1
|
Meng N, Jiang H, Sun J, Shen L, Wang X, Zhou Y, Wu Y, Fu F, Yuan J, Yang Y, Wang Z, Wang M. Amide Proton Transfer-Weighted Imaging and Multiple Models Intravoxel Incoherent Motion-Based 18F-FDG PET/MRI for Predicting Progression-Free Survival in Non-Small Cell Lung Cancer. J Magn Reson Imaging 2024; 60:125-135. [PMID: 37850873 DOI: 10.1002/jmri.29037] [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: 06/25/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Amide proton transfer-weighted imaging (APTWI) and multiple models intravoxel incoherent motion (IVIM) based 18F-FDG PET/MR could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored. PURPOSE To determine whether pretreatment APTWI, mono-, bi-, and stretched-exponential model IVIM, and 18F-FDG PET-derived parameters of the primary lesion may be associated with progression-free survival (PFS) in NSCLC. STUDY TYPE Prospective. POPULATION Seventy-seven patients (mean age, 62 years, range, 20-81 years) with 37 men and 40 women were included. FIELD STRENGTH/SEQUENCE 3.0 T 18F-FDG PET/MRI, single shot echo planar imaging sequences for IVIM and fast spin-echo sequences with magnetization transfer pulses for APTWI. ASSESSMENT Patient clinical characteristics (age, sex, smoke, subtype, TNM stage, and surgery), PFS (chest CT every 3 months, median follow-up was 18 months, range, 4-27 months), and APTWI (MTRasym(3.5 ppm)), IVIM (ADCstand, D, D*, f, DDC, and α), and 18F-FDG PET (SUVmax, MTV, and TLG) parameters were recorded. STATISTICAL TESTS Proportional hazards model, concordance index, calibration curve, decision curve analysis (DCA), and Log-rank test. A P value <0.05 was considered statistically significant. RESULTS Histological subtype, TNM stage, MTV, D*, and MTRasym(3.5 ppm) were all independent predictors of PFS. A prediction model based on these predictors was developed with a C-index of 0.895 (95% CI: 0.839-0.951), which was significantly superior to each of the above predictors alone (C-index = 0.629, 0.707, 0.692, 0.678, and 0.558, respectively). The calibration curve and DCA indicated good consistency and clinical utility of the prediction model, respectively. Log-rank test results showed a significant difference in PFS between the high- and low-risk groups. DATA CONCLUSION APTWI and multiple models IVIM based 18F-FDG PET/MRI can be used for PFS assessment in NSCLC. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
- Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
| | - Han Jiang
- Department of Medical Imaging, Xinxiang Medical University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Jing Sun
- Department of Pediatrics, Zhengzhou Central Hospital Affiliated to Zhengzhou University & Zhengzhou Central Hospital, Zhengzhou, China
| | - Lei Shen
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Xinhui Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Yihang Zhou
- Department of Medical Imaging, Xinxiang Medical University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Healthcare Group, Beijing, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
- Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
| |
Collapse
|
2
|
Foltyn-Dumitru M, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities. Neuro Oncol 2024; 26:1099-1108. [PMID: 38153923 PMCID: PMC11145444 DOI: 10.1093/neuonc/noad259] [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: 09/10/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. METHODS A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. RESULTS Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). CONCLUSIONS Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.
Collapse
Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| |
Collapse
|
3
|
Wu M, Jiang T, Guo M, Duan Y, Zhuo Z, Weng J, Xie C, Sun J, Li J, Cheng D, Liu X, Du J, Zhang X, Zhang Y, Liu Y. Amide proton transfer-weighted imaging and derived radiomics in the classification of adult-type diffuse gliomas. Eur Radiol 2024; 34:2986-2996. [PMID: 37855851 DOI: 10.1007/s00330-023-10343-6] [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: 05/23/2023] [Revised: 08/27/2023] [Accepted: 09/05/2023] [Indexed: 10/20/2023]
Abstract
OBJECTIVES To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma. MATERIALS AND METHODS In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted. RESULTS CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models. CONCLUSION APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma. CLINICAL RELEVANCE STATEMENT Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies. KEY POINTS • This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma.
Collapse
Affiliation(s)
- Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tongling Jiang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Min Guo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinyuan Weng
- Department of Medical Imaging Product, Neusoft, Group Ltd, Shenyang, 110179, China
| | - Cong Xie
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junjie Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dan Cheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Department of Neuropathology, Beijing Neurosurgical Institute, Beijing, 10070, China
| | - Jiang Du
- Department of Neuropathology, Beijing Neurosurgical Institute, Beijing, 10070, China
| | | | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
4
|
Bobholz SA, Lowman AK, Connelly JM, Duenweg SR, Winiarz A, Nath B, Kyereme F, Brehler M, Bukowy J, Coss D, Lupo JM, Phillips JJ, Ellingson BM, Krucoff MO, Mueller WM, Banerjee A, LaViolette PS. Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement. Neurosurgery 2024:00006123-990000000-01091. [PMID: 38501824 DOI: 10.1227/neu.0000000000002898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/09/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures. METHODS A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability. RESULTS Tumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027). CONCLUSION This study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.
Collapse
Affiliation(s)
- Samuel A Bobholz
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Allison K Lowman
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Jennifer M Connelly
- Department of Neurology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Savannah R Duenweg
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Michael Brehler
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee , Wisconsin , USA
| | - Dylan Coss
- Department of Pathology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco , California , USA
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco and Berkeley , California , USA
| | - Joanna J Phillips
- Department of Neurological Surgery, University of California, San Francisco , California , USA
- Department of Pathology, University of California, San Francisco , California , USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles , California , USA
| | - Max O Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Wade M Mueller
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Anjishnu Banerjee
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| |
Collapse
|
5
|
Heo HY, Singh M, Yedavalli V, Jiang S, Zhou J. CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies. Magn Reson Med 2024; 91:1002-1015. [PMID: 38009996 PMCID: PMC10842109 DOI: 10.1002/mrm.29937] [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: 06/13/2023] [Revised: 10/18/2023] [Accepted: 11/04/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. METHODS A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. RESULTS The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. CONCLUSION The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.
Collapse
Affiliation(s)
- Hye-Young Heo
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Munendra Singh
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Shanshan Jiang
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jinyuan Zhou
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| |
Collapse
|
6
|
Xu Y, Wan Q, Ren X, Jiang Y, Wang F, Yao J, Wu P, Shen A, Wang P. Amide proton transfer-weighted MRI for renal tumors: Comparison with diffusion-weighted imaging. Magn Reson Imaging 2024; 106:104-109. [PMID: 38135260 DOI: 10.1016/j.mri.2023.12.002] [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: 08/24/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023]
Abstract
OBJECTIVE To investigate the potential of amide proton transfer-weighted (APTw) MRI in identifying benign and malignant renal tumors and to evaluate whether APTw MRI can add diagnostic value to diffusion-weighted imaging (DWI). MATERIALS AND METHODS Participants with renal tumor underwent preoperative multiparametric MRI, including APTw MRI and DWI. The APTw and apparent diffusion coefficient (ADC) of malignant tumors and benign tumors were calculated independently by two radiologists and compared. The value of the mean APTw and the mean ADC for differentiating malignant and benign tumors was evaluated by receiver operating characteristic analysis. RESULTS In total, 65 participants (mean age, 59 years ±14; 41 men) were evaluated: 54 with malignant and 11 with benign renal tumors. Malignant renal tumors showed higher mean APTw values [2.03% (1.63) vs 1.00% (1.60); P < 0.01] and lower mean ADC values (1.22 × 10-3 mm2/s ± 0.37 vs 1.51 × 10-3 mm2/s ± 0.37; P < 0.05) than benign renal tumors. The area under the receiver operating characteristic curve (AUC) of APTw, ADC and the combination of them for the identification of benign and malignant renal tumors was 0.78(95% CI: 0.66, 0.87; P < 0.001),0.70(95% CI: 0.54, 0.86; P < 0.05) and 0.79 (95% CI: 0.67, 0.88; P < 0.001). The optimal cutoff value for mean APTw was 2.14% (sensitivity, 74%; specificity, 73%). There was no difference between these three parameters for differentiating malignant from benign renal tumors (P > 0.05). CONCLUSION The APTw MRI has the potential use as an imaging biomarker for renal malignant and benign tumors.
