction and percentage change in Ki-67 (p = 0.042). Tissue diffusion and pseudodiffusion showed no significant difference in percentage change between groups at Cycle 1, nor was there a significant correlation against percentage change in Ki-67. Perfusion fraction, tissue diffusion, and pseudodiffusion showed no significant difference between groups at baseline, nor was there a significant correlation against Ki-67 from core biopsy. Conclusion The alteration in tumour perfusion fraction from the Bayesian IVIM model, in association with cellular proliferation, showed early sensitivity to good responders in NACT. Clinical trial registration https://clinicaltrials.gov/ct2/show/NCT03501394, identifier NCT03501394.
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Affiliation(s)
- Sai Man Cheung
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Wing-Shan Wu
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Nicholas Senn
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Ravi Sharma
- Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Trevor McGoldrick
- Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Tanja Gagliardi
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
- Department of Radiology, Royal Marsden Hospital, London, United Kingdom
| | - Ehab Husain
- Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Yazan Masannat
- Breast Unit, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Jiabao He
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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Børretzen A, Reisæter LAR, Ringheim A, Gravdal K, Haukaas SA, Fasmer KE, Haldorsen IHS, Beisland C, Akslen LA, Halvorsen OJ. Microvascular proliferation is associated with high tumour blood flow by mpMRI and disease progression in primary prostate cancer. Sci Rep 2023; 13:17949. [PMID: 37863961 PMCID: PMC10589248 DOI: 10.1038/s41598-023-45158-4] [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: 06/03/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023] Open
Abstract
Active angiogenesis may be assessed by immunohistochemistry using Nestin, a marker of newly formed vessels, combined with Ki67 for proliferating cells. Here, we studied microvascular proliferation by Nestin-Ki67 co-expression in prostate cancer, focusing on relations to quantitative imaging parameters from anatomically matched areas obtained by preoperative mpMRI, clinico-pathological features and prognosis. Tumour slides from 67 patients (radical prostatectomies) were stained for Nestin-Ki67. Proliferative microvessel density (pMVD) and presence of glomeruloid microvascular proliferation (GMP) were recorded. From mpMRI, forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), volume of EES (ve), blood flow, and apparent diffusion coefficient (ADC) were obtained. High pMVD was associated with high blood flow (p = 0.008) and low ADC (p = 0.032). High Ktrans, kep, and blood flow were associated with high Gleason score. High pMVD, GMP, and low ADC were associated with most adverse clinico-pathological factors. Regarding prognosis, high pMVD, Ktrans, kep, and low ADC were associated with reduced biochemical recurrence-free- and metastasis-free survival (p ≤ 0.044) and high blood flow with reduced time to biochemical- and clinical recurrence (p < 0.026). In multivariate analyses however, microvascular proliferation was a stronger predictor compared with blood flow. Indirect, dynamic markers of angiogenesis from mpMRI and direct, static markers of angiogenesis from immunohistochemistry may aid in the stratification and therapy planning of prostate cancer patients.
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Affiliation(s)
- Astrid Børretzen
- Centre for Cancer Biomarkers CCBIO, Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
- Department of Pathology, Haukeland University Hospital, 5021, Bergen, Norway.
| | - Lars A R Reisæter
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Anders Ringheim
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Karsten Gravdal
- Department of Pathology, Haukeland University Hospital, 5021, Bergen, Norway
| | - Svein A Haukaas
- Department of Urology, Haukeland University Hospital, Bergen, Norway
| | - Kristine E Fasmer
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid H S Haldorsen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Christian Beisland
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Urology, Haukeland University Hospital, Bergen, Norway
| | - Lars A Akslen
- Centre for Cancer Biomarkers CCBIO, Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Pathology, Haukeland University Hospital, 5021, Bergen, Norway
| | - Ole J Halvorsen
- Centre for Cancer Biomarkers CCBIO, Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Feng S, Yin J. Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer. Quant Imaging Med Surg 2023; 13:6735-6749. [PMID: 37869317 PMCID: PMC10585575 DOI: 10.21037/qims-22-1073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 08/14/2023] [Indexed: 10/24/2023]
Abstract
Background Breast cancer is a heterogeneous disease with different morphological and biological characteristics. The molecular subtypes of breast cancer are closely related to the treatment and prognosis of patients. In order to predict the luminal type of breast cancer in a noninvasive manner, our study developed and validated a radiomics nomogram combining clinical factors with a radiomics score based on the features of the intratumoral subregion to distinguish between luminal and nonluminal breast cancer. Methods From January 2018 to January 2020, 153 women with clinically and pathologically diagnosed breast cancer with an average age of 50.08 years were retrospectively analyzed. Using a semiautomatic segmentation method, the whole tumor was divided into 3 subregions on the basis of the time required for the contrast agent to reach its peak; 540 features were extracted from 3 subregions and the whole tumor region. Subsequently, 2 machine learning classifiers were developed. The least absolute shrinkage and selection operator method was used for feature selection and radiomics score (Rad-score) construction. Moreover, multivariable logistic regression analysis was applied to select independent factors from the Rad-score and clinical factors to establish a prediction model in the form of a nomogram. The performance of the nomogram was evaluated through calibration, discrimination, and clinical usefulness. Results The prediction performance of texture features from the rapid subregion was the best in the 3 intratumoral subregions, and the area under the receiver operating characteristic curve (AUC) values in the training and validation cohort were 0.805 (95% CI: 0.719-0.892) and 0.737 (95% CI: 0.581-0.893), respectively. The Rad-score, consisting of 5 features from the rapid subregion, was associated with the luminal type of breast cancer (P=0.001 and P=0.035 in the training and validation cohorts, respectively). The predictors included in the personalized prediction nomogram included Rad-score, human epidermal growth factor receptor 2 (HER2) status, and tumor histological grade. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve in the training and validation cohorts of 0.830 (95% CI: 0.746-0.896) and 0.879 (95% CI: 0.748-0.957), respectively. The calibration curve of the 2 cohorts and decision curve analysis demonstrated that the nomogram had good calibration and clinical usefulness. Conclusions We proposed a nomogram model that combined clinical factors and Rad-score, which showed good performance in predicting the luminal type of breast cancer.
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Affiliation(s)
- Shuqian Feng
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Yuan J, Liu K, Zhang Y, Yang Y, Xu H, Han G, Lyu H, Liu M, Tan W, Feng Z, Gong H, Zhan S. Quantitative dynamic contrast-enhance MRI parameters for rectal carcinoma characterization: correlation with tumor tissue composition. World J Surg Oncol 2023; 21:306. [PMID: 37749564 PMCID: PMC10521534 DOI: 10.1186/s12957-023-03193-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/19/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE To investigate the relationship between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) measurements and the potential composition of rectal carcinoma. METHODS Twenty-four patients provided informed consent for this study. DCE-MRI was performed before total mesorectal excision. Quantitative parameters were calculated based on a modified Tofts model. Whole-mount immunohistochemistry and Masson staining sections were generated and digitized at histological resolution. The percentage of tissue components area was measured. Pearson correlation analysis was used to evaluate the correlations between pathological parameters and DCE-MRI parameters. RESULTS On the World Health Organization (WHO) grading scale, there were significant differences in extracellular extravascular space (Ktrans) (F = 9.890, P = 0.001), mean transit time (MTT) (F = 9.890, P = 0.038), CDX-2 (F = 4.935, P = 0.018), and Ki-67 (F = 4.131, P = 0.031) among G1, G2, and G3. ECV showed significant differences in extramural venous invasion (t = - 2.113, P = 0.046). Ktrans was strongly positively correlated with CD34 (r = 0.708, P = 0.000) and moderately positively correlated with vimentin (r = 0.450, P = 0.027). Interstitial volume (Ve) was moderately positively correlated with Masson's (r = 0.548, P = 0.006) and vimentin (r = 0.417, P = 0.043). There was a moderate negative correlation between Ve and CDX-2 (r = - 0.441, P = 0.031). The rate constant from extracellular extravascular space to blood plasma (Kep) showed a strong positive correlation with CD34 expression (r = 0.622, P = 0.001). ECV showed a moderate negative correlation with CDX-2 (r = - 0.472, P = 0.020) and a moderate positive correlation with collagen fibers (r = 0.558, P = 0.005). CONCLUSION The dynamic contrast-enhanced MRI-derived parameters measured in rectal cancer were significantly correlated with the proportion of histological components. This may serve as an optimal imaging biomarker to identify tumor tissue components.
