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Wang X, Shu X, He P, Cai Y, Geng Y, Hu X, Sun Y, Xiao H, Zheng W, Song Y, Xue Y, Jiang R. Ultra-high b-value DWI accurately identifies isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. Eur Radiol 2024; 34:6751-6762. [PMID: 38528135 DOI: 10.1007/s00330-024-10708-5] [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: 11/28/2023] [Revised: 02/08/2024] [Accepted: 02/23/2024] [Indexed: 03/27/2024]
Abstract
OBJECTIVES To distinguish isocitrate dehydrogenase (IDH) genotypes and tumor subtypes of adult-type diffuse gliomas based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5) in 2021 using standard, high, and ultra-high b-value diffusion-weighted imaging (DWI). MATERIALS AND METHODS This prospective study enrolled 70 patients with adult-type diffuse gliomas who underwent multiple b-value DWI. Apparent diffusion coefficient (ADC) values including ADCb500/b1000, ADCb500/b2000, ADCb500/b3000, ADCb500/b4000, ADCb500/b6000, ADCb500/b8000, and ADCb500/b10000 in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The ADC ratios of TP/NAWM were assessed for correlations with IDH genotypes, tumor subtypes, and Ki-67 status; diagnostic performances were compared. RESULTS All ADCs were significantly higher in IDH mutant gliomas than in IDH wild-type gliomas (p < 0.01 for all); ADCb500/b8000 had the highest area under the curve (AUC) of 0.866. All ADCs were significantly lower in glioblastoma than in astrocytoma (p < 0.01 for all). ADCs other than ADCb500/b1000 were significantly lower in glioblastoma than in oligodendroglioma (p < 0.05 for all). ADCb500/b8000 and ADCb500/b10000 were significantly higher in oligodendroglioma than in astrocytoma (p = 0.034 and 0.023). The highest AUCs were 0.818 for ADCb500/b6000 when distinguishing glioblastoma from astrocytoma, 0.979 for ADCb500/b8000 and ADCb500/b10000 when distinguishing glioblastoma from oligodendroglioma, and 0.773 for ADCb500/b10000 when distinguishing astrocytoma from oligodendroglioma. Additionally, all ADCs were negatively correlated with Ki-67 status (p < 0.05 for all). CONCLUSION Ultra-high b-value DWI can reliably separate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using WHO CNS5 criteria. CLINICAL RELEVANCE STATEMENT Ultra-high b-value diffusion-weighted imaging can accurately distinguish isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas, which may facilitate personalized treatment and prognostic assessment for patients with glioma. KEY POINTS • Ultra-high b-value diffusion-weighted imaging can accurately distinguish subtle differences in water diffusion among biological tissues. • Ultra-high b-value diffusion-weighted imaging can reliably separate isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. • Compared with standard b-value diffusion-weighted imaging, high and ultra-high b-value diffusion-weighted imaging demonstrate better diagnostic performances.
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Affiliation(s)
- Xueqin Wang
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
- School of Medical Imaging, Fujian Medical University, Fuzhou, 350004, China
| | - Xinru Shu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350004, China
| | - Pingping He
- School of Medical Imaging, Fujian Medical University, Fuzhou, 350004, China
| | - Yiting Cai
- School of Medical Imaging, Fujian Medical University, Fuzhou, 350004, China
| | - Yingqian Geng
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Xiaomei Hu
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yifan Sun
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Huinan Xiao
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Wanyi Zheng
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthcare, Shanghai, China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China.
- School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350004, China.
