1
|
Smits HJG, Bennink E, Ruiter LN, Breimer GE, Willems SM, Dankbaar JW, Philippens MEP. Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study. Transl Oncol 2024; 48:102051. [PMID: 39018773 DOI: 10.1016/j.tranon.2024.102051] [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: 01/24/2024] [Revised: 05/09/2024] [Accepted: 07/01/2024] [Indexed: 07/19/2024] Open
Abstract
In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.
Collapse
Affiliation(s)
- Hilde J G Smits
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Edwin Bennink
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Lilian N Ruiter
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stefan M Willems
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan W Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | |
Collapse
|
2
|
Guo Y, Zhou L, Li Y, Chiang GC, Liu T, Chen H, Huang W, de Leon MJ, Wang Y, Chen F. Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alterations in Alzheimer's disease. Alzheimers Res Ther 2024; 16:156. [PMID: 38978146 PMCID: PMC11229285 DOI: 10.1186/s13195-024-01524-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/28/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Quantitative transport mapping (QTM) of blood velocity, based on the transport equation has been demonstrated higher accuracy and sensitivity of perfusion quantification than the traditional Kety's method-based cerebral blood flow (CBF). This study aimed to investigate the associations between QTM velocity and cognitive function in Alzheimer's disease (AD) using multiple post-labeling delay arterial spin labeling (ASL) MRI. METHODS A total of 128 subjects (21 normal controls (NC), 80 patients with mild cognitive impairment (MCI), and 27 AD) were recruited prospectively. All participants underwent MRI examination and neuropsychological evaluation. QTM velocity and traditional CBF maps were computed from multiple delay ASL. Regional quantitative perfusion measurements were performed and compared to study group differences. We tested the hypothesis that cognition declines with reduced cerebral blood perfusion with consideration of age and gender effects. RESULTS In cortical gray matter (GM) and the hippocampus, QTM velocity and CBF showed decreased values in the AD group compared to NC and MCI groups; QTM velocity, but not CBF, showed a significant difference between MCI and NC groups. QTM velocity and CBF showed values decreasing with age; QTM velocity, but not CBF, showed a significant gender difference between male and female. QTM velocity and CBF in the hippocampus were positively correlated with cognition, including global cognition, memory, executive function, and language function. CONCLUSION This study demonstrated an increased sensitivity of QTM velocity as compared with the traditional Kety's method-based CBF. Specifically, we observed only in QTM velocity, reduced perfusion velocity in GM and the hippocampus in MCI compared with NC. Both QTM velocity and CBF demonstrated a reduction in AD vs. controls. Decreased QTM velocity and CBF in the hippocampus were correlated with poor cognitive measures. These findings suggest QTM velocity as potential biomarker for early AD blood perfusion alterations and it could provide an avenue for early intervention of AD.
Collapse
Affiliation(s)
- Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Liangdong Zhou
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, 407 East 61 St ST, New York, NY, 10066, USA.
| | - Yi Li
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, 407 East 61 St ST, New York, NY, 10066, USA
| | - Gloria C Chiang
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, 407 East 61 St ST, New York, NY, 10066, USA
- Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, New York- Presbyterian Hospital, New York, NY, USA
| | - Tao Liu
- Department of Neurology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Huijuan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Mony J de Leon
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, 407 East 61 St ST, New York, NY, 10066, USA
| | - Yi Wang
- Department of Radiology, MRI Research Institute (MRIRI), Weill Cornell Medicine, New York, NY, USA
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China.
| |
Collapse
|
3
|
Li Z, Huang H, Zhao Z, Ma W, Mao H, Liu F, Yang Y, Wang D, Lu Z. Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer. Acad Radiol 2024:S1076-6332(24)00300-3. [PMID: 38816315 DOI: 10.1016/j.acra.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024]
Abstract
RATIONALE AND OBJECTIVES The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC. MATERIALS AND METHODS A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves. RESULTS Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. CONCLUSION The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.
Collapse
Affiliation(s)
- Zhiheng Li
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Huizhen Huang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Haijia Mao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Fang Liu
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Ye Yang
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.
| |
Collapse
|
4
|
Chen W, Lin G, Chen Y, Cheng F, Li X, Ding J, Zhong Y, Kong C, Chen M, Xia S, Lu C, Ji J. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study. BMC Cancer 2024; 24:418. [PMID: 38580939 PMCID: PMC10996101 DOI: 10.1186/s12885-024-12026-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 02/20/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.