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Affiliation(s)
- Jie Yuan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Kun Liu
- Department of Pathology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Yun Zhang
- Department of Gastrointestinal Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Yuchan Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Huihui Xu
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Gang Han
- Department of Gastrointestinal Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hua Lyu
- Department of Science and Technology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Mengxiao Liu
- Diagnostic Imaging, MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, 201203, China
| | - Wenli Tan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Zhen Feng
- Department of Pathology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hangjun Gong
- Department of Gastrointestinal Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
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Carmona-Bozo JC, Manavaki R, Miller JL, Brodie C, Caracò C, Woitek R, Baxter GC, Graves MJ, Fryer TD, Provenzano E, Gilbert FJ. PET/MRI of hypoxia and vascular function in ER-positive breast cancer: correlations with immunohistochemistry. Eur Radiol 2023; 33:6168-6178. [PMID: 37166494 PMCID: PMC10415421 DOI: 10.1007/s00330-023-09572-6] [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: 12/16/2022] [Revised: 12/16/2022] [Accepted: 02/08/2023] [Indexed: 05/12/2023]
Abstract
OBJECTIVES To explore the relationship between indices of hypoxia and vascular function from 18F-fluoromisonidazole ([18F]-FMISO)-PET/MRI with immunohistochemical markers of hypoxia and vascularity in oestrogen receptor-positive (ER +) breast cancer. METHODS Women aged > 18 years with biopsy-confirmed, treatment-naïve primary ER + breast cancer underwent [18F]-FMISO-PET/MRI prior to surgery. Parameters of vascular function were derived from DCE-MRI using the extended Tofts model, whilst hypoxia was assessed using the [18F]-FMISO influx rate constant, Ki. Histological tumour sections were stained with CD31, hypoxia-inducible factor (HIF)-1α, and carbonic anhydrase IX (CAIX). The number of tumour microvessels, median vessel diameter, and microvessel density (MVD) were obtained from CD31 immunohistochemistry. HIF-1α and CAIX expression were assessed using histoscores obtained by multiplying the percentage of positive cells stained by the staining intensity. Regression analysis was used to study associations between imaging and immunohistochemistry variables. RESULTS Of the lesions examined, 14/22 (64%) were ductal cancers, grade 2 or 3 (19/22; 86%), with 17/22 (77%) HER2-negative. [18F]-FMISO Ki associated negatively with vessel diameter (p = 0.03), MVD (p = 0.02), and CAIX expression (p = 0.002), whilst no significant relationships were found between DCE-MRI pharmacokinetic parameters and immunohistochemical variables. HIF-1α did not significantly associate with any PET/MR imaging indices. CONCLUSION Hypoxia measured by [18F]-FMISO-PET was associated with increased CAIX expression, low MVD, and smaller vessel diameters in ER + breast cancer, further corroborating the link between inadequate vascularity and hypoxia in ER + breast cancer. KEY POINTS • Hypoxia, measured by [18F]-FMISO-PET, was associated with low microvessel density and small vessel diameters, corroborating the link between inadequate vascularity and hypoxia in ER + breast cancer. • Increased CAIX expression was associated with higher levels of hypoxia measured by [18F]-FMISO-PET. • Morphologic and functional abnormalities of the tumour microvasculature are the major determinants of hypoxia in cancers and support the previously reported perfusion-driven character of hypoxia in breast carcinomas.
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Affiliation(s)
- Julia C Carmona-Bozo
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Roido Manavaki
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Jodi L Miller
- Cancer Research UK - Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Cara Brodie
- Cancer Research UK - Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Corradina Caracò
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Ramona Woitek
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Gabrielle C Baxter
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Martin J Graves
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Tim D Fryer
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Box 65 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Elena Provenzano
- Cancer Research UK - Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Box 97 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218 - Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
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Feng H, Liu H, Wang Q, Song M, Yang T, Zheng L, Wu D, Shao X, Shi G. Breast cancer diagnosis and prognosis using a high b-value non-Gaussian continuous-time random-walk model. Clin Radiol 2023:S0009-9260(23)00227-1. [PMID: 37344324 DOI: 10.1016/j.crad.2023.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 06/23/2023]
Abstract
AIM To compare the diagnostic performance of mono-exponential model-derived apparent diffusion coefficient (ADC), continuous-time random-walk (CTRW) model-derived Dm, α, β and their combinations in discriminating malignancy of breast lesions, and investigate the association between model-derived parameters and prognosis-related immunohistochemical indices. MATERIALS AND METHODS A total of 85 patients with breast lesions (51 malignant, 34 benign) were analysed in this retrospective study. Clinical characteristics include oestrogen receptor (ER), progesterone receptor (PR), human epidermal receptor 2 (HER2), and Ki-67. The ADC was fitted using a mono-exponential model (b-values = 0, 800 s/mm2), while Dm, α, and β were fitted using a CTRW model. Independent Student's t-test and the Mann-Whitney U-test were used for the comparison of parameters. Discrimination performance was accomplished by receiver operating characteristic (ROC) analysis, and Spearman's correlation analysis was used to explore the association between immunohistochemical indices and diffusion parameters, the statistical significance level was p<0.05. RESULTS Dm and ADC demonstrated similar performance in differentiating malignant and benign lesions (AUC = 0.928 versus 0.930), while the combination of Dm, α, and β could improve the AUC to 0.969. The combined parameter generated by ADC, Dm, α, and β was effective in identifying the ER+/ER- and PR+/PR- patients. Temporal heterogeneity parameter α correlated significantly with the expression of PR. CONCLUSION Diffusion parameters derived from the CTRW model could effectively discriminate the malignancy of breast lesions. Meanwhile, the hormone receptor expression could be distinguished by combined diffusion parameters, and have the potential to reflect the prognosis.
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Affiliation(s)
- H Feng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - M Song
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - T Yang
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L Zheng
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - D Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China
| | - X Shao
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
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EL-Metwally D, Monier D, Hassan A, Helal AM. Preoperative prediction of Ki-67 status in invasive breast carcinoma using dynamic contrast-enhanced MRI, diffusion-weighted imaging and diffusion tensor imaging. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023. [DOI: 10.1186/s43055-023-01007-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
Abstract
Background
The Ki-67 is a beneficial marker of tumor aggressiveness. It is proliferation index that has been used to distinguish luminal B from luminal A breast cancers. By fast progress in quantitative radiology modalities, tumor biology and genetics can be assessed in a more accurate, predictive, and cost-effective method. The aim of this study was to assess the role of dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging and diffusion tensor imaging in prediction of Ki-67 status in patients with invasive breast carcinoma estimate cut off values between breast cancer with high Ki-67 status and those with low Ki-67 status.
Results
Cut off ADC (apparent diffusion co-efficient) value of 0.657 mm2/s had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off maximum enhancement value of 1715 had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off washout rate of 0.73 I/S had 60.7% sensitivity, 75% specificity and 62.5% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off time to peak value of 304 had 71.4% sensitivity, 75% specificity and 71.9% accuracy in differentiating cases with high Ki67 from those with low Ki67.
Conclusions
ADC, time to peak and maximum enhancement values had high sensitivity, specificity and accuracy in differentiating breast cancer with high Ki-67 status from those with low Ki-67 status.
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10
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Du Y, Zhang S, Liang T, Shang J, Guo C, Lian J, Gong H, Yang J, Niu G. Dynamic contrast-enhanced MRI perfusion parameters are imaging biomarkers for angiogenesis in lung cancer. Acta Radiol 2023; 64:572-580. [PMID: 35369721 DOI: 10.1177/02841851221088581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential to reflect angiogenesis and proliferation of pulmonary neoplasms. PURPOSE To verify whether DCE-MRI can identify pulmonary neoplasm property and evaluate the correlation of DCE-MRI perfusion parameters with microvessel density (MVD) and Ki-67 in lung cancer. MATERIAL AND METHODS This study enrolled 65 patients with one pulmonary neoplasm who underwent computed tomography-guided percutaneous lung biopsy with pathological diagnosis (43 malignant, 22 benign; mean age = 59.71 ± 11.72 years). All patients did DCE-MRI before biopsy. Quantitative MRI parameters including endothelial transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space (EES) volume (Ve) were calculated by extended Tofts linear model. MVD was evaluated by CD34-expressing tumor vessels. Proliferation was assessed by Ki-67 staining. The correlations of parameters with MVD and Ki-67 expression were analyzed. RESULTS Ktrans and Kep values were significantly increased in malignant lesions compared to benign lesions (P = 0.001 and 0.022, respectively), whereas no statistical difference in Ve was found. The CD34 expression was positively correlated to Ktrans (r = 0.608; P = 0.004) and Kep (r = 0.556; P = 0.001). Subsequent subtype analyses also showed positive correlations of Ktrans and Kep with MVD in adenocarcinoma group (r = 0.550 and 0.563; P = 0.012 and 0.015, respectively). No significant correlation was found between these parameters and Ki-67. CONCLUSION Ktrans and Kep may distinguish benign and malignant pulmonary neoplasm. Ktrans and Kep, with their positive correlation to MVD, can be used as non-invasive parameters reflecting lung cancer angiogenesis.
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Affiliation(s)
- Yonghao Du
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Shuo Zhang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Ting Liang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Jin Shang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Chenguang Guo
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Jie Lian
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Huilin Gong
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Gang Niu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
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11
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Xie T, Jiang T, Zhao Q, Fu C, Nickel MD, Peng W, Gu Y. Model‐Free and Model‐based Parameters Derived From
CAIPIRINHA‐Dixon‐TWIST‐VIBE DCE‐MRI
: Associations With Prognostic Factors and Molecular Subtypes of Invasive Ductal Breast Cancer. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 11/03/2022] [Accepted: 11/05/2022] [Indexed: 11/27/2022] Open
Affiliation(s)
- Tianwen Xie
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
| | - Tingting Jiang
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital Shanghai University of Traditional Chinese Medicine Shanghai People's Republic of China
| | - Caixia Fu
- MR Applications Development Siemens Shenzhen Magnetic Resonance Ltd. Shenzhen People's Republic of China
| | | | - Weijun Peng
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
| | - Yajia Gu
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
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12
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Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion. Tomography 2022; 8:2676-2686. [PMID: 36412682 PMCID: PMC9680473 DOI: 10.3390/tomography8060223] [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: 08/18/2022] [Revised: 10/20/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann-Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_ADC, model_IVIM, model_DCE, model_DCE+ADC, and model_DCE+IVIM). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_DCE+IVIM showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828-0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_ADC (p = 0.014) and model_IVIM (p = 0.033). The model_ADC had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672-0.846) but was not significantly different from model_IVIM (p = 0.168). The united quantitative model with DCE-MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.