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Xu J, Sheng Y, Li H, Yang Z, Ren Y, Wang H. A data-driven intravoxel mean diffusivities distribution approach for molecular classifications and MIB-1 prediction of gliomas. Med Phys 2024. [PMID: 38949565 DOI: 10.1002/mp.17280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND Measuring non-parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes. PURPOSE We developed a data-driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB-1 labeling index (LI) levels and molecular status of gliomas. METHODS An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR∈ [ 30 , 300 ] $ \in [ {30,\ 300} ]$ . Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ± $ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo-echo planar imaging (SE-EPI) diffusion-weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C-means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions (f n , n = 1 - 6 ${{f}_n},\ n = 1 -6$ ) and diffusivities of the maximum peaks (D p e a k ${{D}_{peak}}$ ). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann-Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation. RESULTS The simulation results showed that the FCN-based MDD approach (FCN-MDD) achieved higher CSS than non-negative least squares-based MDD (NNLS-MDD). For in vivo data, the spectra of ET and NET obtained by FCN-MDD are more distinguishable than NNLS-MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non-enhancing tumor, edema, and necrosis).f 3 , f 4 , D p e a k ${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB-1 respectively (r = 0.568 , r = - 0.521 , r = - 0.654 $r = 0.568,\ r = - 0.521,\ r = - 0.654$ , allp < 0.001 $p < 0.001$ ). The AUC ofD p e a k ${{D}_{peak}}$ for predicting MIB-1 LI levels was 0.900 (95% CI, 0.826-0.974), versus 0.781 (0.677-0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model (f 1 + f 3 ${{f}_1} + {{f}_3}$ ) was 0.873 (95% CI, 0.802-0.944). CONCLUSION The proposed FCN-MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS-MDD method. FCN-MDD also enabled qualitative and quantitative evaluation of the composition of gliomas.
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Affiliation(s)
- Junqi Xu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yaru Sheng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hao Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zidong Yang
- USC Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
- Laboratory of FMRI Technology, USC Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Fourth People's Hospital, Tongji University School of Medicine, Shanghai, China
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Yu M, Ge Y, Wang Z, Zhang Y, Hou X, Chen H, Chen X, Ji N, Li X, Shen H. The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study. J Neurooncol 2024; 167:305-313. [PMID: 38424338 DOI: 10.1007/s11060-024-04609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024]
Abstract
PURPOSE Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. METHODS Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. RESULTS 64 patients (female, 21; male, 43; age, 47.0 ± 13.7 years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). CONCLUSIONS The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.
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Affiliation(s)
- Meimei Yu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Radiology, The First People's Hospital of Longquanyi District, Chengdu, Sichuan Province, China
| | - Ying Ge
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Radiology, Beijing Huimin Hospital, Beijing, China
| | - Zixuan Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xinyi Hou
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Hongyan Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huicong Shen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
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Weng C, Yang Y, Yang L, Hu C, Ma X, Li G. Evaluations of the diagnostic performance of ZOOMit diffusion-weighted imaging and conventional diffusion-weighted imaging for breast lesions. Quant Imaging Med Surg 2023; 13:8478-8488. [PMID: 38106248 PMCID: PMC10722069 DOI: 10.21037/qims-23-415] [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: 03/29/2023] [Accepted: 10/07/2023] [Indexed: 12/19/2023]
Abstract
Background Diffusion-weighted imaging (DWI) is valuable in the screening, diagnosis, and grading of breast lesions. However, conventional DWI (C-DWI) is prone to chemical shift and distortion. ZOOMit DWI (Z-DWI), as an advanced magnetic resonance imaging (MRI) technique, applies two spatially selective parallel excitation pulses to focus sampling in the hope of obtaining more valuable information. This study aimed to evaluate and compare the image quality and feasibility of Z-DWI with those of C-DWI in breast lesions. Methods The study included 51 patients with breast lesions who underwent breast MRI from May 2021 to February 2022. All patients received Z-DWI and C-DWI sequences, with b values selected as 50 and 800 s/mm2 (Z-DWIb50, Z-DWIb800, C-DWIb50, and C-DWIb800). Apparent diffusion coefficient (ADC) values based on Z-DWI and C-DWI were calculated. For qualitative analysis, four image quality parameters were selected and assessed on a 4-point Likert scale (1 = poor and 4 = excellent). For quantitative analysis, ADC, relative ADC (rADC), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and tumor-to-parenchymal contrast (TPC) values were selected for comparison. Results Z-DWI had higher scores compared to C-DWI in terms of lesion conspicuity, anatomical details, distortion and artifacts, and overall image quality (P<0.05). Meanwhile, the agreement between the two readers was reasonably good [intraclass correlation coefficient (ICC) range, 0.360-0.881]. The SNR of Z-DWIb800 was better than that of C-DWIb800 (P<0.001). The Z-DWI ADC and rADC values of breast lesions were statistically significantly lower than those of C-DWI (mean ADC: P<0.001; rADC; P=0.005). Conclusions Z-DWI sequences were shown to have superior image quality. The ADC map of Z-DWI is more conducive to the imaging evaluation of breast lesions.