Collapse
Affiliation(s)
- Weiyue Chen
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Guihan Lin
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Yongjun Chen
- Department of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
| | - Feng Cheng
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Head and Neck Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
| | - Xia Li
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Jiayi Ding
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Yi Zhong
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Chunli Kong
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Minjiang Chen
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Shuiwei Xia
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Chenying Lu
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| | - Jiansong Ji
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| |
Collapse
|
5
|
Guo Y, Zhou L, Li Y, Chiang GC, Liu T, Chen H, Huang W, de Leon MJ, Wang Y, Chen F. Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alteration in Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.18.24304481. [PMID: 38562724 PMCID: PMC10984056 DOI: 10.1101/2024.03.18.24304481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Quantitative transport mapping (QTM) of blood velocity, based on the transport equation has been demonstrated higher accuracy and sensitivity of perfusion quantification than the traditional Kety's method-based blood flow (Kety flow). This study aimed to investigate the associations between QTM velocity and cognitive function in Alzheimer's disease (AD) using multiple post-labeling delay arterial spin labeling (ASL) MRI. Methods A total of 128 subjects (21 normal controls (NC), 80 patients with mild cognitive impairment (MCI), and 27 AD) were recruited prospectively. All participants underwent MRI examination and neuropsychological evaluation. QTM velocity and traditional Kety flow maps were computed from multiple delay ASL. Regional quantitative perfusion measurements were performed and compared to study group differences. We tested the hypothesis that cognition declines with reduced cerebral blood flow with consideration of age and gender effects. Results In cortical gray matter (GM) and the hippocampus, QTM velocity and Kety flow showed decreased values in AD group compared to NC and MCI groups; QTM velocity, but not Kety flow, showed a significant difference between MCI and NC groups. QTM velocity and Kety flow showed values decreasing with age; QTM velocity, but not Kety flow, showed a significant gender difference between male and female. QTM velocity and Kety flow in the hippocampus were positively correlated with cognition, including global cognition, memory, executive function, and language function. Conclusion This study demonstrated an increased sensitivity of QTM velocity as compared with the traditional Kety flow. Specifically, we observed only in QTM velocity, reduced perfusion velocity in GM and the hippocampus in MCI compared with NC. Both QTM velocity and Kety flow demonstrated reduction in AD vs controls. Decreased QTM velocity and Kety flow in the hippocampus were correlated with cognitive measures. These findings suggest QTM velocity as an improved biomarker for early AD blood flow alterations.
Collapse
Affiliation(s)
- Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Liangdong Zhou
- Department of Radiology, Brain Health Imaging Institute (BHII), Weill Cornell Medicine, New York, New York, United States
| | - Yi Li
- Department of Radiology, Brain Health Imaging Institute (BHII), Weill Cornell Medicine, New York, New York, United States
| | - Gloria C. Chiang
- Department of Radiology, Brain Health Imaging Institute (BHII), Weill Cornell Medicine, New York, New York, United States
- Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York, USA
| | - Tao Liu
- Department of Neurology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Huijuan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Mony J. de Leon
- Department of Radiology, Brain Health Imaging Institute (BHII), Weill Cornell Medicine, New York, New York, United States
| | - Yi Wang
- Department of Radiology, MRI Research Institute (MRIRI), Weill Cornell Medicine, New York, New York, United States
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| |
Collapse
|
6
|
Zou M, Zhang B, Shi L, Mao H, Huang Y, Zhao Z. Correlation of MRI quantitative perfusion parameters with EGFR, VEGF and EGFR gene mutations in non-small cell cancer. Sci Rep 2024; 14:4447. [PMID: 38396128 PMCID: PMC10891079 DOI: 10.1038/s41598-024-55033-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 02/19/2024] [Indexed: 02/25/2024] Open
Abstract
To explore the relationship between quantitative perfusion histogram parameters of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with the expression of tumor tissue epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and EGFR gene mutations in non-small cell lung cancer (NSCLC). A total of 44 consecutive patients with known NSCLC were recruited from March 2018 to August 2021. Histogram parameters (mean, uniformity, skewness, energy, kurtosis, entropy, percentile) of each (Ktrans, Kep, Ve, Vp, Fp) were obtained by Omni Kinetics software. Immunohistochemistry staining was used in the detection of the expression of VEGF and EGFR protein, and the mutation of EGFR gene was detected by PCR. Corresponding statistical test was performed to compare the parameters and protein expression between squamous cell carcinoma (SCC) and adenocarcinoma (AC), as well as EGFR mutations and wild-type. Correlation analysis was used to evaluate the correlation between parameters with the expression of VEGF and EGFR protein. Fp (skewness, kurtosis, energy) were statistically significant between SCC and AC, and the area under the ROC curve were 0.733, 0.700 and 0.675, respectively. The expression of VEGF in AC was higher than in SCC. Fp (skewness, kurtosis, energy) were negatively correlated with VEGF (r = - 0.527, - 0.428, - 0.342); Ktrans (Q50) was positively correlated with VEGF (r = 0.32); Kep (energy), Ktrans (skewness, kurtosis) were positively correlated with EGFR (r = 0.622, r = 0.375, 0.358), some histogram parameters of Kep, Ktrans (uniformity, entropy) and Ve (kurtosis) were negatively correlated with EGFR (r = - 0.312 to - 0.644). Some perfusion histogram parameters were statistically significant between EGFR mutations and wild-type, they were higher in wild-type than mutated (P < 0.05). Quantitative perfusion histogram parameters of DCE-MRI have a certain value in the differential diagnosis of NSCLC, which have the potential to non-invasively evaluate the expression of cell signaling pathway-related protein.