Collapse
Affiliation(s)
- Yun Xu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Qingxuan Wan
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Xihui Ren
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Yutao Jiang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Fang Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Jing Yao
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Peng Wu
- Philips Healthcare, Shanghai 200072, China
| | - Aijun Shen
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China.
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China.
| |
Collapse
|
7
|
Cheng D, Zhuo Z, Zhang P, Qu L, Duan Y, Xu X, Xie C, Liu X, Haller S, Barkhof F, Zhang L, Liu Y. Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration. Acta Radiol 2023; 64:2922-2930. [PMID: 37722801 DOI: 10.1177/02841851231197503] [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] [Indexed: 09/20/2023]
Abstract
BACKGROUND Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs.
Collapse
Affiliation(s)
- Dan Cheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liying Qu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaolu Xu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Cong Xie
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Beijing Neurosurgical Institute, Beijing, China
| | - Sven Haller
- Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
8
|
Singh M, Jiang S, Li Y, van Zijl P, Zhou J, Heo HY. Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging. Magn Reson Med 2023; 90:1518-1536. [PMID: 37317675 PMCID: PMC10524222 DOI: 10.1002/mrm.29748] [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: 09/12/2022] [Revised: 04/17/2023] [Accepted: 05/18/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. METHODS The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross-linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization-transfer ratio asymmetry effect was evaluated in MTC-MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test-retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep-learning framework. RESULTS Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC-MRF dictionary or a training data set reduced the computation time by 181-fold, without compromising MRF profile accuracy. The recurrent neural network-based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC-MRF framework for tissue-parameter quantification, the test-retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. CONCLUSION Bloch simulator-driven, deep-learning MTC-MRF can provide robust and repeatable multiple-tissue parameter quantification in a clinically feasible scan time on a 3T scanner.
Collapse
Affiliation(s)
- Munendra Singh
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuguo Li
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Peter van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| |
Collapse
|
9
|
Huang Q, Wu J, Le N, Shen Y, Guo P, Schreck KC, Kamson D, Blair L, Heo HY, Li X, Li W, Sair HL, Blakeley JO, Laterra J, Holdhoff M, Grossman SA, Mukherjee D, Bettegowda C, van Zijl P, Zhou J, Jiang S. CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas. Magn Reson Imaging 2023; 102:222-228. [PMID: 37321378 PMCID: PMC10528251 DOI: 10.1016/j.mri.2023.06.003] [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: 03/02/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023]
Abstract
New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.
Collapse
Affiliation(s)
- Qianqi Huang
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jingpu Wu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nhat Le
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yiqing Shen
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Pengfei Guo
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Karisa C Schreck
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David Kamson
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Lindsay Blair
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Hye-Young Heo
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Wenbo Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Haris L Sair
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jaishri O Blakeley
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - John Laterra
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Matthias Holdhoff
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Stuart A Grossman
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Debraj Mukherjee
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Chetan Bettegowda
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Peter van Zijl
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jinyuan Zhou
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Shanshan Jiang
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
10
|
Martucci M, Russo R, Giordano C, Schiarelli C, D’Apolito G, Tuzza L, Lisi F, Ferrara G, Schimperna F, Vassalli S, Calandrelli R, Gaudino S. Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay. Cancers (Basel) 2023; 15:3790. [PMID: 37568606 PMCID: PMC10417432 DOI: 10.3390/cancers15153790] [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: 06/15/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment's lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.
Collapse
Affiliation(s)
- Matia Martucci
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Rosellina Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Carolina Giordano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Chiara Schiarelli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Gabriella D’Apolito
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Laura Tuzza
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Francesca Lisi
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Giuseppe Ferrara
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Francesco Schimperna
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Stefania Vassalli
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Rosalinda Calandrelli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Simona Gaudino
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| |
Collapse
|
11
|
Shi T, Zhu J, Zhang X, Mao X. The Role of Hypoxia and Cancer Stem Cells in Development of Glioblastoma. Cancers (Basel) 2023; 15:cancers15092613. [PMID: 37174078 PMCID: PMC10177528 DOI: 10.3390/cancers15092613] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/22/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023] Open
Abstract
Glioblastoma multiform (GBM) is recognized as the most malignant brain tumor with a high level of hypoxia, containing a small population of glioblastoma stem like cells (GSCs). These GSCs have the capacity of self-renewal, proliferation, invasion and recapitulating the parent tumor, and are major causes of radio-and chemoresistance of GBM. Upregulated expression of hypoxia inducible factors (HIFs) in hypoxia fundamentally contributes to maintenance and progression of GSCs. Therefore, we thoroughly reviewed the currently acknowledged roles of hypoxia-associated GSCs in development of GBM. In detail, we recapitulated general features of GBM, especially GSC-related features, and delineated essential responses resulted from interactions between GSC and hypoxia, including hypoxia-induced signatures, genes and pathways, and hypoxia-regulated metabolic alterations. Five hypothesized GSC niches are discussed and integrated into one comprehensive concept: hypoxic peri-arteriolar niche of GSCs. Autophagy, another protective mechanism against chemotherapy, is also closely related to hypoxia and is a potential therapeutic target for GBM. In addition, potential causes of therapeutic resistance (chemo-, radio-, surgical-, immuno-), and chemotherapeutic agents which can improve the therapeutic effects of chemo-, radio-, or immunotherapy are introduced and discussed. At last, as a potential approach to reverse the hypoxic microenvironment in GBM, hyperbaric oxygen therapy (HBOT) might be an adjuvant therapy to chemo-and radiotherapy after surgery. In conclusion, we focus on demonstrating the important role of hypoxia on development of GBM, especially by affecting the function of GSCs. Important advantages have been made to understand the complicated responses induced by hypoxia in GBM. Further exploration of targeting hypoxia and GSCs can help to develop novel therapeutic strategies to improve the survival of GBM patients.
Collapse
Affiliation(s)
- Tingyu Shi
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
- Tangdu Hospital, Fourth Military Medical University, Xi'an 710024, China
| | - Jun Zhu
- State Key Laboratory of Cancer Biology, Institute of Digestive Diseases, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Xiang Zhang
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Xinggang Mao
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| |
Collapse
|
12
|
Qi D, Li J, Quarles CC, Fonkem E, Wu E. Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023; 146:1281-1298. [PMID: 36445396 PMCID: PMC10319779 DOI: 10.1093/brain/awac450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.
Collapse
Affiliation(s)
- Dan Qi
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ekokobe Fonkem
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
| | - Erxi Wu
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX 77843, USA
- Department of Oncology and LIVESTRONG Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| |
Collapse
|
13
|
3D Amide Proton Transfer-Weighted Imaging for Grading Glioma and Correlating IDH Mutation Status: Added Value to 3D Pseudocontinuous Arterial Spin Labelling Perfusion. Mol Imaging Biol 2023; 25:343-352. [PMID: 35962302 DOI: 10.1007/s11307-022-01762-w] [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: 03/03/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE The goal of this study was to evaluate the diagnostic performance of 3D amide proton transfer-weighted (3D-APTW) imaging and 3D pseudocontinuous arterial spin labelling (3D-pCASL) alone and in combination in grading gliomas (low-grade glioma (LGG) vs. high-grade glioma (HGG)) and correlating isocitrate dehydrogenase (IDH) mutation status. PROCEDURES Preoperatively, 81 patients with pathologically confirmed gliomas underwent 3.0-T magnetic resonance imaging (MRI) examinations. The APTW, relative APTW (rAPTW), cerebral blood flow (CBF), and relative CBF (rCBF) values were calculated to evaluate the solid components of the tumours. The MRI parameters were compared in the classification of gliomas by independent- and paired-samples t tests. A receiver operating characteristic (ROC) curve was constructed, and the area under the ROC curve (AUC) was calculated to assess the diagnostic performance of each parameter and the combination of the rAPTW and rCBF values. RESULTS Patients with HGG showed significantly higher APTW, rAPTW, CBF, and rCBF values than those with LGG (all p < 0.001). In the ROC curve analysis, the AUC of rAPTW was the highest at 0.90. By adding the rAPTW signal to the rCBF values, the diagnostic ability of the combined parameters improved from 0.90 to 0.96. The rAPTW value yielded the highest AUC (0.92) in correlating the IDH mutation status, and the diagnostic ability improved to 0.96 by adding it to the rCBF value. CONCLUSION 3D-APTW imaging combined with 3D-pCASL imaging may be used to aid assessment of grading glioma and IDH mutation status.