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Makineli S, Filipe MD, Euwe F, Sakes A, Dankelman J, Breedveld P, Vriens MR, van Diest PJ, Witkamp AJ. Feasibility of Narrow-Band Imaging, Intraductal Biopsy, and Laser Ablation During Mammary Ductoscopy: Protocol for an Interventional Study. Int J Surg Protoc 2022; 26:73-80. [PMID: 36118293 PMCID: PMC9438461 DOI: 10.29337/ijsp.180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- S. Makineli
- Department of Surgical Oncology, University Medical Center, Utrecht, The Netherlands
| | - M. D. Filipe
- Department of Surgical Oncology, University Medical Center, Utrecht, The Netherlands
| | - F. Euwe
- Department of Medical Technology and Clinical Physics, University Medical Center, Utrecht, The Netherlands
| | - A. Sakes
- Department of BioMechanical Engineering, Technical University, Delft, The Netherlands
| | - J. Dankelman
- Department of BioMechanical Engineering, Technical University, Delft, The Netherlands
| | - P. Breedveld
- Department of BioMechanical Engineering, Technical University, Delft, The Netherlands
| | - M. R. Vriens
- Department of Surgical Oncology, University Medical Center, Utrecht, The Netherlands
| | - P. J. van Diest
- Department of Pathology, University Medical Center, Utrecht, The Netherlands
| | - A. J. Witkamp
- Department of Surgical Oncology, University Medical Center, Utrecht, The Netherlands
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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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15
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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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16
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Gong C, Cheng Z, Yang Y, Shen J, Zhu Y, Ling L, Lin W, Yu Z, Li Z, Tan W, Zheng C, Zheng W, Zhong J, Zhang X, Zeng Y, Liu Q, Huang RS, Komorowski AL, Yang ES, Bertucci F, Ricci F, Orlandi A, Franceschini G, Takabe K, Klimberg S, Ishii N, Toss A, Tan MP, Cherian MA, Song E. A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer. SCIENCE CHINA. LIFE SCIENCES 2022; 65:2205-2217. [PMID: 35579777 DOI: 10.1007/s11427-022-2104-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/23/2022] [Indexed: 12/21/2022]
Abstract
Patients with hormone receptor (HR)-positive tumors breast cancer usually experience a relatively low pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Here, we derived a 10-microRNA risk score (10-miRNA RS)-based model with better performance in the prediction of pCR and validated its relation with the disease-free survival (DFS) in 755 HR-positive breast cancer patients (273, 265, and 217 in the training, internal, and external validation sets, respectively). This model, presented as a nomogram, included four parameters: the 10-miRNA RS found in our previous study, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and volume transfer constant (Ktrans). Favorable calibration and discrimination of 10-miRNA RS-based model with areas under the curve (AUC) of 0.865, 0.811, and 0.804 were shown in the training, internal, and external validation sets, respectively. Patients who have higher nomogram score (>92.2) with NAC treatment would have longer DFS (hazard ratio=0.57; 95%CI: 0.39-0.83; P=0.004). In summary, our data showed the 10-miRNA RS-based model could precisely identify more patients who can attain pCR to NAC, which may help clinicians formulate the personalized initial treatment strategy and consequently achieves better clinical prognosis for patients with HR-positive breast cancer.
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Affiliation(s)
- Chang Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Ziliang Cheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Yaping Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Yingying Zhu
- Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Li Ling
- Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.,Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Wanyi Lin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhigang Yu
- Department of Breast Surgery, the Second Affiliated Hospital, Shandong University, Jinan, 250033, China
| | - Zhihua Li
- Department of Breast Surgery, Key Laboratory of Breast Diseases, Third Hospital of Nanchang, Nanchang, 330009, China
| | - Weige Tan
- Department of Breast Surgery, the First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Chushan Zheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Wenbo Zheng
- Department of Breast Surgery, the First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Jiajie Zhong
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Yunjie Zeng
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - R Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Andrzej L Komorowski
- Department of Surgery, College of Medicine, University of Rzeszów, Rzeszów, 35-959, Poland
| | - Eddy S Yang
- Department of Radiation Oncology, O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
| | - François Bertucci
- Laboratoty of Predictive Oncology, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, INSERM UMR1068, CNRS UMR725, Marseille, France
| | - Francesco Ricci
- Department of Drug Development and Innovation(D3i), Institut Curie, Paris, 75005, France
| | - Armando Orlandi
- Comprehensive Cancer Center, UOC di Oncologia Medica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, 00168, Italy
| | - Gianluca Franceschini
- Multidisciplinary Breast Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, 00168, Italy
| | - Kazuaki Takabe
- Breast Surgery, Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Suzanne Klimberg
- Department of Surgery, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Naohiro Ishii
- Department of Plastic and Reconstructive Surgery, International University of Health and Welfare Hospital, Nasushiobara City, Tochigi, 329-2763, Japan
| | - Angela Toss
- Department of Oncology and Hematology, University Hospital of Modena, Modena, 41124, Italy
| | - Mona P Tan
- MammoCare: Breast Clinic and Surgery in Singapore, Singapore, 228510, Singapore
| | - Mathew A Cherian
- The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH, 43210, USA
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
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Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
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Amado Cabana S, Gallego Ojea J, Félez Carballada M. Usefulness of dynamic contrast-enhanced magnetic resonance imaging in characterizing ovarian tumors classified as indeterminate at ultrasonography. RADIOLOGIA 2022; 64:110-118. [DOI: 10.1016/j.rxeng.2020.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/20/2020] [Indexed: 10/18/2022]
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Bordeau BM, Polli JR, Schweser F, Grimm HP, Richter WF, Balthasar JP. Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition. Int J Mol Sci 2022; 23:679. [PMID: 35054865 PMCID: PMC8775965 DOI: 10.3390/ijms23020679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.
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Affiliation(s)
- Brandon M. Bordeau
- Department of Pharmaceutical Sciences, University at Buffalo, 450 Pharmacy Building, Buffalo, NY 14214, USA; (B.M.B.); (J.R.P.)
| | - Joseph Ryan Polli
- Department of Pharmaceutical Sciences, University at Buffalo, 450 Pharmacy Building, Buffalo, NY 14214, USA; (B.M.B.); (J.R.P.)
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
- Clinical and Translational Science Institute, Center for Biomedical Imaging, University at Buffalo, Buffalo, NY 14203, USA
| | - Hans Peter Grimm
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (H.P.G.); (W.F.R.)
| | - Wolfgang F. Richter
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (H.P.G.); (W.F.R.)
| | - Joseph P. Balthasar
- Department of Pharmaceutical Sciences, University at Buffalo, 450 Pharmacy Building, Buffalo, NY 14214, USA; (B.M.B.); (J.R.P.)
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Tang W, Zhou H, Quan T, Chen X, Zhang H, Lin Y, Wu R. XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses. Front Oncol 2022; 12:833680. [PMID: 35372060 PMCID: PMC8968064 DOI: 10.3389/fonc.2022.833680] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/21/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The malignant probability of MRI BiRADS 4 breast lesions ranges from 2% to 95%, leading to unnecessary biopsies. The purpose of this study was to construct an optimal XGboost prediction model through a combination of DKI independently or jointly with other MR imaging features and clinical characterization, which was expected to reduce false positive rate of MRI BiRADS 4 masses and improve the diagnosis efficiency of breast cancer. METHODS 120 patients with 158 breast lesions were enrolled. DKI, Diffusion-weighted Imaging (DWI), Proton Magnetic Resonance Spectroscopy (1H-MRS) and Dynamic Contrast-Enhanced MRI (DCE-MRI) were performed on a 3.0-T scanner. Wilcoxon signed-rank test and χ2 test were used to compare patient's clinical characteristics, mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), total choline (tCho) peak, extravascular extracellular volume fraction (Ve), flux rate constant (Kep) and volume transfer constant (Ktrans). ROC curve analysis was used to analyze the diagnostic performances of the imaging parameters. Spearman correlation analysis was performed to evaluate the associations of imaging parameters with prognostic factors and breast cancer molecular subtypes. The Least Absolute Shrinkage and Selectionator operator (lasso) and the area under the curve (AUC) of imaging parameters were used to select discriminative features for differentiating the breast benign lesions from malignant ones. Finally, an XGboost prediction model was constructed based on the discriminative features and its diagnostic efficiency was verified in BiRADS 4 masses. RESULTS MK derived from DKI performed better for differentiating between malignant and benign lesions than ADC, MD, tCho, Kep and Ktrans (p < 0.05). Also, MK was shown to be more strongly correlated with histological grade, Ki-67 expression and lymph node status. MD, MK, age, shape and menstrual status were selected to be the optimized feature subsets to construct an XGboost model, which exhibited superior diagnostic ability for breast cancer characterization and an improved evaluation of suspicious breast tumors in MRI BiRADS 4. CONCLUSIONS DKI is promising for breast cancer diagnosis and prognostic factor assessment. An optimized XGboost model that included DKI, age, shape and menstrual status is effective in improving the diagnostic accuracy of BiRADS 4 masses.