Collapse
Affiliation(s)
- Mingyue Zou
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Science (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Bingqian Zhang
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Science (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Science (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Haijia Mao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Yanan Huang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, Zhejiang, China.
| |
Collapse
|
7
|
Li H, Huang W, Wang S, Balasubramanian PS, Wu G, Fang M, Xie X, Zhang J, Dong D, Tian J, Chen F. Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma. Vis Comput Ind Biomed Art 2023; 6:23. [PMID: 38036750 PMCID: PMC10689317 DOI: 10.1186/s42492-023-00149-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.
Collapse
Affiliation(s)
- Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Xuebin Xie
- Department of Radiology, Kiang Wu Hospital, Santo António, Macao, 999078, China
| | - Jie Zhang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Zhuhai, Guangdong, 519000, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
- Zhuhai Precision Medical Center, Zhuhai People's Hospital, Zhuhai, Guangdong, 519000, China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China.
| |
Collapse
|
8
|
van der Hulst HJ, Jansen RW, Vens C, Bos P, Schats W, de Jong MC, Martens RM, Bodalal Z, Beets-Tan RGH, van den Brekel MWM, de Graaf P, Castelijns JA. The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:5077. [PMID: 37894447 PMCID: PMC10605807 DOI: 10.3390/cancers15205077] [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: 08/18/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC.
Collapse
Affiliation(s)
- Hedda J. van der Hulst
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, 6211 LK Maastricht, The Netherlands
| | - Robin W. Jansen
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Conchita Vens
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- School of Cancer Science, University of Glasgow, Glasgow G61 1QH, UK
| | - Paula Bos
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Winnie Schats
- Scientific Information Service, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Marcus C. de Jong
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Roland M. Martens
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, 6211 LK Maastricht, The Netherlands
| | - Regina G. H. Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, 6211 LK Maastricht, The Netherlands
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark
| | - Michiel W. M. van den Brekel
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HZ Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Jonas A. Castelijns
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| |
Collapse
|
9
|
Yang F, Li X, Li Y, Lei H, Du Q, Yu X, Li L, Zhao Y, Xie L, Lin M. Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors in nasopharyngeal carcinoma. Eur Radiol 2023; 33:5344-5354. [PMID: 37036478 DOI: 10.1007/s00330-023-09553-9] [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: 08/31/2022] [Revised: 01/30/2023] [Accepted: 02/17/2023] [Indexed: 04/11/2023]
Abstract
OBJECTIVES To evaluate the correlation between histogram parameters derived from synthetic magnetic resonance imaging (SyMRI) and prognostically relevant factors in nasopharyngeal carcinoma (NPC). METHODS Fifty-nine consecutive NPC patients were prospectively enrolled. Quantitative parameters (T1, T2, and proton density (PD)) were obtained by outlining the three-dimensional volume of interest (VOI) of all lesions. Then, histogram analysis of these quantitative parameters was performed and the correlations with prognostically relevant factors were assessed. By choosing appropriate cutoff, we divided the sample into two groups. Independent-samples t test/Mann-Whitney U test was used and ROC curve analysis was further processed. RESULTS Histogram parameters of the T1, T2, and PD maps were positively correlated with the Ki-67 expression levels, and PD_mean was the most representative parameter (AUC: 0.861). The PD map exhibited good performance in differentiating epidermal growth factor receptor (EGFR) expression levels (AUC: 0.706~0.732) and histological type (AUC: 0.650~0.660). T2_minimum was highest correlated with Epstein-Barr virus (EBV) DNA levels (r = - 0.419), and PD_75th percentile exhibited the highest performance in distinguishing positive and negative EBV DNA groups (AUC: 0.721). T1_minimum was statistically correlated with EA-IgA expression (r = - 0.313). Additionally, several histogram parameters were negatively correlated with tumor stage (T stage: r = - 0.259 ~ - 0.301; N stage: r = - 0.348 ~ - 0.456; clinical stage: r = - 0.419). CONCLUSIONS Histogram parameters of SyMRI could reflect tissue intrinsic characteristics and showed potential value in assessing the Ki-67 and EGFR expression levels, histological type, EBV DNA level, EA-IgA, and tumor stage. KEY POINTS • SyMRI combined with histogram analysis may help clinicians to assess different prognostic factor statuses in nasopharyngeal carcinoma. • The PD map exhibited good discriminating performance in the Ki-67 and EGFR expression levels. • Histogram parameters of SyMRI were negatively correlated with EBV-related blood biomarkers and TNM stage.