Collapse
|
14
|
Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer. Bioengineering (Basel) 2023; 10:bioengineering10030331. [PMID: 36978722 PMCID: PMC10045132 DOI: 10.3390/bioengineering10030331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Background: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Materials and Methods: Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student’s t-test or Mann–Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. Results: Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10−3 mm2/s vs. 1.193 ± 0.337 ×10−3 mm2/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. Conclusion: APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.
Collapse
|
15
|
Bai JW, Qiu SQ, Zhang GJ. Molecular and functional imaging in cancer-targeted therapy: current applications and future directions. Signal Transduct Target Ther 2023; 8:89. [PMID: 36849435 PMCID: PMC9971190 DOI: 10.1038/s41392-023-01366-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/19/2023] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.
Collapse
Affiliation(s)
- Jing-Wen Bai
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Medical Oncology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
| | - Si-Qi Qiu
- Diagnosis and Treatment Center of Breast Diseases, Clinical Research Center, Shantou Central Hospital, 515041, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Shantou University Medical College, 515041, Shantou, China
| | - Guo-Jun Zhang
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
| |
Collapse
|
16
|
Henssen D, Meijer F, Verburg FA, Smits M. Challenges and opportunities for advanced neuroimaging of glioblastoma. Br J Radiol 2023; 96:20211232. [PMID: 36062962 PMCID: PMC10997013 DOI: 10.1259/bjr.20211232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 08/10/2022] [Accepted: 08/25/2022] [Indexed: 11/05/2022] Open
Abstract
Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.
Collapse
Affiliation(s)
- Dylan Henssen
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederick Meijer
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederik A. Verburg
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Marion Smits
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| |
Collapse
|
17
|
Guo H, Liu J, Hu J, Zhang H, Zhao W, Gao M, Zhang Y, Yang G, Cui Y. Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models. J Magn Reson Imaging 2022; 56:1834-1844. [PMID: 35488516 PMCID: PMC9790544 DOI: 10.1002/jmri.28211] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The focus of neuro-oncology research has changed from histopathologic grading to molecular characteristics, and medical imaging routinely follows this change. PURPOSE To compare the diagnostic performance of amide proton transfer (APT) and four diffusion models in gliomas grading and isocitrate dehydrogenase (IDH) genotype. STUDY TYPE Prospective. POPULATION A total of 62 participants (37 males, 25 females; mean age, 52 ± 13 years) whose IDH genotypes were mutant in 6 of 14 grade II gliomas, 8 of 20 of grade III gliomas, and 4 of 28 grade IV gliomas. FIELD STRENGTH/SEQUENCE APT imaging using sampling perfection with application optimized contrasts by using different flip angle evolutions (SPACE) and DWI with q-space Cartesian grid sampling were acquired at 3 T. ASSESSMENT The ability of diffusion kurtosis imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging (NODDI), mean apparent propagator (MAP), and APT imaging for glioma grade and IDH status were assessed, with histopathological grade and genetic testing used as a reference standard. Regions of interest (ROIs) were drawn by two neuroradiologists after consensus. STATISTICAL TESTS T-test and Mann-Whitney U test; one-way analysis of variance (ANOVA); receiver operating curve (ROC) and area under the curve (AUC); DeLong test. P value < 0.05 was considered statistically significant. RESULTS Compared with IDH-mutant gliomas, IDH-wildtype gliomas showed a significantly higher mean, 5th-percentile (APT5 ), and 95th-percentile from APTw, the 95th-percentile value of axial, mean, and radial diffusivity from DKI, and 95th-percentile value of isotropic volume fraction from NODDI, and no significantly different parameters from DTI and MAP (P = 0.075-0.998). The combined APT model showed a significantly wider area under the curve (AUC 0.870) for IDH status, when compared with DKI and NODDI. APT5 was significantly different between two of the three groups (glioma II vs. glioma III vs. glioma IV: 1.35 ± 0.75 vs. 2.09 ± 0.93 vs. 2.71 ± 0.81). DATA CONCLUSION APT has higher diagnostic accuracy than DTI, DKI, MAP, and NODDI in glioma IDH genotype. APT5 can effectively identify both tumor grading and IDH genotyping, making it a promising biomarker for glioma classification. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Hu Guo
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - Jun Liu
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China,Department of Radiology Quality Control CenterHunan ProvinceChangsha410011China
| | - JunJiao Hu
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - HuiTing Zhang
- MR Scientific Marketing, Siemens Healthineers Ltd.Wuhan430071China
| | - Wei Zhao
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - Min Gao
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - Yi Zhang
- Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouZhejiangChina
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic ResonanceSchool of Physics and Electronic, East China Normal UniversityShanghaiChina
| | - Yan Cui
- Department of NeurosurgeryThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Rd, ChangshaHunan Province410011P.R. China
| |
Collapse
|
18
|
Nardini M, Capotosti A, Mazzoni LN, Cusumano D, Boldrini L, Chiloiro G, Romano A, Valentini V, Indovina L, Placidi L. Tuning the optimal diffusion-weighted MRI parameters on a 0.35-T MR-Linac for clinical implementation: A phantom study. Front Oncol 2022; 12:867792. [PMID: 36523999 PMCID: PMC9745186 DOI: 10.3389/fonc.2022.867792] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 11/07/2022] [Indexed: 12/06/2023] Open
Abstract
PURPOSE This study aims to assess the quality of a new diffusion-weighted imaging (DWI) sequence implemented on an MR-Linac MRIdian system, evaluating and optimizing the acquisition parameters to explore the possibility of clinically implementing a DWI acquisition protocol in a 0.35-T MR-Linac. MATERIALS AND METHODS All the performed analyses have been carried out on two types of phantoms: a homogeneous 24-cm diameter polymethylmethacrylate (PMMA) sphere (SP) and a homemade phantom (HMP) constating in a PMMA cylinder filled with distilled water with empty sockets into which five cylindrical vials filled with five different concentrations of methylcellulose water solutions have been inserted. SP was used to evaluate the dependence of diffusion gradient inhomogeneity artifacts on gantry position. Four diffusion sequences with b-values of 500 s/mm2 and 3 averages have been acquired: three with diffusion gradients in the three main directions (phase direction, read direction, slice direction) and one with the diffusion gradients switched off. The dependence of diffusion image uniformity and SNR on the number of averages in the MR sequences was also investigated to determine the optimal number of averages. Finally, the ADC values of HMP have been computed and then compared between images acquired in the scanners at 0.35 and 1.5 T. RESULTS In order to acquire high-quality artifact-free DWI images, the "slice" gradient direction has been identified to be the optimal one and 0° to be the best gradient angle. Both the SNR ratio and the uniformity increase with the number of averages. A threshold value of 80 for SNR and 85% for uniformity was adopted to choose the best number of averages. By making a compromise between time and quality and limiting the number of b-values, it is possible to reduce the acquisition time to 78 s. The Passing-Bablok test showed that the two methods, with 0.35 and 1.5 T scanners, led to similar results. CONCLUSION The quality of the DWI has been accurately evaluated in relation to different sequence parameters, and optimal parameters have been identified to select a clinical protocol for the acquisition of ADC maps sustainable in the workflow of a hybrid radiotherapy system with a 0.35-T MRI scanner.