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Affiliation(s)
- Wan Tang
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Institute of Health Monitoring, Inspection and Protection, Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
| | - Han Zhou
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Tianhong Quan
- Department of Electronic and information Engineering, College of Engineering, Shantou University, Shantou, China
| | - Xiaoyan Chen
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Huanian Zhang
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yan Lin
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Renhua Wu
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
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Li X, Fu P, Jiang M, Zhang J, Tan L, Ai T, Li X. The diagnostic performance of dynamic contrast-enhanced MRI and its correlation with subtypes of breast cancer. Medicine (Baltimore) 2021; 100:e28109. [PMID: 34941052 PMCID: PMC8701457 DOI: 10.1097/md.0000000000028109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 11/16/2021] [Indexed: 01/05/2023] Open
Abstract
To evaluate diagnostic performance of perfusion-weighted imaging in differentiating benign from malignant breast lesions, and the correlation between the prognostic factors/subtypes of breast cancers and the perfusion parameters.A total of 76 patients (59 cases with breast cancer) were included in our study. The Wilcoxon rank-sum test or the Kruskal-Wallis test were adopted for comparisons according to the dichotomous histopathologic prognostic factors or immunohistochemical subtypes. Receiver operating characteristic curves were used to determine the area under the curve (AUC) values for perfusion parameters to assess discrimination ability.Confirming by pathology after operation, the percentage of benign lesions is 22.37% (17/76), malignant lesions (breast cancer) is 77.63% (59/76). According to puncture and pathological findings after operation, the standard of the molecular subtypes of breast cancer, triple negative account for 13.6% (8/59), non-triple negative account for 86.4% (51/59). The value of mean Ktrans and Kep were lower in benign than malignant lesions (P ≤ .001). The AUC of the 3 indicators are significantly improved after adjusting for age (AUC = 0.858 for Ktrans, AUC = 0.926 for Kep, and AUC = 0.827 for Ve). Moreover, the Ve index showed better discrimination performance than other indicators in identifying patients with triple-negative subtypes. Similarly, the identification ability came to the highest when combing Kep and Ve.Perfusion parameters on dynamic enhanced magnetic resonance imaging are statistically significant in distinguishing benign from malignant breast lesion, and may potentially be used as biomarkers in discriminating patients with triple-negative molecular subtypes of breast cancer.
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Affiliation(s)
- Xun Li
- Department of Thyroid and Breast Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, China
| | - Peng Fu
- Department of Thyroid and Breast Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, China
| | - Ming Jiang
- Department of Thyroid and Breast Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, China
| | - Jiaming Zhang
- Department of Thyroid and Breast Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, China
| | - Lun Tan
- Department of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, China
| | - Tao Ai
- Department of Imaging Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, China
| | - Xingrui Li
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, China
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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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Mansour S, Selim A, Kassam L, Adel M, Hashem AB. Diffusion-weighted imaging or MR spectroscopy: Which to use for the assessment of the response to chemotherapy in breast cancer patients? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00574-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Diffusion-weighted MRI (DWI) and MR spectroscopy (MRS) both are noninvasive MR sequences that could be used as a reliable tool to assess the functional behavior of the breast cancer. The aim of the study was to assess the value of DWI and MRS in predicting the early response to neo-adjuvant chemotherapy (NAC) and absence of residual disease after treatment.
Results
One hundred thirty-three patients diagnosed with breast cancer and scheduled for NAC were enrolled in this study. All lesions were subjected to qualitative and quantitative analysis of DCE-MRI, DWI and MRS, where the lesions size, kinetic parameters, ADC values and MRS choline peak were recorded before the start of NAC and after completion of chemotherapy. The results of each MRI modality were correlated with the findings that were found at the pathology report of the complete surgical specimen. The sensitivity and specificity of the MR modalities to predict pathological complete remission post-NAC were 73.68% and 83.33%, respectively, using the kinetic curve pattern, 78.95% and 83.33%, respectively, using the ADC value and finally 78.95% and 91.67%, respectively, using the MRS choline peak. Similar sensitivity (89.47%) to predict pathological complete remission was presented by the ADC value and the MRS choline peak together when compared to the ADC value and dynamic curve patterns.
Conclusion
DWI and MRS are valuable MRI techniques and their accuracy in detecting residual disease is almost similar to that of DCE MRI. The inclusion of these sequences in the imaging protocol of NAC candidates improve monitoring of the response to treatment and allow early distinction between complete, partial and non-responders' cases in breast cancer patients.
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Park HS, Lee KS, Seo BK, Kim ES, Cho KR, Woo OH, Song SE, Lee JY, Cha J. Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients. Cancers (Basel) 2021; 13:cancers13236013. [PMID: 34885124 PMCID: PMC8656976 DOI: 10.3390/cancers13236013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/17/2021] [Accepted: 11/27/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine. Abstract This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.
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Affiliation(s)
- Hyun-Soo Park
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
| | - Kwang-sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Korea;
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
- Correspondence:
| | - Eun-Sil Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea; (K.-R.C.); (S.-E.S.)
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea;
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea; (K.-R.C.); (S.-E.S.)
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Korea;
| | - Jaehyung Cha
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
- Cheng Hyang NF Co., Ltd., 44-5 Daehak-ro, Jongno-gu, Seoul 03122, Korea
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Lee J, Kim SH, Kang BJ, Lee A, Park WC, Hwang J. Imaging characteristics of young age breast cancer (YABC) focusing on pathologic correlation and disease recurrence. Sci Rep 2021; 11:20205. [PMID: 34642389 PMCID: PMC8511101 DOI: 10.1038/s41598-021-99600-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 09/14/2021] [Indexed: 11/19/2022] Open
Abstract
The purpose of this study is to investigate imaging characteristics of young age breast cancer (YABC) focusing on correlation with pathologic factors and association with disease recurrence. From January 2017 to December 2019, patients under 40 years old who were diagnosed as breast cancer were enrolled in this study. Morphologic analysis of tumor and multiple quantitative parameters were obtained from pre-treatment dynamic contrast enhanced breast magnetic resonance imaging (DCE-MRI). Tumor-stroma ratio (TSR), microvessel density (MVD) and endothelial Notch 1 (EC Notch 1) were investigated for correlation with imaging parameters. In addition, recurrence associated factors were assessed using both clinico-pathologic factors and imaging parameters. A total of 53 patients were enrolled. Several imaging parameters derived from apparent diffusion coefficient (ADC) histogram showed negative correlation with TSR; and there was negative correlation between MVD and Ve in perfusion analysis. There were nine cases of recurrences with median interval of 16 months. Triple negative subtype and low CD34 MVD positivity in Notch 1 hotspots showed significant association with tumor recurrence. Texture parameters reflecting tumor sphericity and homogeneity were also associated with disease recurrence. In conclusion, several quantitative MRI parameters can be used as imaging biomarkers for tumor microenvironment and can predict disease recurrence in YABC.
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Affiliation(s)
- Jeongmin Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
| | - Bong Joo Kang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Ahwon Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Woo-Chan Park
- Division of Breast-Thyroid Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jinwoo Hwang
- Philips Healthcare Korea, Seoul, Republic of Korea
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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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Zhang B, Song L, Yin J. Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors. Front Oncol 2021; 11:688182. [PMID: 34307153 PMCID: PMC8299951 DOI: 10.3389/fonc.2021.688182] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/15/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose To evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors. Materials and Methods A total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves. Results In the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808-0.906), 0.860 (95% CI, 0.806-0.904), 0.934 (95% CI, 0.891-0.963), and 0.921 (95% CI, 0.876-0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747-0.908), 0.784 (95% CI, 0.601-0.798), 0.890 (95% CI, 0.806-0.946), and 0.865 (95% CI, 0.777-0.928), respectively. Conclusion The texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.
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Affiliation(s)
- Bin Zhang
- School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China
| | - Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Filipe MD, de Bock E, Postma EL, Bastian OW, Schellekens PPA, Vriens MR, Witkamp AJ, Richir MC. Robotic nipple-sparing mastectomy complication rate compared to traditional nipple-sparing mastectomy: a systematic review and meta-analysis. J Robot Surg 2021; 16:265-272. [PMID: 34128142 PMCID: PMC8960562 DOI: 10.1007/s11701-021-01265-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 06/09/2021] [Indexed: 12/03/2022]
Abstract
Breast cancer is worldwide the most common cause of cancer in women and causes the second most common cancer-related death. Nipple-sparing mastectomy (NSM) is commonly used in therapeutic and prophylactic settings. Furthermore, (preventive) mastectomies are, besides complications, also associated with psychological and cosmetic consequences. Robotic NSM (RNSM) allows for better visualization of the planes and reducing the invasiveness. The aim of this study was to compare the postoperative complication rate of RNSM to NSM. A systematic search was performed on all (R)NSM articles. The primary outcome was determining the overall postoperative complication rate of traditional NSM and RNSM. Secondary outcomes were comparing the specific postoperative complication rates: implant loss, hematoma, (flap)necrosis, infection, and seroma. Forty-nine studies containing 13,886 cases of (R)NSM were included. No statistically significant differences were found regarding postoperative complications (RNSM 3.9%, NSM 7.0%, p = 0.070), postoperative implant loss (RNSM 4.1%, NSM 3.2%, p = 0.523), hematomas (RNSM 4.3%, NSM 2.0%, p = 0.059), necrosis (RNSM 4.3%, NSM 7.4%, p = 0.230), infection (RNSM 8.3%, NSM 4.0%, p = 0.054) or seromas (RNSM 3.0%, NSM 2.0%, p = 0.421). Overall, there are no statistically significant differences in complication rates between NSM and RNSM.
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Affiliation(s)
- M. D. Filipe
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - E. de Bock
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - E. L. Postma
- Department of Surgery, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - O. W. Bastian
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - P. P. A. Schellekens
- Department of Plastic Surgery, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - M. R. Vriens
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - A. J. Witkamp
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - M. C. Richir
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands
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Liang H, Hu C, Lu J, Zhang T, Jiang J, Ding D, Du S, Duan S. Correlation of radiomic features on dynamic contrast-enhanced magnetic resonance with microvessel density in hepatocellular carcinoma based on different models. J Int Med Res 2021; 49:300060521997586. [PMID: 33682491 PMCID: PMC7944531 DOI: 10.1177/0300060521997586] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Objective To explore the correlations of radiomic features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with microvessel density (MVD) in patients with hepatocellular carcinoma (HCC), based on single-input and dual-input two-compartment extended Tofts (SITET and DITET) models. Methods We compared the quantitative parameters of SITET and DITET models for DCE-MRI in 30 patients with HCC using paired sample t-tests. The correlations of SITET and DITET model parameters with CD31-MVD and CD34-MVD were analyzed using Pearson’s correlation analysis. A diagnostic model of CD34-MVD was established and the diagnostic abilities of models for MVD were analyzed using receiver operating characteristic curve (ROC) analysis. Results There were significant differences between the quantitative parameters in the two kinds of models. Compared with SITET, DITET parameters showed better correlations with CD31-MVD and CD34-MVD. The Ktrans and Ve radiomics features of the DITET model showed high efficiency for predicting the level of CD34-MVD according to ROC analysis, with areas under curves of 0.83 and 0.94, respectively. Conclusion Compared with SITET, the DITET model provides a better indication of the microcirculation of HCC and is thus more suitable for examining patients with HCC.