Collapse
Affiliation(s)
- Fan Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaolu Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yujie Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Huizi Lei
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qiang Du
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaoduo Yu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yanfeng Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, China
| | - Meng Lin
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| |
Collapse
|
10
|
Zhang Q, Luo X, Zhou L, Nguyen TD, Prince MR, Spincemaille P, Wang Y. Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification. IEEE Trans Biomed Eng 2023; 70:980-990. [PMID: 36107908 DOI: 10.1109/tbme.2022.3207057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. METHODS QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI. RESULTS In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. CONCLUSION QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. SIGNIFICANCE We propose to apply QTM method in liver DCE MRI perfusion quantification.
Collapse
|
11
|
Zhang Q, Wu G, Yang Q, Dai G, Li T, Chen P, Li J, Huang W. Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network. Cancer Sci 2022; 114:1596-1605. [PMID: 36541519 PMCID: PMC10067413 DOI: 10.1111/cas.15704] [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: 07/13/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 12/24/2022] Open
Abstract
To achieve a better treatment regimen and follow-up assessment design for intensity-modulated radiotherapy (IMRT)-treated nasopharyngeal carcinoma (NPC) patients, an accurate progression-free survival (PFS) time prediction algorithm is needed. We propose developing a PFS prediction model of NPC patients after IMRT treatment using a deep learning method and comparing that with the traditional texture analysis method. One hundred and fifty-one NPC patients were included in this retrospective study. T1-weighted, proton density and dynamic contrast-enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF-1α, EGFR, PTEN, Ki-67, and VEGF) and infection of Epstein-Barr (EB) virus were tested. A residual network was trained to predict PFS from MR images. The output as well as patient characteristics were combined using a linear regression model to provide a final PFS prediction. The prediction accuracy was compared with that of the traditional texture analysis method. A regression model combining the deep learning output with HIF-1α expression and Epstein-Barr infection provides the best PFS prediction accuracy (Spearman correlation R2 = 0.53; Harrell's C-index = 0.82; receiver operative curve [ROC] analysis area under the curve [AUC] = 0.88; log-rank test hazard ratio [HR] = 8.45), higher than a regression model combining texture analysis with HIF-1α expression (Spearman correlation R2 = 0.14; Harrell's C-index =0.68; ROC analysis AUC = 0.76; log-rank test HR = 2.85). The deep learning method does not require a manually drawn tumor region of interest. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and does not rely on specific kernels or tumor regions of interest, which is needed for the texture analysis method.