Collapse
Affiliation(s)
- Matteo Nardini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Amedeo Capotosti
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Nicola Mazzoni
- Azienda Unità Sanitaria Locale (AUSL) Toscana Centro, Medical Physics Unit, Prato-Pistoia, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- Mater Olbia Hospital, UOS Fisica Medica, Olbia, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| |
Collapse
|
19
|
Kurokawa R, Baba A, Kurokawa M, Capizzano A, Ota Y, Kim J, Srinivasan A, Moritani T. Perfusion and diffusion-weighted imaging parameters: Comparison between pre- and postbiopsy MRI for high-grade glioma. Medicine (Baltimore) 2022; 101:e30183. [PMID: 36107564 PMCID: PMC9439799 DOI: 10.1097/md.0000000000030183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We aimed to evaluate the differences in dynamic susceptibility contrast (DSC)- magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) parameters between the pre- and postbiopsy MRI obtained before treatment in patients with diffuse midline glioma, H3K27-altered. The data of 25 patients with pathologically proven diffuse midline glioma, H3K27-altered, were extracted from our hospital's database between January 2017 and August 2021. Twenty (median age, 13 years; range, 3-52 years; 12 women) and 8 (13.5 years; 5-68 years; 1 woman) patients underwent preoperative DSC-MRI and DWI before and after biopsy, respectively. The normalized corrected relative cerebral blood volume (ncrCBV), normalized relative cerebral blood flow (nrCBF), and normalized maximum, mean, and minimum apparent diffusion coefficient (ADC) were calculated using the volumes-of-interest of the tumor and normal-appearing reference region. The macroscopic postbiopsy changes (i.e., biopsy tract, tissue defect, and hemorrhage) were meticulously excluded from the postbiopsy measurements. The DSC-MRI and DWI parameters of the pre- and postbiopsy groups were compared using the Mann-Whitney U test. The ncrCBV was significantly lower in the postbiopsy group than in the prebiopsy group [prebiopsy group: median 1.293 (range, 0.513 to 2.547) versus postbiopsy group: 0.877 (0.748 to 1.205), P = .016]. No significant difference was observed in the nrCBF and normalized ADC values, although the median nrCBF was lower in the postbiopsy group. The DSC-MRI parameters differed between the pre- and postbiopsy MRI obtained pretreatment, although the macroscopic postbiopsy changes were carefully excluded from the analysis. The results emphasize the potential danger of integrating and analyzing DSC-MRI parameters derived from pre- and postbiopsy MRI.
Collapse
Affiliation(s)
- Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
- *Correspondence: Ryo Kurokawa, Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E Medical Center Dr, UH B2, Ann Arbor, MI 48109 (e-mail: )
| | - Akira Baba
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Mariko Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Aristides Capizzano
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Yoshiaki Ota
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - John Kim
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Ashok Srinivasan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| |
Collapse
|
20
|
Qin J, Tang Y, Wang B. Regional 18F-fluoromisonidazole PET images generated from multiple advanced MR images using neural networks in glioblastoma. Medicine (Baltimore) 2022; 101:e29572. [PMID: 35905276 PMCID: PMC9333488 DOI: 10.1097/md.0000000000029572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Generated 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) images for glioblastoma are highly sought after because 18F-FMISO can be radioactive, and the imaging procedure is not easy. This study aimed to explore the feasibility of using advanced magnetic resonance (MR) images to generate regional 18F-FMISO PET images and its predictive value for survival. Twelve kinds of advanced MR images of 28 patients from The Cancer Imaging Archive were processed. Voxel-by-voxel correlation analysis between 18F-FMISO images and advanced MR images was performed to select the MR images for generating regional 18F-FMISO images. Neural network algorithms provided by the MATLAB toolbox were used to generate regional 18F-FMISO images. The mean square error (MSE) was used to evaluate the regression effect. The prognostic value of generated 18F-FMISO images was evaluated by the Mantel-Cox test. A total of 299 831 voxels were extracted from the segmented regions of all patients. Eleven kinds of advanced MR images were selected to generate 18F-FMISO images. The best neural network algorithm was Bayesian regularization. The MSEs of the training, validation, and testing groups were 2.92E-2, 2.9E-2, and 2.92E-2, respectively. Both the maximum Tissue/Blood ratio (P = .017) and hypoxic volume (P = .023) of the generated images were predictive factors of overall survival, but only hypoxic volume (P = .029) was a predictive factor of progression-free survival. Multiple advanced MR images are feasible to generate qualified regional 18F-FMISO PET images using neural networks. The generated images also have predictive value in the prognostic evaluation of glioblastoma.
Collapse
Affiliation(s)
- Jianhua Qin
- School of Medicine, Qingdao University, Qingdao, P. R. China
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | - Yu Tang
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | - Bao Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, P. R. China
- *Correspondence: Bao Wang, Department of Radiology, Qilu Hospital of Shandong University, Jinan, P. R. China, 250012 (e-mail: )
| |
Collapse
|
21
|
Zhang HW, Liu XL, Zhang HB, Li YQ, Wang YL, Feng YN, Deng K, Lei Y, Huang B, Lin F. Differentiation of Meningiomas and Gliomas by Amide Proton Transfer Imaging: A Preliminary Study of Brain Tumour Infiltration. Front Oncol 2022; 12:886968. [PMID: 35646626 PMCID: PMC9132094 DOI: 10.3389/fonc.2022.886968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/07/2022] [Indexed: 11/22/2022] Open
Abstract
Background Gliomas are more malignant and invasive than meningiomas. Objective To distinguish meningiomas from low-grade/high-grade gliomas (LGGs/HGGs) using amide proton transfer imaging (APT) combined with conventional magnetic resonance imaging (MRI) and to explore the application of APT in evaluating brain tumour invasiveness. Materials and Methods The imaging data of 50 brain tumors confirmed by pathology in patients who underwent APT scanning in our centre were retrospectively analysed. Of these tumors, 25 were meningiomas, 10 were LGGs, and 15 were HGGs. The extent of the tumour-induced range was measured on APT images, T2-weighted imaging (T2WI), and MRI enhancement; additionally, and the degree of enhancement was graded. Ratios (RAPT/T2 and RAPT/E) were obtained by dividing the range of changes observed by APT by the range of changes observed via T2WI and MR enhancement, respectively, and APTmean values were measured. The Mann–Whitney U test was used to compare the above measured values with the pathological results obtained for gliomas and meningiomas, the Kruskal-Wallis test was used to compare LGGs, HGGs and meningiomas, and Dunn’s test was used for pairwise comparisons. In addition, receiver operating characteristic (ROC) curves were drawn. Results The Mann–Whitney U test showed that APTmean (p=0.005), RAPT/T2 (p<0.001), and RAPT/E (p<0.001) values were statistically significant in the identification of meningioma and glioma. The Kruskal-Wallis test showed that the parameters APTmean, RAPT/T2, RAPT/E and the degree of enhancement are statistically significant. Dunn’s test revealed that RAPT/T2 (p=0.004) and RAPT/E (p=0.008) could be used for the identification of LGGs and meningiomas. APTmean (p<0.001), RAPT/T2 (p<0.001), and RAPT/E (p<0.001) could be used for the identification of HGGs and meningiomas. APTmean (p<0.001) was statistically significant in the comparison of LGGs and HGGs. ROC curves showed that RAPT/T2 (area under the curve (AUC)=0.947) and RAPT/E (AUC=0.919) could be used to distinguish gliomas from meningiomas. Conclusion APT can be used for the differential diagnosis of meningioma and glioma, but APTmean values can only be used for the differential diagnosis of HGGs and meningiomas or HGGs and LGGs. Gliomas exhibit more obvious changes than meningiomas in APT images of brain tissue; this outcome may be caused by brain infiltration.