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Affiliation(s)
- Hongwei Liang
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China.,Department of Radiology, Nantong Third People's Hospital, Nantong, China
| | - Chunhong Hu
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Jian Lu
- Department of Radiology, Nantong Third People's Hospital, Nantong, China
| | - Tao Zhang
- Department of Radiology, Nantong Third People's Hospital, Nantong, China
| | - Jifeng Jiang
- Department of Radiology, Nantong Third People's Hospital, Nantong, China
| | - Ding Ding
- Department of Radiology, Nantong Third People's Hospital, Nantong, China
| | - Sheng Du
- Department of Radiology, Nantong Third People's Hospital, Nantong, China
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Xiao J, Rahbar H, Hippe DS, Rendi MH, Parker EU, Shekar N, Hirano M, Cheung KJ, Partridge SC. Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis. NPJ Breast Cancer 2021; 7:42. [PMID: 33863924 PMCID: PMC8052427 DOI: 10.1038/s41523-021-00247-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 03/15/2021] [Indexed: 02/02/2023] Open
Abstract
Angiogenesis is a critical component of breast cancer development, and identification of imaging-based angiogenesis assays has prognostic and treatment implications. We evaluated the association of semi-quantitative kinetic and radiomic breast cancer features on dynamic contrast-enhanced (DCE)-MRI with microvessel density (MVD), a marker for angiogenesis. Invasive breast cancer kinetic features (initial peak percent enhancement [PE], signal enhancement ratio [SER], functional tumor volume [FTV], and washout fraction [WF]), radiomics features (108 total features reflecting tumor morphology, signal intensity, and texture), and MVD (by histologic CD31 immunostaining) were measured in 27 patients (1/2016-7/2017). Lesions with high MVD levels demonstrated higher peak SER than lesions with low MVD (mean: 1.94 vs. 1.61, area under the receiver operating characteristic curve [AUC] = 0.79, p = 0.009) and higher WF (mean: 50.6% vs. 22.5%, AUC = 0.87, p = 0.001). Several radiomics texture features were also promising for predicting increased MVD (maximum AUC = 0.84, p = 0.002). Our study suggests DCE-MRI can non-invasively assess breast cancer angiogenesis, which could stratify biology and optimize treatments.
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Affiliation(s)
- Jennifer Xiao
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Habib Rahbar
- Department of Radiology, University of Washington, Seattle, WA, USA
- Breast Imaging, Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Daniel S Hippe
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Mara H Rendi
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | - Neal Shekar
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Michael Hirano
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Kevin J Cheung
- Department of Medicine, Division of Medical Oncology, University of Washington, Seattle, WA, USA
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, WA, USA.
- Breast Imaging, Seattle Cancer Care Alliance, Seattle, WA, USA.
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Jin T, Zhang H, Liu X, Kong X, Makamure J, Chen Z, Alwalid O, Yao Z, Wang J. Enhancement degree of brain metastases: correlation analysis between enhanced T2 FLAIR and vascular permeability parameters of dynamic contrast-enhanced MRI. Eur Radiol 2021; 31:5595-5604. [PMID: 33847812 DOI: 10.1007/s00330-020-07625-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/13/2020] [Accepted: 12/10/2020] [Indexed: 10/21/2022]
Abstract
OBJECTIVES To investigate the correlation between enhancement degrees of brain metastases on contrast-enhanced T2-fluid-attenuated inversion recovery (CE-T2 FLAIR) and vascular permeability parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS Thirty-nine patients with brain metastases were prospectively collected. They underwent non-enhanced T2 FLAIR, DCE-MRI, CE-T2 FLAIR, and contrast-enhanced three-dimensional brain volume imaging (CE-BRAVO). Quantitative parameters of DCE-MRI were evaluated for all lesions, which included volume transfer constant (Ktrans), rate constant (Kep), and fractional volume of the extracellular extravascular space (Ve). Contrast ratio (CR) and percentage increase (PI) values of all lesions on CE-T2 FLAIR were also measured. The tumor enhancement degree on CE-T2 FLAIR in relation to CE-BRAVO was visually classified as higher (group A), equal (group B), and lower (group C). RESULTS A total of 82 brain metastases were evaluated, including 31 in group A, 19 in group B, and 32 in group C. The Ktrans and Kep were negatively correlated with the CR (ρ = - 0.551, p < 0.001 and ρ = - 0.708, p < 0.001, respectively) and PI (ρ = - 0.511, p < 0.001 and ρ = - 0.621, p < 0.001, respectively). The Ktrans and Kep of group A were significantly lower than those of group C (both p < 0.001). No significant difference was found in Ve among the groups (p = 0.327). CONCLUSIONS The enhancement degree of brain metastases on CE-T2 FLAIR is negatively correlated with Ktrans and Kep values, which indicate that vascular permeability parameters may play an important role in explaining the difference in enhancement between CE-T2 FLAIR and CE-BRAVO. KEY POINTS • The enhancement degree on CE-T2 FLAIR was negatively correlated with Ktrans and Kep values. • The vascular permeability of brain metastasis accounted for the difference in enhancement degree between CE-T2 FLAIR and CE-BRAVO. • CE-T2 FLAIR is useful for detecting brain metastases with mild disruption of the blood-brain barrier.
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Affiliation(s)
- Teng Jin
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hua Zhang
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Joyman Makamure
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ziwen Chen
- Department of General Surgery, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, Jiangxi, China
| | - Osamah Alwalid
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
| | - Jing Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Wu S, Okada R, Liu Y, Fang Y, Yan F, Wang C, Li H, Kobayashi H, Chen Y, Tang Q. Quantitative analysis of vascular changes during photoimmunotherapy using speckle variance optical coherence tomography (SV-OCT). BIOMEDICAL OPTICS EXPRESS 2021; 12:1804-1820. [PMID: 33996199 PMCID: PMC8086455 DOI: 10.1364/boe.419163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/24/2021] [Accepted: 02/24/2021] [Indexed: 06/12/2023]
Abstract
Near-infrared (NIR) photoimmunotherapy (NIR-PIT) is an emerging cancer therapy based on a monoclonal antibody and phthalocyanine dye conjugate. Direct tumor necrosis and immunogenic cell death occur during NIR irradiation. However, the alteration of tumor blood vessels and blood volume inside the blood vessels induced by the NIR-PIT process is still unknown. In our study, a speckle variance (SV) algorithm combined with optical coherence tomography (OCT) technology was applied to monitor the change of blood vessels and the alterations of the blood volume inside the blood vessels during and after NIR-PIT treatment. Vascular density and the measurable diameter of the lumen in the blood vessel (the diameter of the region filled with blood) were extracted for quantitively uncovering the alterations of blood vessels and blood volume induced by NIR-PIT treatment. The results indicate that both the density and the diameter of the lumen in the blood vessels decrease during the NIR-PIT process, while histological results indicated the blood vessels were dilated. The increase of permeability of blood vessels could lead to the increase of the blood pool volume within the tumor (shown in histology) and results in the decrease of free-moving red blood cells inside the blood vessels (shown in SV-OCT).
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Affiliation(s)
- Shulian Wu
- College of Photonic and Electronic Engineering, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Provincial Key Laboratory of Photonic Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou, 350007, China
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
- These authors contributed equally to this work
| | - Ryuhei Okada
- National Institute of Health, National Cancer Institute, Molecular Imaging Program, Bldg 10, Room B3B47, Bethesda, Maryland 20892-1088, USA
- These authors contributed equally to this work
| | - Yi Liu
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Yuhong Fang
- College of Photonic and Electronic Engineering, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Provincial Key Laboratory of Photonic Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou, 350007, China
| | - Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Chen Wang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Hui Li
- College of Photonic and Electronic Engineering, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Provincial Key Laboratory of Photonic Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou, 350007, China
| | - Hisataka Kobayashi
- National Institute of Health, National Cancer Institute, Molecular Imaging Program, Bldg 10, Room B3B47, Bethesda, Maryland 20892-1088, USA
| | - Yu Chen
- College of Photonic and Electronic Engineering, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Provincial Key Laboratory of Photonic Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou, 350007, China
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
- Department of Biomedical Engineering, University of Massachusetts, Amherst, MA 01003, USA
| | - Qinggong Tang
- Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
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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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Meng T, He N, He H, Liu K, Ke L, Liu H, Zhong L, Huang C, Yang A, Zhou C, Qian L, Xie C. The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI. Cancer Imaging 2020; 20:88. [PMID: 33317609 PMCID: PMC7737277 DOI: 10.1186/s40644-020-00365-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 12/04/2020] [Indexed: 01/03/2023] Open
Abstract
Background Previous studies have indicated that quantitative MRI (qMR) is beneficial for diagnosis of breast cancer. As a novel qMR technology, synthetic MRI (syMRI) may be advantageous by offering simultaneous generation of T1 and T2 mapping in one scan within a few minutes and without concern to the deposition of the gadolinium contrast agent in cell nucleus. In this study, the potential of quantitative mapping derived from Synthetic MRI (SyMRI) to diagnose breast cancer was investigated. Methods From April 2018 to May 2019, a total of 87 patients with suspicious breast lesions underwent both conventional and SyMRI before treatment. The quantitative metrics derived from SyMRI, including T1 and T2 values, were measured in breast lesions. The diagnostic performance of SyMRI was evaluated with unpaired Student’s t-tests, receiver operating characteristic curve analysis and multivariate logistic regression analysis. The AUCs of quantitative values were compared using Delong test. Results Among 77 patients who met the inclusion criteria, 48 were diagnosed with histopathological confirmed breast cancers, and the rest had benign lesions. The breast cancers showed significantly higher T1 (1611.61 ± 215.88 ms) values and lower T2 (80.93 ± 7.51 ms) values than benign lesions. The area under the ROC curve (AUC) values were 0.931 (95% CI: 0.874–0.989) and 0.883 (95% CI: 0.810–0.956) for T1 and T2 maps, respectively, in diagnostic discrimination between breast cancers and benign lesions. A slightly increased AUC of 0.978 (95% CI: 0.915–0.993) was achieved by combining those two relaxation-based quantitative metrics. Conclusion In conclusion, our preliminary study showed that the quantitative T1 and T2 values obtained by SyMRI could distinguish effectively between benign and malignant breast lesions, and T1 relaxation time showed the highest diagnostic efficiency. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance.