Collapse
Affiliation(s)
- Qihao Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital, Hainan, China
| | - Qianyu Yang
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Ganmian Dai
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Tiansheng Li
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Pianpian Chen
- Department of Pathology, Hainan General Hospital, Hainan, China
| | - Jiao Li
- Department of Pathology, Hainan General Hospital, Hainan, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital, Hainan, China
| |
Collapse
|
12
|
Li X, Hu Y, Xie Y, Lu R, Li Q, Tao H, Chen S. Whole-tumor histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for soft tissue sarcoma: correlation with HIF-1alpha expression. Eur Radiol 2022; 33:3961-3973. [PMID: 36462043 DOI: 10.1007/s00330-022-09296-z] [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: 09/02/2022] [Revised: 09/02/2022] [Accepted: 11/10/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVE To investigate the correlation of histogram metrics from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with HIF-1alpha expression in soft tissue sarcoma (STS). METHODS We enrolled 71 patients with STS who underwent 3.0-T MRI, including conventional MRI, DWI, and DCE-MRI sequences. Location, maximum tumor diameter, envelope, T2-weighted tumor heterogeneity, peritumoral edema, peritumoral enhancement, necrosis, tail-like pattern, bone invasion, and vessel/nerve invasion and/or encasement were determined using conventional MRI images. The whole-tumor histogram metrics were calculated on the apparent diffusion coefficient (ADC), Ktrans, Kep, and Ve maps. Independent-samples t test and one-way ANOVA were used for testing the differences between normally distributed categorical data with HIF-1alpha expression. Pearson and Spearman correlations and multiple linear regression analyses were performed to determine the correlations between histogram metrics and HIF-1alpha expression. Survival curves were plotted using the Kaplan-Meier method. RESULTS Regarding conventional MRI features, only highly heterogeneous on T2-weighted images (55.6 ± 19.9% vs. 45.4 ± 20.5%, p = 0.041) and more than 50% necrotic area (57.3 ± 20.4% vs. 43.9 ± 19.7%, p = 0.002) were prone to indicate STS with higher HIF-1alpha expression. Histogram metrics obtained from ADC (mean, median, 10th, and 25th percentile values), Ktrans (mean, median, 75th, and 90th percentile values), and Kep (90th percentile values) were significantly correlated with HIF-1alpha expression. Multiple linear regression analysis demonstrated that more than 50% necrosis, ADCskewness, Ktrans90th, and grade III were independently associated with HIF-1alpha expression. CONCLUSION DWI and DCE-MRI histogram parameters were significantly correlated with HIF-1alpha expression in STS. KEY POINTS • DWI and DCE-MRI histogram parameters are correlated with HIF-1alpha expression in STS. • More than 50% necrosis, ADCskewness, Ktrans90th, and grade III were independently associated with HIF-1alpha expression in STS.
Collapse
Affiliation(s)
- Xiangwen Li
- Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China
| | - Yiwen Hu
- Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China
| | - Yuxue Xie
- Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China
| | - Rong Lu
- Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China
| | - Qing Li
- MR Collaborations, Siemens Healthineers Ltd., Shanghai, China
| | - Hongyue Tao
- Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China.
| | - Shuang Chen
- Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, 2 middle Wulumuqizhong Road, Shanghai, China.
| |
Collapse
|
13
|
Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing. Tomography 2022; 8:2687-2697. [PMID: 36412683 PMCID: PMC9680251 DOI: 10.3390/tomography8060224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety's tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤&nbsp;10%) and a high-risk group (LSF >&nbsp;10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety's parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety's Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve&nbsp;(0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.
Collapse
|
14
|
Li JP, Liu YJ, Zeng SH, Gao HJ, Chen YG, Zou X. Identification of COX4I2 as a hypoxia-associated gene acting through FGF1 to promote EMT and angiogenesis in CRC. Cell Mol Biol Lett 2022; 27:76. [PMID: 36064310 PMCID: PMC9446847 DOI: 10.1186/s11658-022-00380-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background Current evidence suggests that the hypoxic tumor microenvironment further aggravates tumor progression, leading to poor therapeutic outcomes. There is as yet no biomarker capable of evaluating the hypoxic state of the tumor. The cytochrome c oxidase (COX) subunit is crucial to the mitochondrial respiratory chain. Methods We investigated the potential oncogenic role of COX subunit 4 isoform 2 gene (COX4I2) in colorectal cancer (CRC) by least absolute shrinkage and selection operator (LASSO) and COX regression analysis to examine whether COX4I2 overexpression can predict colorectal cancer (CRC) prognosis. The association of COX4I2 levels with clinical features and its biological actions were evaluated both in vitro and in vivo. Results Our analysis showed that elevated COX4I2 levels were correlated with poor clinical outcomes. We also observed that that COX4I2 may be involved in epithelial-mesenchymal transition, activation of cancer-related fibroblasts and angiogenesis in relation to fibroblast growth factor 1. Conclusions The COX4I2 level may be a predictor of outcome in CRC and may represent a novel target for treatment development. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s11658-022-00380-2.
Collapse
Affiliation(s)
- Jie-Pin Li
- Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, China.,Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, 210029, Jiangsu, China.,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Yuan-Jie Liu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, 210029, Jiangsu, China.,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Shu-Hong Zeng
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, 210029, Jiangsu, China.,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Hai-Jian Gao
- Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, China
| | - Yu-Gen Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, 210029, Jiangsu, China.
| | - Xi Zou
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, 210029, Jiangsu, China. .,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China. .,Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| |
Collapse
|