Collapse
Affiliation(s)
- Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xiao-Lei Liu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Hong-Bo Zhang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Ying-Qi Li
- Department of Radiology, Songgang People's Hospital, Shenzhen, China
| | - Yu-Li Wang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yu-Ning Feng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Kan Deng
- Research Department, Philips Healthcare, Guangzhou, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| |
Collapse
|
22
|
Zhang N, Zhang H, Gao B, Miao Y, Liu A, Song Q, Lin L, Wang J. 3D Amide Proton Transfer Weighted Brain Tumor Imaging With Compressed SENSE: Effects of Different Acceleration Factors. Front Neurosci 2022; 16:876587. [PMID: 35692419 PMCID: PMC9178274 DOI: 10.3389/fnins.2022.876587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/13/2022] [Indexed: 12/05/2022] Open
Abstract
Objectives The aim of the current study was to evaluate the performance of compressed SENSE (CS) for 3D amide proton transfer weighted (APTw) brain tumor imaging with different acceleration factors (AFs), and the results were compared with those of conventional SENSE. Methods Approximately 51 patients with brain tumor (22 males, 49.95 ± 10.52 years) with meningiomas (n = 16), metastases (n = 12), or gliomas (n = 23) were enrolled. All the patients received 3D APTw imaging scans on a 3.0 T scanner with acceleration by CS (AFs: CS2, CS3, CS4, and CS5) and SENSE (AF: S1.6). Two readers independently and subjectively evaluated the APTw images relative to image quality and measured confidence concerning image blur, distortion, motion, and ghosting artifacts, lesion recognition, and contour delineation with a 5-point Likert scale. Mean amide proton transfer (APT) values of brain tumors (APTtumor), the contralateral normal-appearing white matter (APTCNAWM), and the peritumoral edema area (if present, APTedema) and the tumor volume (VAPT) were measured for objective evaluation and determination of the optimal AF. The Ki67 labeling index was also measured by using standard immunohistochemical staining procedures in samples from patients with gliomas, and the correlation between tumor APT values and the Ki67 index was analyzed. Results The image quality of AF = CS5 was significantly lower than that of other groups. VAPT showed significant differences among the six sequences in meningiomas (p = 0.048) and gliomas (p = 0.023). The pairwise comparison showed that the VAPT values of meningiomas measured from images by CS5 were significantly lower, and gliomas were significantly larger than those by SENSE1.6 and other CS accelerations, (p < 0.05). APTtumor (p = 0.191) showed no significant difference among the three types of tumors. The APTtumor values of gliomas measured by APTw images with the SENSE factor of 1.6 and the CS factor of 2, 3, and 4 (except for CS5) were all positively correlated with Ki67. Conclusion Compressed SENSE could be successfully extended to accelerated 3D APTw imaging of brain tumors without compromising image quality using the AF of 4.
Collapse
Affiliation(s)
- Nan Zhang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
- Department of Radiology, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Haonan Zhang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bingbing Gao
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yanwei Miao
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Qingwei Song,
| | - Liangjie Lin
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Jiazheng Wang
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| |
Collapse
|
23
|
Zhou J, Zaiss M, Knutsson L, Sun PZ, Ahn SS, Aime S, Bachert P, Blakeley JO, Cai K, Chappell MA, Chen M, Gochberg DF, Goerke S, Heo HY, Jiang S, Jin T, Kim SG, Laterra J, Paech D, Pagel MD, Park JE, Reddy R, Sakata A, Sartoretti-Schefer S, Sherry AD, Smith SA, Stanisz GJ, Sundgren PC, Togao O, Vandsburger M, Wen Z, Wu Y, Zhang Y, Zhu W, Zu Z, van Zijl PCM. Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors. Magn Reson Med 2022; 88:546-574. [PMID: 35452155 PMCID: PMC9321891 DOI: 10.1002/mrm.29241] [Citation(s) in RCA: 73] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/26/2022] [Accepted: 03/02/2022] [Indexed: 12/16/2022]
Abstract
Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.
Collapse
Affiliation(s)
- Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Linda Knutsson
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Medical Radiation Physics, Lund University, Lund, Sweden.,F.M. Kirby Research Center for Functional Brain Imaging, Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | - Phillip Zhe Sun
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Peter Bachert
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Jaishri O Blakeley
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Michael A Chappell
- Mental Health and Clinical Neurosciences and Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Daniel F Gochberg
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Physics, Vanderbilt University, Nashville, Tennessee, USA
| | - Steffen Goerke
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tao Jin
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - John Laterra
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany.,Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Mark D Pagel
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Ravinder Reddy
- Center for Advance Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - A Dean Sherry
- Advanced Imaging Research Center and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson, Texas, USA
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Greg J Stanisz
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Pia C Sundgren
- Department of Diagnostic Radiology/Clinical Sciences Lund, Lund University, Lund, Sweden.,Lund University Bioimaging Center, Lund University, Lund, Sweden.,Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| |
Collapse
|
24
|
Evaluation of Temozolomide Treatment for Glioblastoma Using Amide Proton Transfer Imaging and Diffusion MRI. Cancers (Basel) 2022; 14:cancers14081907. [PMID: 35454814 PMCID: PMC9031574 DOI: 10.3390/cancers14081907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/06/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Glioblastoma (GBM), the most frequent and malignant histological type of glioma, is associated with a very high mortality rate. MRI is a useful method for the evaluation of tumor growth. However, there are few studies that have quantitatively evaluated the changes in disease state after TMZ treatment against GBM, and it is not fully understood how the effects of treatment are reflected in the quantitative values measured on MRI. We used the C6 glioma rat model to evaluate the tumor changes due to chemotherapy at different doses of TMZ in terms of quantitative values measured by neurite orientation dispersion and density imaging (NODDI) and amide proton transfer (APT) imaging using 7T-MRI. These methods can evaluate the microstructural changes caused by TMZ-induced tumor growth inhibition. Abstract This study aimed to evaluate tumor changes due to chemotherapy with temozolomide (TMZ) in terms of quantitative values measured by APT imaging and NODDI. We performed TMZ treatment (administered orally by gavage to the TMZ-40 mg and TMZ-60 mg groups) on 7-week-old male Wistar rats with rat glioma C6 implanted in the right brain. T2WI, APT imaging, diffusion tensor imaging (DTI), and NODDI were performed on days 7 and 14 after implantation using 7T-MRI, and the calculated quantitative values were statistically compared. Then, HE staining was performed on brain tissue at day 7 and day 14 for each group to compare the results with the MR images. TMZ treatment inhibited tumor growth and necrotic area formation. The necrotic areas observed upon hematoxylin and eosin (HE) staining were consistent with the MTR low-signal areas observed upon APT imaging. The intracellular volume fraction (ICVF) map of the NODDI could best show the microstructure of the tumor, and its value could significantly highlight the difference in treatment effects at different TMZ doses. APT imaging and NODDI can be used to detect the microstructural changes caused by TMZ-induced tumor growth inhibition. The ICVF may be useful as a parameter for determining the effect of TMZ.
Collapse
|
25
|
Moon HH, Park JE, Kim YH, Kim JH, Kim HS. Contrast enhancing pattern on pre-treatment MRI predicts response to anti-angiogenic treatment in recurrent glioblastoma: comparison of bevacizumab and temozolomide treatment. J Neurooncol 2022; 157:405-415. [PMID: 35275335 DOI: 10.1007/s11060-022-03980-2] [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: 01/06/2022] [Accepted: 03/04/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To evaluate the value of the contrast enhancing pattern on pre-treatment MRI for predicting the response to anti-angiogenic treatment in patients with IDH-wild type recurrent glioblastoma. METHODS This retrospective study enrolled 65 patients with IDH wild-type recurrent glioblastoma who received standard therapy and then received either bevacizumab (46 patients) or temozolomide (19 patients) as a secondary treatment. The contrast enhancing pattern on pre-treatment MRI was visually analyzed and dichotomized into contrast enhancing lesion (CEL) dominant and non-enhancing lesion (NEL) dominant types. Quantitative volumetric analysis was used to support the dichotomization. The Kaplan-Meier method and Cox proportional hazards regression analysis were used to stratify progression free survival (PFS) according to the treatment in the entire patients, CEL dominant group, and NEL dominant group. RESULTS In all patients, the PFS of those treated with bevacizumab was not significantly different from those treated with temozolomide (log-rank test, P = 0.96). When the contrast enhancing pattern was considered, bevacizumab was associated with longer PFS in the CEL dominant group (P = 0.031), whereas temozolomide showed longer PFS in the NEL dominant group (P = 0.022). Quantitative analysis revealed mean values for the proportion of solid-enhancing tumor of 13.7% for the CEL dominant group and 4.3% for the NEL dominant group. CONCLUSION Patients with the CEL dominant type showed a better treatment response to bevacizumab, whereas NEL dominant types showed a better response to temozolomide. The contrast enhancing pattern on pre-treatment MRI can be used to stratify patients with IDH wild-type recurrent glioblastoma according to the effect of anti-angiogenic treatment.