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Affiliation(s)
- Tiebao Meng
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Ni He
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Haoqiang He
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Kuiyuan Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Liangru Ke
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Huiming Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Linchang Zhong
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Chenghui Huang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Anli Yang
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Chunyan Zhou
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Long Qian
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China.
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Liu M, Mao N, Ma H, Dong J, Zhang K, Che K, Duan S, Zhang X, Shi Y, Xie H. Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer. Cancer Imaging 2020; 20:65. [PMID: 32933585 PMCID: PMC7493182 DOI: 10.1186/s40644-020-00342-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 09/02/2020] [Indexed: 12/13/2022] Open
Abstract
Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.
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Affiliation(s)
- Meijie Liu
- School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000.,Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Jianjun Dong
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | | | - Xuexi Zhang
- GE Healthcare, China, Shanghai, P. R. China, 200000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000.
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Ucar EA, Durur-Subasi I, Yilmaz KB, Arikok AT, Hekimoglu B. Quantitative perfusion parameters of benign inflammatory breast pathologies: A descriptive study. Clin Imaging 2020; 68:249-256. [PMID: 32911313 DOI: 10.1016/j.clinimag.2020.08.024] [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: 03/23/2020] [Revised: 07/07/2020] [Accepted: 08/24/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE With this study, we evaluated the perfusion magnetic resonance imaging (MRI) features of benign inflammatory breast lesions for the first time and compared their Ktrans, Kep, Ve values and contrast kinetic curves to benign masses and invasive ductal carcinoma (IDC). MATERIALS AND METHODS Perfusion MRIs of the benign masses (n = 42), inflammatory lesions (n = 25), and IDCs (n = 16) were evaluated retrospectively in terms of Ktrans, Kep, Ve values and contrast kinetic curves and compared by the Kruskal-Wallis, Mann-Whitney U, chi-square tests statistically. Cronbach α test was used to measure intraobserver and interobserver reliability. RESULTS Mean Ktrans values were 0.052 for benign masses, 0.086 for inflammatory lesions and 0.101 for IDC (p < 0.001). Mean Kep values were 0.241 for benign masses, 0.435 for inflammatory lesions and 0.530 for IDC (p < 0.001). Mean Ve values were 0.476 for benign masses, 0.318 for inflammatory lesions and 0.310 for IDC (p = 0.067). For inflammatory and IDC lesions, Ktrans and Kep values were found to be higher and Ve values were lower than benign masses (p = 0.001 for Ktrans, p = 0.001 for Kep, p = 0.045 for Ve). There were excellent or good intra-interobserver reliabilities. For the kinetic curve pattern, most of the benign lesions showed progressive (81%), inflammatory lesions progressive (64%) and IDC lesions plateau (75%) patterns (p < 0.001). CONCLUSIONS On T1 perfusion MRI, similar to IDC lesions, inflammatory lesions demonstrate higher Ktrans and Kep and lower Ve values than benign masses. Quantitative perfusion parameters are not helpful in differentiating them from IDC lesions.
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Affiliation(s)
- Elif Ayse Ucar
- Bor Public Hospital, Clinic of Radiology, Nigde, Turkey; University of Health Sciences, Diskapi Yildirim Beyazit Training and Research Hospital, Clinic of Radiology, Ankara, Turkey.
| | - Irmak Durur-Subasi
- University of Health Sciences, Diskapi Yildirim Beyazit Training and Research Hospital, Clinic of Radiology, Ankara, Turkey; Istanbul Medipol University, Faculty of Medicine, Department of Radiology, Istanbul, Turkey
| | - Kerim Bora Yilmaz
- University of Health Sciences, Diskapi Yildirim Beyazit Training and Research Hospital, Clinic of General Surgery, Ankara, Turkey
| | - Ata Turker Arikok
- University of Health Sciences, Diskapi Yildirim Beyazit Training and Research Hospital, Clinic of Pathology, Ankara, Turkey
| | - Baki Hekimoglu
- University of Health Sciences, Diskapi Yildirim Beyazit Training and Research Hospital, Clinic of Radiology, Ankara, Turkey
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Porembka JH, Ma J, Le-Petross HT. Breast density, MR imaging biomarkers, and breast cancer risk. Breast J 2020; 26:1535-1542. [PMID: 32654416 DOI: 10.1111/tbj.13965] [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: 12/19/2019] [Accepted: 01/03/2020] [Indexed: 11/29/2022]
Abstract
Mammographic breast density and various breast MRI features are imaging biomarkers that can predict a woman's future risk of breast cancer. While mammographic density (MD) has been established as an independent risk factor for the development of breast cancer, MD assessment methods need to be accurate and reproducible for widespread clinical use in stratifying patients based on their risk. In addition, a number of breast MRI biomarkers using contrast-enhanced and noncontrast-enhanced techniques are also being investigated as risk predictors. The validation and standardization of these breast MRI biomarkers will be necessary for population-based clinical implementation of patient risk stratification, as well. This review provides an update on MD assessment methods, breast MRI biomarkers, and their ability to predict breast cancer risk.
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Affiliation(s)
- Jessica H Porembka
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jingfei Ma
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Huong T Le-Petross
- Diagnostic Imaging Division, Department of Breast Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Amado Cabana S, Gallego Ojea JC, Félez Carballada M. Usefulness of dynamic contrast-enhanced magnetic resonance imaging in characterizing ovarian tumors classified as indeterminate at ultrasonography. RADIOLOGIA 2020; 64:S0033-8338(20)30073-4. [PMID: 32650993 DOI: 10.1016/j.rx.2020.05.006] [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: 01/14/2020] [Revised: 05/11/2020] [Accepted: 05/20/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To determine whether there is a significant relationship between the shape of the time-intensity curve on dynamic gadolinium-enhanced magnetic resonance imaging (MRI) of ovarian tumors classified as indeterminate at ultrasonography and the type of lesion (benign, borderline, or malignant) to enable an accurate presurgical diagnosis. MATERIAL AND METHODS We used dynamic contrast-enhanced MRI to study 68 ovarian tumors that were classified as indeterminate at ultrasonography. We included only cases for which a definitive diagnosis (histologic diagnosis or ≥1 year stability on imaging tests) was available. Each case was classified as benign, borderline, or malignant. To analyze the MRI studies, we marked regions of interest in the lesion and in the myometrium (as a reference). We obtained a curve defined by the relation between the intensity of enhancement and time and classified each tumor according to four predefined curve types. We also analyzed semiquantitative parameters. Finally, we compared the results for each of the three groups of tumors. RESULTS We found significant associations (p <0.001) between the curves without early enhancement and benign and borderline lesions as well as between the curves with early enhancement and malignant lesions. Malignant lesions were significantly associated with the semiquantitative enhancement parameters: maximum (p=0.002), maximum relative (p=0.006), and relative (p=0.018). CONCLUSIONS In ovarian tumors classified as indeterminate at ultrasonography, dynamic contrast-enhanced MRI can be useful for classification as benign, borderline, or malignant because the malignant lesions are significantly associated with early enhancement curves.
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Affiliation(s)
- S Amado Cabana
- Servicio de Radiodiagnóstico, Complexo Hospitalario Universitario de Ferrol, Ferrol, A Coruña, España.