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Affiliation(s)
- Chunjiao Weng
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yiwen Yang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ling Yang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xinxing Ma
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guangzheng Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Yang X, Hu C, Xing Z, Lin Y, Su Y, Wang X, Cao D. Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI. Eur Radiol 2023; 33:7003-7014. [PMID: 37133522 DOI: 10.1007/s00330-023-09695-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 02/19/2023] [Accepted: 03/09/2023] [Indexed: 05/04/2023]
Abstract
OBJECTIVE Noninvasive detection of molecular status of astrocytoma is of great clinical significance for predicting therapeutic response and prognosis. We aimed to evaluate whether morphological MRI (mMRI), SWI, DWI, and DSC-PWI could predict Ki-67 labeling index (LI), ATRX mutation, and MGMT promoter methylation status in IDH mutant (IDH-mut) astrocytoma. METHODS We retrospectively analyzed mMRI, SWI, DWI, and DSC-PWI in 136 patients with IDH-mut astrocytoma.The features of mMRI and intratumoral susceptibility signals (ITSS) were compared using Fisher exact test or chi-square tests. Wilcoxon rank sum test was used to compare the minimum ADC (ADCmin), and minimum relative ADC (rADCmin) of IDH-mut astrocytoma in different molecular markers status. Mann-Whitney U test was used to compare the rCBVmax of IDH-mut astrocytoma with different molecular markers status. Receiver operating characteristic curves was performed to evaluate their diagnostic performances. RESULTS ITSS, ADCmin, rADCmin, and rCBVmax were significantly different between high and low Ki-67 LI groups. ITSS, ADCmin, and rADCmin were significantly different between ATRX mutant and wild-type groups. Necrosis, edema, enhancement, and margin pattern were significantly different between low and high Ki-67 LI groups. Peritumoral edema was significantly different between ATRX mutant and wild-type groups. Grade 3 IDH-mut astrocytoma with unmethylated MGMT promoter was more likely to show enhancement compared to the methylated group. CONCLUSIONS mMRI, SWI, DWI, and DSC-PWI were shown to have the potential to predict Ki-67 LI and ATRX mutation status in IDH-mut astrocytoma. A combination of mMRI and SWI may improve diagnostic performance for predicting Ki-67 LI and ATRX mutation status. CLINICAL RELEVANCE STATEMENT Conventional MRI and functional MRI (SWI, DWI, and DSC-PWI) can predict Ki-67 expression and ATRX mutation status of IDH mutant astrocytoma, which may help clinicians determine personalized treatment plans and predict patient outcomes. KEY POINTS • A combination of multimodal MRI may improve the diagnostic performance to predict Ki-67 LI and ATRX mutation status. • Compared with IDH-mutant astrocytoma with low Ki-67 LI, IDH-mutant astrocytoma with high Ki-67 LI was more likely to show necrosis, edema, enhancement, poorly defined margin, higher ITSS levels, lower ADC, and higher rCBV. • ATRX wild-type IDH-mutant astrocytoma was more likely to show edema, higher ITSS levels, and lower ADC compared to ATRX mutant IDH-mutant astrocytoma.
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Affiliation(s)
- Xiefeng Yang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China
| | - Chengcong Hu
- Department of Pathology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, 20 Cha-Zhong Road, Fuzhou, 350005, People's Republic of China
| | - Zhen Xing
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China
| | - Yu Lin
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China
| | - Yan Su
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China
| | - Xingfu Wang
- Department of Pathology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, 20 Cha-Zhong Road, Fuzhou, 350005, People's Republic of China.
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People's Republic of China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People's Republic of China.