Collapse
Affiliation(s)
- Hye Hyeon Moon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea.
| | - Young-Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea
| |
Collapse
|
26
|
Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges. Pharmaceutics 2022; 14:pharmaceutics14020451. [PMID: 35214183 PMCID: PMC8880023 DOI: 10.3390/pharmaceutics14020451] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/10/2022] Open
Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future.
Collapse
|
27
|
Lingl JP, Wunderlich A, Goerke S, Paech D, Ladd ME, Liebig P, Pala A, Kim SY, Braun M, Schmitz BL, Beer M, Rosskopf J. The Value of APTw CEST MRI in Routine Clinical Assessment of Human Brain Tumor Patients at 3T. Diagnostics (Basel) 2022; 12:diagnostics12020490. [PMID: 35204583 PMCID: PMC8871436 DOI: 10.3390/diagnostics12020490] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 12/10/2022] Open
Abstract
Background. With fast-growing evidence in literature for clinical applications of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), this prospective study aimed at applying amide proton transfer-weighted (APTw) CEST imaging in a clinical setting to assess its diagnostic potential in differentiation of intracranial tumors at 3 tesla (T). Methods. Using the asymmetry magnetization transfer ratio (MTRasym) analysis, CEST signals were quantitatively investigated in the tumor areas and in a similar sized region of the normal-appearing white matter (NAWM) on the contralateral hemisphere of 27 patients with intracranial tumors. Area under curve (AUC) analyses were used and results were compared to perfusion-weighted imaging (PWI). Results. Using APTw CEST, contrast-enhancing tumor areas showed significantly higher APTw CEST metrics than contralateral NAWM (AUC = 0.82; p < 0.01). In subgroup analyses of each tumor entity vs. NAWM, statistically significant effects were yielded for glioblastomas (AUC = 0.96; p < 0.01) and for meningiomas (AUC = 1.0; p < 0.01) but not for lymphomas as well as metastases (p > 0.05). PWI showed results comparable to APTw CEST in glioblastoma (p < 0.01). Conclusions. This prospective study confirmed the high diagnostic potential of APTw CEST imaging in a routine clinical setting to differentiate brain tumors.
Collapse
Affiliation(s)
- Julia P. Lingl
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Arthur Wunderlich
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Steffen Goerke
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (S.G.); (M.E.L.)
| | - Daniel Paech
- German Cancer Research Center (DKFZ), Division of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
- Department of Neuroradiology, Venusberg-Campus 1, Bonn University, 53127 Bonn, Germany
| | - Mark E. Ladd
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (S.G.); (M.E.L.)
- Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany
| | - Patrick Liebig
- Siemens Healthcare GmbH, Henkestraße 127, 91052 Erlangen, Germany;
| | - Andrej Pala
- Department of Neurosurgery, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany;
| | - Soung Yung Kim
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Michael Braun
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Bernd L. Schmitz
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Meinrad Beer
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Johannes Rosskopf
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
- Correspondence:
| |
Collapse
|
28
|
Kurokawa R, Baba A, Kurokawa M, Capizzano A, Hassan O, Johnson T, Ota Y, Kim J, Hagiwara A, Moritani T, Srinivasan A. Pretreatment ADC Histogram Analysis as a Prognostic Imaging Biomarker for Patients with Recurrent Glioblastoma Treated with Bevacizumab: A Systematic Review and Meta-analysis. AJNR Am J Neuroradiol 2022; 43:202-206. [PMID: 35058300 PMCID: PMC8985678 DOI: 10.3174/ajnr.a7406] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/15/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND The mean ADC value of the lower Gaussian curve (ADCL) derived from the bi-Gaussian curve-fitting histogram analysis has been reported as a predictive/prognostic imaging biomarker in patients with recurrent glioblastoma treated with bevacizumab; however, its systematic summary has been lacking. PURPOSE We applied a systematic review and meta-analysis to investigate the predictive/prognostic performance of ADCL in patients with recurrent glioblastoma treated with bevacizumab. DATA SOURCES We performed a literature search using PubMed, Scopus, and EMBASE. STUDY SELECTION A total of 1344 abstracts were screened, of which 83 articles were considered potentially relevant. Data were finally extracted from 6 studies including 578 patients. DATA ANALYSIS Forest plots were generated to illustrate the hazard ratios of overall survival and progression-free survival. The heterogeneity across the studies was assessed using the Cochrane Q test and I2 values. DATA SYNTHESIS The pooled hazard ratios for overall survival and progression-free survival in patients with an ADCL lower than the cutoff values were 1.89 (95% CI, 1.53-2.31) and 1.98 (95% CI, 1.54-2.55) with low heterogeneity among the studies. Subgroup analysis of the bevacizumab-free cohort showed a pooled hazard ratio for overall survival of 1.20 (95% CI, 1.08-1.34) with low heterogeneity. LIMITATIONS The conclusions are limited by the difference in the definition of recurrence among the included studies. CONCLUSIONS This systematic review with meta-analysis supports the prognostic value of ADCL in patients with recurrent glioblastoma treated with bevacizumab, with a low ADCL demonstrating decreased overall survival and progression-free survival. On the other hand, the predictive role of ADCL for bevacizumab treatment was not confirmed.
Collapse
Affiliation(s)
- R. Kurokawa
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Baba
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - M. Kurokawa
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Capizzano
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - O. Hassan
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - T. Johnson
- Department of Biostatistics (T.J.), University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Y. Ota
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - J. Kim
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Hagiwara
- Department of Radiology (A.H.), Juntendo University School of Medicine, Tokyo, Japan
| | - T. Moritani
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Srinivasan
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| |
Collapse
|
29
|
Guo P, Unberath M, Heo HY, Eberhart CG, Lim M, Blakeley JO, Jiang S. Learning-based analysis of amide proton transfer-weighted MRI to identify true progression in glioma patients. NEUROIMAGE: CLINICAL 2022; 35:103121. [PMID: 35905666 PMCID: PMC9421489 DOI: 10.1016/j.nicl.2022.103121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Pengfei Guo
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Hye-Young Heo
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Michael Lim
- Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA
| | | | - Shanshan Jiang
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
30
|
Guo Z, Meng Z, Mu R, Qin X, Zhuang Z, Zheng W, Liu F, Zhu X. Amide Proton Transfer MRI Could Be Used to Evaluate the Pathophysiological Status of White Matter Hyperintensities. J Magn Reson Imaging 2021; 56:301-309. [PMID: 34854519 DOI: 10.1002/jmri.28013] [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: 10/12/2021] [Revised: 11/21/2021] [Accepted: 11/24/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The pathophysiology of white matter hyperintensities (WMH) remains unclear, investigations of amide proton transfer (APT) signals in WMH disease may provide relevant pathophysiological information. PURPOSE To evaluate the APT signals differences and heterogeneity of WMH and adjacent normal-appearing white matter (NAWM) at different Fazekas grades and different locations. STUDY TYPE Prospective. POPULATION In all, 180 WMH patients (age, 40-76; male/female, 77/103) and 59 healthy controls (age, 42-70; male/female, 23/36). FIELD STRENGTH/SEQUENCE A 3 T; 3D fluid-attenuated inversion recovery (FLAIR), 3D APT-weighted (APTw). ASSESSMENT The mean APTw values (APTwmean ) and the APTw signals heterogeneity (APTwmax-min ) among different grades WMH and NAWM and the APTwmean of the same grade deep WMH (DWMH) and paraventricular WMH (PWMH) were calculated and compared. Regions of interests were delineated on WMH lesions, NAWM and healthy white matter. STATISTICAL TESTS One-way analysis of variance (ANOVA); independent sample t test; Chi-square test. Significance level: P < 0.05. RESULTS APTwmean among different grade WMH (from grade 0 to 3, 0.58 ± 0.14% vs. 0.29 ± 0.23% vs. 0.37 ± 0.24% vs. 0.61 ± 0.22%, respectively) were significantly different except between grade 1 and 2 (P = 0.27) and between grade 0 and 3 (P = 0.97). The differences in APTwmean between WMH and NAWM were significant (WMH vs. NAWM from grade 1 to 3, 0.29% ± 0.23% vs. 0.55% ± 0.27%; 0.37% ± 0.24% vs. 0.59% ± 0.22%; 0.61% ± 0.22% vs. 0.42% ± 0.24%, respectively). Lower APTwmean values were found only in grade 3 NAWM than other grades NAWM and controls. The APTwmax-min values of grade 1-3 WMH (0.38% ± 0.27% vs. 0.51% ± 0.31% vs. 0.67% ± 0.34%, respectively) were significantly different. Higher APTmean values were found only in grade 2 PWMH (0.47% ± 0.22% vs. 0.32% ± 0.24%). DATA CONCLUSION Significant differences of APT signals were found in WMH of different Fazekas grades and different locations. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 3.