| | - J C Gallego Ojea
- Servicio de Radiodiagnóstico, Complexo Hospitalario Universitario de Ferrol, Ferrol, A Coruña, España
| | - M Félez Carballada
- Servicio de Radiodiagnóstico, Complexo Hospitalario Universitario de Ferrol, Ferrol, A Coruña, España
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Kang SR, Kim HW, Kim HS. Evaluating the Relationship Between Dynamic Contrast-Enhanced MRI (DCE-MRI) Parameters and Pathological Characteristics in Breast Cancer. J Magn Reson Imaging 2020; 52:1360-1373. [PMID: 32524658 DOI: 10.1002/jmri.27241] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/13/2020] [Accepted: 05/15/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Dynamic contrast-enhanced MRI (DCE-MRI) is used to evaluate tumor microvasculature. However, studies demonstrating an association between perfusion parameters derived from DCE-MRI and histopathologic characteristics are limited to a small set of histopathologic factors, and the results are inconsistent. PURPOSE To evaluate the relationship between DCE-MRI perfusion parameters and common histopathologic tumor characteristics used to predict angiogenesis and determine prognosis in breast cancer. STUDY TYPE Retrospective. POPULATION In all, 105 breast cancer patients with invasive ductal carcinoma (122 lesions). FIELD STRENGTH/SEQUENCE 3.0T, turbo spin-echo (TSE) T1 -weighted, fat-suppressed T2 -weighted, TSE T2 -weighted, and dynamic unenhanced and contrast-enhanced 3D T1 high-resolution isotropic volume examination. ASSESSMENT One reviewer obtained perfusion parameters (Ktrans , kep , ve , and vp ) of each breast cancer from DCE MRI using the extended Tofts model with a fixed baseline T1 value and a population-based arterial input function. The relationship between DCE-MRI perfusion parameters and histopathologic tumor characteristics used to predict angiogenesis and determine prognosis was evaluated. STATISTICAL TESTS Student's t-test, Mann-Whitney U-test, analysis of variance (ANOVA), and Kruskal-Wallis test were used. RESULTS Triple-negative breast cancers exhibited higher Ktrans and kep than luminal cancers (P < 0.05). Estrogen receptor (ER)-negative tumors showed higher Ktrans than ER-positive tumors (P < 0.05). Progesterone receptor (PR)-negative tumors presented higher ve than PR-positive tumors (P < 0.05). Tumors with higher Ki-67 showed higher kep than tumors with lower Ki-67 (P < 0.05). P53-positive tumors exhibited higher Ktrans and kep than p53-negative tumors (P < 0.05). Higher histologic grade tumors (grade II/III) presented higher Ktrans , kep , vp (P < 0.05) than grade I tumors. Tumors with LVSI presented higher Ktrans and kep than tumors without LVSI (P < 0.05). DATA CONCLUSION Breast cancer presenting higher Ktrans and kep on DCE-MRI was associated with poor prognostic histopathologic factors. Therefore, pretreatment DCE-MRI perfusion parameters may be useful imaging biomarkers for the evaluation of tumor prognosis and angiogenesis. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Se Ri Kang
- Department of Radiology, Wonkwang University Hospital, Iksan, Republic of Korea
| | - Hye Won Kim
- Department of Radiology, Wonkwang University Hospital, Wonkwang University School of Medicine, Iksan, Republic of Korea
| | - Hun Soo Kim
- Department of Pathology, Wonkwang University Hospital, Wonkwang University School of Medicine, Iksan, Republic of Korea
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Correlation analysis of apparent diffusion coefficient value and P53 and Ki-67 expression in esophageal squamous cell carcinoma. Magn Reson Imaging 2020; 68:183-189. [DOI: 10.1016/j.mri.2020.01.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 12/20/2022]
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Moawad AW, Szklaruk J, Lall C, Blair KJ, Kaseb AO, Kamath A, Rohren SA, Elsayes KM. Angiogenesis in Hepatocellular Carcinoma; Pathophysiology, Targeted Therapy, and Role of Imaging. J Hepatocell Carcinoma 2020; 7:77-89. [PMID: 32426302 PMCID: PMC7188073 DOI: 10.2147/jhc.s224471] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 12/24/2019] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, usually occurring on a background of liver cirrhosis. HCC is a highly vascular tumor in which angiogenesis plays a major role in tumor growth and spread. Tumor-induced angiogenesis is usually related to a complex interplay between multiple factors and pathways, with vascular endothelial growth factor being a major player in angiogenesis. In the past decade, understanding of tumor-induced angiogenesis has led to the emergence of novel anti-angiogenic therapies, which act by reducing neo-angiogenesis, and improving patient survival. Currently, Sorafenib and Lenvatinib are being used as the first-line treatment for advanced unresectable HCC. However, a disadvantage of these agents is the presence of numerous side effects. A major challenge in the management of HCC patients being treated with anti-angiogenic therapy is effective monitoring of treatment response, which decides whether to continue treatment or to seek second-line treatment. Several criteria can be used to assess response to treatment, such as quantitative perfusion on cross-sectional imaging and novel/emerging MRI techniques, including a host of known and emerging biomarkers and radiogenomics. This review addresses the pathophysiology of angiogenesis in HCC, accurate imaging assessment of angiogenesis, monitoring effects of anti-angiogenic therapy to guide future treatment and assessing prognosis.
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Affiliation(s)
- Ahmed W Moawad
- Department of Diagnostic Radiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Janio Szklaruk
- Department of Diagnostic Radiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Katherine J Blair
- Department of Diagnostic Radiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Ahmed O Kaseb
- Department of Gastrointestinal Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Amita Kamath
- Department of Radiology, Icahn School of Medicine at Mount Sinai West, New York, NY, USA
| | - Scott A Rohren
- School of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Khaled M Elsayes
- Department of Diagnostic Radiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
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Zhou X, Gao F, Duan S, Zhang L, Liu Y, Zhou J, Bai G, Tao W. Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer. Phys Eng Sci Med 2020; 43:517-524. [PMID: 32524436 DOI: 10.1007/s13246-020-00852-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 02/11/2020] [Indexed: 01/03/2023]
Abstract
To explore radiomic features of pharmacokinetic dynamic contrast-enhanced (Pk-DCE) MRI on the extensive Tofts model to diagnose breast cancer and predict molecular phenotype. Breast lesions enrolled must undergo Pk-DCE MRI before treatment or puncture, and be identified as primary lesions by pathology. Ktrans, Kep, Ve and Vp were generated on the extensive Tofts model. Radiomic features (histogram, geometry and texture features) were extracted from parametric maps and selected by LASSO. The subjects were divided into training and validation cohort with a ratio of 4:1 to construct model in diagnosis of breast cancer. Feature analysis was made to predict the molecular phenotype. Area under curve (AUC), sensitivity, specificity and accuracy were used to evaluate radiomic features. DeLong's test was performed to compare AUC values. 228 breast lesions met the criteria were used to discrimination and 126 malignant lesions were used to study molecular phenotypes. The number of training cohort and validation cohort were 182 and 46, respectively. The AUC of Ktrans, Kep, Ve, and Vp was 0.95, 0.93, 0.89, and 0.96, and their accuracy was 85%, 89%, 89%, 94% respectively in diagnosis of breast lesions, while their AUC was 0.71 to 0.77, 0.61 to 0.68, and 0.67 to 0.74 to predict ER/PR, Her-2, and Ki-67. There was no significant difference among parameters (P > 0.05). Radiomic features based on Pk-DCE MRI have an advantage to diagnose breast cancer and less ability to predict molecular phenotypes, which are beneficial to guide clinical treatment of breast lesions in some extent.
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Affiliation(s)
- Xiaoyu Zhou
- Research Center of Internet Things (Sensory Mine), China University of Mining and Technology, Xuzhou, People's Republic of China.,Faculty of Mechanical Electronic and Information Engineering, Jiangsu Vocational College of Finance and Economics, Huai'an, People's Republic of China
| | - Feng Gao
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Shaofeng Duan
- GE Healthcare China, Shanghai, People's Republic of China
| | - Lianmei Zhang
- Department of Pathology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, People's Republic of China
| | - Yan Liu
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huanghe Road No. 1, Huai'an, 223300, Jiangsu Province, People's Republic of China
| | - Junyi Zhou
- Department of Medical Imaging, Jiangsu University, Zhenjiang, People's Republic of China
| | - Genji Bai
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huanghe Road No. 1, Huai'an, 223300, Jiangsu Province, People's Republic of China.
| | - Weijing Tao
- Department of Nuclear Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huanghe Road No.1, Huai'an, 223300, Jiangsu Province, People's Republic of China.
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Zhang Y, Yue B, Zhao X, Chen H, Sun L, Zhang X, Hao D. Benign or Malignant Characterization of Soft-Tissue Tumors by Using Semiquantitative and Quantitative Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Can Assoc Radiol J 2020; 71:92-99. [PMID: 32062994 DOI: 10.1177/0846537119888409] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To evaluate the efficacy of the semiquantitative and quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating between benign and malignant soft-tissue tumors. METHODS A total of 45 patients with pathologically confirmed soft-tissue tumors (15 benign and 30 malignant tumors) underwent DCE-MRI. The semiquantitative parameters assessed were as follows: time to peak (TTP), maximum concentration (MAX Conc), area under the curve of time-concentration curve (AUC-TC), and maximum rise slope (MAX Slope). Quantitative DCE-MRI was analyzed with the extended Tofts-Kety model to assess the following quantitative parameters: volume transfer constant (Ktrans), microvascular permeability reflux constant (Kep), and distribute volume per unit tissue volume (Ve). Data were evaluated using the independent t test or Mann-Whitney U test and receiver operating characteristic (ROC) curves. RESULTS The TTP (P = .0035), MAX Conc (P = .0018), AUC-TC (P = .0018), MAX Slope (P = .0018), Ktrans (P = .0018), and Kep (P = .0035) were significantly different between the benign and malignant soft-tissue tumors. The AUC of the ROC curve demonstrated the diagnostic potential of TTP (0.778), MAX Conc (0.849), AUC-TC (0.831), MAX Slope (0.847), Ktrans (0.836), Kep (0.778), and Ve (0.638). CONCLUSIONS The use of semiquantitative and quantitative parameters of DCE-MRI enabled differentiation between benign and malignant soft-tissue tumors. The values of TTP were lower, while those of MAX Conc, AUC-TC, MAX Slope, Ktrans, and Kep were higher in malignant than in benign tumors.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bin Yue
- Department of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaodan Zhao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haisong Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lingling Sun
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | | | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Zhang Y, Tan Y, Dong C, Gao S, Xu W, Chen H. Evaluating the scope of intramedullary invasion of malignant bone tumor by DCE-MRI quantitative parameters in animal study. J Bone Oncol 2019; 19:100269. [PMID: 31799112 PMCID: PMC6881657 DOI: 10.1016/j.jbo.2019.100269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 11/14/2019] [Accepted: 11/16/2019] [Indexed: 02/07/2023] Open
Abstract
UNLABELLED The purpose was to analyze the value of quantitative parameters of DCE-MRI in evaluating micro-infiltration of malignant bone tumors. METHODS Thirty-nine New Zealand white rabbits were used to establish malignant bone tumor models by implanting VX2 tumor fragments into the right tibiae. After three weeks, models were examined by conventional MRI and DCE-MRI; then the right tibiae were cut into sagittal sections and partitioned into histology slices for comparison with microscopic findings. Micro-infiltration groups were selected and the range of infiltration was determined under the microscope, and corresponding DCE images analyzed to obtain the quantitative parameters include Ktrans, Kep, ve and vp in parenchyma areas, micro-infiltration areas and simple edema areas. One-way ANOVA was used to compare the differences of the parameters between the three areas. Receiver operating characteristic curves (ROCs) were plotted to determine the accuracy of different parameters by area under curves (AUCs). RESULTS 22 cases (22/39, 56.4%) were included in the micro-infiltration group and the infiltration depth ranged from 1.3 mm to 4.6 mm, with an average depth of 3.2 mm ± 0.8 mm. The statistical results of quantitative parameters in the three areas were as follows: Ktrans values were (0.494 ± 0.052), (0.403 ± 0.049), (0.173 ± 0.047) min-1 (p = =0.000), Kep values were (1.959 ± 0.65), (1.528 ± 0.372), (1.174 ± 0.486) min-1 (p = =0.000), ve values were (0.247 ± 0.068), (0.283 ± 0.057), (0.168 ± 0.062) min-1 (p = =0.000), vp values were (0.125 ± 0.036), (0.108 ± 0.033), (0.098 ± 0.025) min-1 (p = =0.022), respectively. Ktrans and Kep values had significant difference in the three areas after comparing between-groups, respectively. However, there were no significant difference in vp values between parenchyma and micro-infiltration areas (p = =0.078), micro-infiltration and simple edema areas (p = =0.315), and ve values between parenchyma and micro-infiltration areas (p = =0.056). The ve values were higher in parenchyma and micro-infiltration areas then simple edema areas. Ktrans had highest accuracy in differentiating different areas (AUC > 0.9), respectively. CONCLUSION Quantitative parameters Ktrans, Kep and ve can assess the extent of intramedullary invasion of malignant bone tumors. Ktrans have highest accuracy in differentiating different regions.