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Zhao D, Peng S, Xiao H, Li Q, Chai Y, Sun H, Liu R, Yao L, Ma L. High-Performance T1- T2 Dual-Modal MRI Contrast Agents through Interface Engineering. ACS APPLIED BIO MATERIALS 2023. [PMID: 37229527 DOI: 10.1021/acsabm.3c00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Iron oxide nanoparticles (IONPs) have been developed as contrast agents for T1- or T2-weighted magnetic resonance imaging (MRI) on account of their excellent physicochemical and biological properties. However, general strategies to improve longitudinal relaxivity (r1) often decrease transverse relaxivity (r2), thus synchronously strengthening the T1 and T2 enhancement effect of IONPs remains a challenge. Here, we report interface regulation and size tailoring of a group of FePt@Fe3O4 core-shell nanoparticles (NPs), which possess high r1 and r2 relaxivities. The increase of r1 and r2 is due to the enhancement of the saturation magnetization (Ms), which is a result of the strengthened exchange coupling across the core-shell interface. In vivo subcutaneous tumor study and brain glioma imaging revealed that FePt@Fe3O4 NPs can serve as a favorable T1-T2 dual-modal contrast agent. We envision that the core-shell NPs, through interface engineering, have great potential in preclinical and clinical MRI applications.
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Affiliation(s)
- Dan Zhao
- Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Shibo Peng
- Department of Radiology, First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, PLA Medical School, Beijing 100853, China
| | - Hanzhang Xiao
- Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qilong Li
- Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yahong Chai
- Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongxia Sun
- Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruping Liu
- Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Li Yao
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry Chinese Academy of Science, Beijing 100190, China
| | - Lin Ma
- Department of Radiology, First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, PLA Medical School, Beijing 100853, China
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He J, Ren J, Niu G, Liu A, Wu Q, Xie S, Ma X, Li B, Wang P, Shen J, Wu J, Gao Y. Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status. BMC Med Imaging 2022; 22:137. [PMID: 35931979 PMCID: PMC9354364 DOI: 10.1186/s12880-022-00865-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 07/28/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma. Methods In this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05 by multivariate logistic regression were retained to establish clinical model, T1WI model, T2WI model, T1 + C (T1WI contrast enhancement model, DWI model and ADC model, multi sequence model. Clinical features were combined with multi sequence model to establish a combined model. The predictive performance was validated by receiver operating characteristic curve (ROC analysis and decision curve analysis (DCA). Results The combined model showed the better performance in some groups of genotype status among some models (IDH AUC = 0.93, MGMT AUC = 0.88, TERT AUC = 0.76). Multi sequence model performed better than single sequence model in IDH, MGMT, TERT. There was no significant difference among the models in predicting 1p/19q status. Decision curve analysis showed combined model has higher clinical benefit than multi sequence model. Conclusion Multi sequence model is an effective method to identify the genotype status of cerebral glioma. Combined with clinical models can better distinguish genotype status of glioma. Key Points The combined model showed the higher performance compare with other models in predicting genotype status of IDH, MGMT, TERT. Multi sequence model showed a better predictive model than that of a single sequence model. Compared with other models, the combined model and multi sequence model show no advantage in prediction of 1p/19q status.
Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00865-8.
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Affiliation(s)
- Jinlong He
- Graduate School, Tianjin Medical University, Tianjin, 300070, China.,Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Jialiang Ren
- GE Healthcare Co., Ltd., Shanghai, 210000, China
| | - Guangming Niu
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Aishi Liu
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Qiong Wu
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Shenghui Xie
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Xueying Ma
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Bo Li
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Peng Wang
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
| | - Yang Gao
- Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China.
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Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2022; 12:752119. [PMID: 35069406 PMCID: PMC8770750 DOI: 10.3389/fneur.2021.752119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.
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Affiliation(s)
- Caijie Qin
- Institute of Information Engineering, Sanming University, Sanming, China
| | - Wenxing Hu
- University of New South Wales, Sydney, NSW, Australia
| | - Xinsheng Wang
- School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China
| | - Xibo Ma
- CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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