Collapse
Affiliation(s)
- Zixuan Guo
- Department of Medical Imaging, Guilin Medical University, Guilin, China.,Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Zhuoni Meng
- Department of Medical Imaging, Guilin Medical University, Guilin, China.,Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Ronghua Mu
- Department of Medical Imaging, Guilin Medical University, Guilin, China.,Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Xiaoyan Qin
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Zeyu Zhuang
- Department of Medical Imaging, Guilin Medical University, Guilin, China.,Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Wei Zheng
- Department of Medical Imaging, Guilin Medical University, Guilin, China.,Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Fuzhen Liu
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Xiqi Zhu
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| |
Collapse
|
31
|
Song Q, Chen P, Chen X, Sun C, Wang J, Tan B, Liu H, Cheng Y. Dynamic Change of Amide Proton Transfer Imaging in Irradiated Nasopharyngeal Carcinoma and Related Histopathological Mechanism. Mol Imaging Biol 2021; 23:846-853. [PMID: 33876335 DOI: 10.1007/s11307-021-01607-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 10/24/2022]
Abstract
OBJECTIVE To investigate the dynamic change of amide proton transfer (APT) imaging before and after irradiation in nasopharyngeal carcinoma (NPC) and the underlying histopathological mechanism. MATERIALS AND METHODS Tumor-bearing BALB/C nude mouse models were established and randomly divided into three groups: high-dose group (20 Gy/2 fractions), low-dose group (10 Gy/2 fractions), and control group (0 Gy). MRI scanning was performed before irradiation and 3rd, 6th, and 9th day post-irradiation. Scanning sequence included T1 weighted, T2 weighted, and APT. HE staining and TUNEL immunofluorescence detection were performed to detect necrosis and apoptosis. RESULTS After high-dose irradiation, the mean tumor APT values decreased significantly on the 3rd day and 6th day (from 3.83 before radiotherapy to 2.41%, P < 0.001, 3rd day; from 2.41 to 1.80%, P = 0.001, 6th day). For low-dose irradiation, the mean tumor APT values decreased slightly on the 3rd day and 6th day (from 3.52 to 3.13%, P = 0.109, 3rd day; from 3.13 to 3.05%, P = 0.64, 6th day). The mean APT values of nonirradiated tumor changed slightly. In contrast, the average volume of high-dose irradiated tumors did not decrease obviously until the 9th day post-irradiation (from 290 before radiotherapy to 208 mm3 on the 9th day). The low-dose irradiated tumors showed slow growth, and the nonirradiated tumors showed rapid growth. Subsequent HE staining and TUNEL staining showed obvious necrosis characteristics and higher proportion of positive apoptotic cell nucleus in high-dose irradiated tumors, but not nonirradiated tumors. CONCLUSION The APT signal intensity decreased after irradiation, which is earlier than the change of tumor volume. What is more, the decrease of APT signal intensity is more significant in high-dose group. Histological analysis showed obvious apoptosis and necrosis histological characteristic in irradiated tumor, which may explain the decrease of APT signal intensity. These results indicate that APT imaging has the potential to serve as a reliable biomarker for response assessment in NPC.
Collapse
Affiliation(s)
- Qingxu Song
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China
| | - Pengxiang Chen
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China
| | - Xin Chen
- Department of MR, Shandong Medical Imaging Research Institute, Jinan, Shandong, People's Republic of China
| | - Cong Sun
- Department of MR, Shandong Medical Imaging Research Institute, Jinan, Shandong, People's Republic of China
| | - Jianbo Wang
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China
| | - Bingxu Tan
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China
| | - Hong Liu
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China
| | - Yufeng Cheng
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China.
| |
Collapse
|
32
|
Zhang H, Zhou J, Peng Y. Amide Proton Transfer-Weighted MR Imaging of Pediatric Central Nervous System Diseases. Magn Reson Imaging Clin N Am 2021; 29:631-641. [PMID: 34717850 DOI: 10.1016/j.mric.2021.06.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Amide proton transfer-weighted (APTw) imaging is a molecular MR imaging technique that can detect the concentration of the amide protons in mobile cellular proteins and peptides or a pH change in vivo. Previous studies have indicated that APTw MR imaging can be used to detect malignant brain tumors, stroke, and other neurologic diseases, although the clinical application in pediatric patients remains limited. The authors briefly introduce the basic principles of APTw imaging. Then, they review early clinical applications of this approach to pediatric central nervous system diseases, including pediatric brain development, hypoxic-ischemic encephalopathy, intracranial infection, and brain tumors.
Collapse
Affiliation(s)
- Hong Zhang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, 56 Nan Li Shi Road, Xi Cheng District, Beijing, 100045, China
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 336, Baltimore, MD 21287, USA
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, 56 Nan Li Shi Road, Xi Cheng District, Beijing, 100045, China.
| |
Collapse
|
33
|
Abstract
PURPOSE OF REVIEW This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions. RECENT FINDINGS Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings. SUMMARY Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.
Collapse
Affiliation(s)
- Lucia Nichelli
- Department of Neuroradiology, Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix
- Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, boulevard de l’Hôpital, Paris
| | - Stefano Casagranda
- Department of Research & Innovation, Olea Medical, avenue des Sorbiers, La Ciotat, France
| |
Collapse
|
34
|
Platt T, Ladd ME, Paech D. 7 Tesla and Beyond: Advanced Methods and Clinical Applications in Magnetic Resonance Imaging. Invest Radiol 2021; 56:705-725. [PMID: 34510098 PMCID: PMC8505159 DOI: 10.1097/rli.0000000000000820] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/07/2021] [Accepted: 08/07/2021] [Indexed: 12/15/2022]
Abstract
ABSTRACT Ultrahigh magnetic fields offer significantly higher signal-to-noise ratio, and several magnetic resonance applications additionally benefit from a higher contrast-to-noise ratio, with static magnetic field strengths of B0 ≥ 7 T currently being referred to as ultrahigh fields (UHFs). The advantages of UHF can be used to resolve structures more precisely or to visualize physiological/pathophysiological effects that would be difficult or even impossible to detect at lower field strengths. However, with these advantages also come challenges, such as inhomogeneities applying standard radiofrequency excitation techniques, higher energy deposition in the human body, and enhanced B0 field inhomogeneities. The advantages but also the challenges of UHF as well as promising advanced methodological developments and clinical applications that particularly benefit from UHF are discussed in this review article.