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Affiliation(s)
- Yuan Zhang
- Department of Radiology, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, 430000, Hubei, China
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Yiqing Tan
- Department of Radiology, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, 430000, Hubei, China
| | - Cheng Dong
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Sai Gao
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Wenjian Xu
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Haisong Chen
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
- Corresponding author.
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Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps. Radiol Med 2019; 125:109-116. [PMID: 31696388 DOI: 10.1007/s11547-019-01100-1] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/24/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study is to develop a radiomics model for predicting the Ki-67 proliferation index in patients with invasive ductal breast cancer through magnetic resonance imaging (MRI) preoperatively. MATERIALS AND METHODS A total of 128 patients who were clinicopathologically diagnosed with invasive ductal breast cancer were recruited. This cohort included 32 negative Ki67 expression (Ki67 proliferation index < 14%) and 96 cases with positive Ki67 expression (Ki67 proliferation index ≥ 14%). All patients had undergone diffusion-weighted imaging (DWI) MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from apparent diffusion coefficient (ADC) maps which were obtained by DWI-MRI from patients with invasive ductal breast cancer. 80% of the patients were divided into training set to build radiomics model, and the rest into test set to evaluate its performance. The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features, and then, the logistic regression (LR) model was established using fivefold cross-validation to predict the Ki-67 index. The performance was evaluated by receiver-operating characteristic (ROC) analysis, accuracy, sensitivity and specificity. RESULTS Quantitative imaging features (n = 1029) were extracted from ADC maps, and 11 features were selected to construct the LR model. Good identification ability was exhibited by the ADC-based radiomics model, with areas under the ROC (AUC) values of 0.75 ± 0.08, accuracy of 0.71 in training set and 0.72, 0.70 in test set. CONCLUSIONS The ADC-based radiomics model is a feasible predictor for the Ki-67 index in patients with invasive ductal breast cancer. Therefore, we proposed that three-dimensional imaging features from ADC maps could be used as candidate biomarker for preoperative prediction the Ki-67 index noninvasively.
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Meyer HJ, Hamerla G, Leifels L, Höhn AK, Surov A. Histogram analysis parameters derived from DCE-MRI in head and neck squamous cell cancer – Associations with microvessel density. Eur J Radiol 2019; 120:108669. [DOI: 10.1016/j.ejrad.2019.108669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 09/04/2019] [Accepted: 09/10/2019] [Indexed: 01/21/2023]
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Fan M, Liu Z, Xie S, Xu M, Wang S, Gao X, Li L. Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma. Phys Med Biol 2019; 64:215001. [PMID: 31470420 DOI: 10.1088/1361-6560/ab3fd3] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Tumour histological grade has prognostic implications in breast cancer. Tumour features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and T2-weighted (T2W) imaging can provide related and complementary information in the analysis of breast lesions to improve MRI-based histological status prediction in breast cancer. A dataset of 167 patients with invasive ductal carcinoma (IDC) was assembled, consisting of 72 low/intermediate-grade and 95 high-grade cases with preoperative DCE-MRI and T2W images. The data cohort was separated into development (n = 111) and validation (n = 56) cohorts. Each tumour was segmented in the precontrast and the intermediate and last postcontrast DCE-MR images and was mapped to the tumour in the T2W images. Radiomic features, including texture, morphology, and histogram distribution features in the tumour image, were extracted for those image series. Features from the DCE-MR and T2W images were fused by a canonical correlation analysis (CCA)-based method. The support vector machine (SVM) classifiers were trained and tested on the development and validation cohorts, respectively. SVM-based recursive feature elimination (SVM-RFE) was adopted to identify the optimal features for prediction. The areas under the ROC curves (AUCs) for the T2W images and the DCE-MRI series of precontrast, intermediate and last postcontrast images were 0.750 ± 0.047, 0.749 ± 0.047, and 0.788 ± 0.045, respectively, for the development cohort and 0.715 ± 0.068, 0.704 ± 0.073, and 0.744 ± 0.067, respectively, for the validation cohort. After the CCA-based fusion of features from the DCE-MRI series and T2W images, the AUCs increased to 0.751 ± 0.065, 0.803 ± 0.0600 and 794 ± 0.060 in the validation cohort. Moreover, the method of fusing features between DCE-MRI and T2W images using CCA achieved better performance than the concatenation-based feature fusion or classifier fusion methods. Our results demonstrated that anatomical and functional MR images yield complementary information, and feature fusion of radiomic features by matrix transformation to optimize their correlations produced a classifier with improved performance for predicting the histological grade of IDC.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
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Surov A, Meyer HJ, Wienke A. Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions. BMC Cancer 2019; 19:955. [PMID: 31615463 PMCID: PMC6794799 DOI: 10.1186/s12885-019-6201-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 09/24/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions. METHODS MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions. RESULTS The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10- 3 mm2/s and the mean value of the benign lesions was 1.5 × 10- 3 mm2/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10- 3 mm2/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area). CONCLUSION An ADC threshold of 1.00 × 10- 3 mm2/s can be recommended for distinguishing breast cancers from benign lesions.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr. 20, 04103, Leipzig, Germany. .,Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr. 20, 04103, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str. 8, 06097, Halle, Germany
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Zhou Y, Sun Y, Yang W, Lu Z, Huang M, Lu L, Zhang Y, Feng Y, Chen W, Feng Q. Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2352-2363. [PMID: 30908198 DOI: 10.1109/tmi.2019.2906493] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A "correlation-weighted" constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients' map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency.
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Jagannathan NR. Application of in vivo MR methods in the study of breast cancer metabolism. NMR IN BIOMEDICINE 2019; 32:e4032. [PMID: 30456917 DOI: 10.1002/nbm.4032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 08/25/2018] [Accepted: 09/26/2018] [Indexed: 06/09/2023]
Abstract
In the last two decades, various in vivo MR methodologies have been evaluated for their potential in the study of cancer metabolism. During malignant transformation, metabolic alterations occur, leading to morphological and functional changes. Among various MR methods, in vivo MRS has been extensively used in breast cancer to study the metabolism of cells, tissues or whole organs. It provides biochemical information at the metabolite level. Altered choline, phospholipid and energy metabolism has been documented using proton (1 H), phosphorus (31 P) and carbon (13 C) isotopes. Increased levels of choline-containing compounds, phosphomonoesters and phosphodiesters in breast cancer, which are indicative of altered choline and phospholipid metabolism, have been reported using in vivo, in vitro and ex vivo NMR studies. These changes are reversed on successful therapy, which depends on the treatment regimen given. Monitoring the various tumor intermediary metabolic pathways using nuclear spin hyperpolarization of 13 C-labeled substrates by dynamic nuclear polarization has also been recently reported. Furthermore, the utility of various methods such as diffusion, dynamic contrast and perfusion MRI have also been evaluated to study breast tumor metabolism. Parameters such as tumor volume, apparent diffusion coefficient, volume transfer coefficient and extracellular volume ratio are estimated. These parameters provide information on the changes in tumor microstructure, microenvironment, abnormal vasculature, permeability and grade of the tumor. Such changes seen during cancer progression are due to alterations in the tumor metabolism, leading to changes in cell architecture. Due to architectural changes, the tissue mechanical properties are altered; this can be studied using magnetic resonance elastography, which measures the elastic properties of tissues. Moreover, these structural MRI methods can be used to investigate the effect of therapy-induced changes in tumor characteristics. This review discusses the potential of various in vivo MR methodologies in the study of breast cancer metabolism.
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