Collapse
Affiliation(s)
- Tanja Platt
- From the Medical Physics in Radiology, German Cancer Research Center (DKFZ)
| | - Mark E. Ladd
- From the Medical Physics in Radiology, German Cancer Research Center (DKFZ)
- Faculty of Physics and Astronomy
- Faculty of Medicine, University of Heidelberg, Heidelberg
- Erwin L. Hahn Institute for MRI, University of Duisburg-Essen, Essen
| | - Daniel Paech
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg
- Clinic for Neuroradiology, University of Bonn, Bonn, Germany
| |
Collapse
|
35
|
Guo P, Wang P, Yasarla R, Zhou J, Patel VM, Jiang S. Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2832-2844. [PMID: 33351754 PMCID: PMC8543492 DOI: 10.1109/tmi.2020.3046460] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant, and a significant limit of the potential applications. In our previous work, we explored the synthesis of anatomic and molecular MR image networks (SAMR) in patients with post-treatment malignant gliomas. In this work, we extend this through a confidence-guided SAMR (CG-SAMR) that synthesizes data from lesion contour information to multi-modal MR images, including T1-weighted ( [Formula: see text]), gadolinium enhanced [Formula: see text] (Gd- [Formula: see text]), T2-weighted ( [Formula: see text]), and fluid-attenuated inversion recovery ( FLAIR ), as well as the molecular amide proton transfer-weighted ( [Formula: see text]) sequence. We introduce a module that guides the synthesis based on a confidence measure of the intermediate results. Furthermore, we extend the proposed architecture to allow training using unpaired data. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than current the state-of-the-art synthesis methods. Our code is available at https://github.com/guopengf/CG-SAMR.
Collapse
|
36
|
Park YW, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging. Neuroradiology 2021; 63:1811-1822. [PMID: 33755766 DOI: 10.1007/s00234-021-02693-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE We aimed to evaluate the utility of diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast (DSC) imaging for stratifying bevacizumab treatment outcomes in patients with recurrent high-grade glioma. METHODS Fifty-three patients with recurrent high-grade glioma who underwent baseline magnetic resonance imaging including DTI, DCE, and DSC before bevacizumab treatment were included. The mean apparent diffusion coefficient, fractional anisotropy, normalized cerebral blood volume, normalized cerebral blood flow, volume transfer constant, rate transfer coefficient (Kep), extravascular extracellular volume fraction, and plasma volume fraction were assessed. Predictors of response status, progression-free survival (PFS), and overall survival (OS) were determined using logistic regression and Cox proportional hazard modeling. RESULTS Responders (n = 16) showed significantly longer PFS and OS (P < 0.001) compared with nonresponders (n = 37). Multivariable analysis revealed that lower mean Kep (odds ratio = 0.01, P = 0.008) was the only independent predictor of favorable response after adjustment for age, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Multivariable Cox proportional hazard modeling showed that a higher mean Kep was the only variable associated with shorter PFS (hazard ratio [HR] = 7.90, P = 0.006) and OS (HR = 9.71, P = 0.020) after adjustment for age, IDH mutation status, and MGMT promoter methylation status. CONCLUSION Baseline mean Kep may be a useful biomarker for predicting response and stratifying patient outcomes following bevacizumab treatment in patients with recurrent high-grade glioma.
Collapse
Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| |
Collapse
|
37
|
Zhang J, Zhao Z, Dong L, Han T, Zhang G, Cao Y, Zhou J. Differentiating between non-functioning pituitary macroadenomas and sellar meningiomas using ADC. Endocr Connect 2020; 9:1233-1239. [PMID: 33112805 PMCID: PMC7774768 DOI: 10.1530/ec-20-0434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 11/23/2022]
Abstract
INTRODUCTION AND AIM It is difficult to distinguish between non-functioning pituitary macroadenomas (NFPMAs) and sellar meningiomas because of their overlapping imaging manifestations on routine MRI, especially in cases of meningiomas growing into the saddle. Here, we aimed to differentiate between these two tumors using apparent diffusion coefficient (ADC) values and MRI characteristics. METHODS A total of 60 NFPMA and 52 sellar meningioma cases confirmed by the pathological analysis were retrospectively reviewed. All patients were examined via routine MRI and diffusion-weighted imaging (DWI) before undergoing surgery. The clinical characteristics, MRI characteristics, and max ADC (ADCmax), average ADC (ADCmean), and minimum ADC (ADCmin) values were compared between the two tumors via Chi-square test and two sample t-tests. Receiver operating characteristic (ROC) curve and binary logistic regression analyses were conducted to determine the discrimination ability. RESULTS The ADCmax, ADCmean, and ADCmin values were significantly higher in NFPMAs compared to sellar meningiomas (P < 0.001 for all). Among ADC values, ADCmax demonstrated good performance with an AUC of 0.896 (95% CI, 0.823-0.969) and accuracy of 88.7%. A cut-off value of 0.97 × 10-3 mm2/s was used for ADCmax for differentiation between tumors. A combination of ADCmax values and clinicoradiological features showed the best discrimination ability for differential diagnosis between the two tumors, with an AUC of 0.981 (95% CI, 0.958-1.000) and accuracy of 96.9%. CONCLUSION A combination of ADCmax and clinicoradiological features demonstrates good discrimination ability and high accuracy for differentiation between NFPMAs and sellar meningiomas, and is a potential quantitative tool to aid in the selection of surgical techniques.
Collapse
Affiliation(s)
- Jing Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Zhiyong Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Li Dong
- Department of Pathology, Lanzhou University Second Hospital, Lanzhou, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Guojin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Yuntai Cao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Correspondence should be addressed to J Zhou:
| |
Collapse
|
38
|
Kang B, Kim B, Schär M, Park H, Heo HY. Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging. Magn Reson Med 2020; 85:2040-2054. [PMID: 33128483 DOI: 10.1002/mrm.28573] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 10/06/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging. METHODS Pseudo-randomized RF saturation parameters and relaxation delay times were applied in an MR fingerprinting framework to generate transient-state signal evolutions for different MTC parameters. Prospectively compressed sensing-accelerated (four-fold) MR fingerprinting images were acquired from 6 healthy volunteers at 3 T. A convolutional neural network framework in an unsupervised fashion was designed to solve an inverse problem of a two-pool MTC Bloch equation, and was compared with a conventional Bloch equation-based fitting approach. The MTC images synthesized by the convolutional neural network architecture were used for amide proton transfer and nuclear Overhauser enhancement imaging as a reference baseline image. RESULTS The fully unsupervised learning scheme incorporated with the two-pool exchange model learned a set of unique features that can describe the MTC-MR fingerprinting input, and allowed only small amounts of unlabeled data for training. The MTC parameter values estimated by the unsupervised learning method were in excellent agreement with values estimated by the conventional Bloch fitting approach, but dramatically reduced computation time by ~1000-fold. CONCLUSION Given the considerable time efficiency compared to conventional Bloch fitting, unsupervised learning-based MTC-MR fingerprinting could be a powerful tool for quantitative MTC and CEST/nuclear Overhauser enhancement imaging.
Collapse
Affiliation(s)
- Beomgu Kang
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Byungjai Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.,Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael Schär
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - HyunWook Park
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Hye-Young Heo
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| |
Collapse
|
39
|
Park JE, Kim HS. [Current Applications and Future Perspectives of Brain Tumor Imaging]. TAEHAN YONGSANG UIHAKHOE CHI 2020; 81:467-487. [PMID: 36238631 PMCID: PMC9431910 DOI: 10.3348/jksr.2020.81.3.467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/04/2020] [Accepted: 05/07/2020] [Indexed: 11/29/2022]
Abstract
뇌종양의 진단 및 치료 반응 평가의 기본이 되는 영상기법은 해부학적 영상이다. 현재 임상에서 사용 가능한 영상기법들 중 확산 강조 영상 및 관류 영상이 추가적인 정보를 제공하고 있다. 최근에는 종양의 유전체 변이와 이질성 평가가 중요해지면서 라디오믹스와 딥러닝을 이용한 영상분석기법의 임상 응용이 기대되고 있다. 본 종설에서는 뇌종양 영상 임상 적용에서 여전히 중요한 해부학적 영상을 중심으로 한 자기공명영상 촬영 권고안, 최신 영상기법 중 확산 강조 영상 및 관류 영상의 기본 원리, 병태생리학적 배경 및 임상응용, 마지막으로 최근 컴퓨터 기술의 발전으로 많이 연구되고 있는 라디오믹스와 딥러닝의 뇌종양에서의 향후 활용가치에 대해 기술하고자 한다.
Collapse
|