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Wang Q, An P, Song L, Liu J, Liu J. Prognostic modeling for nasopharyngeal carcinoma (NC) undergoing concurrent chemoradiotherapy using clinical and enhanced MRI-Delta radiomics data: A preliminary study. Technol Health Care 2024; 32:2381-2394. [PMID: 38517817 DOI: 10.3233/thc-231173] [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: 03/24/2024]
Abstract
BACKGROUND Nasopharyngeal carcinoma (NC) is one of the prevalent malignancies of the head and neck region with poor prognosis. OBJECTIVE The aim of this study is to establish a predictive model for assessing NC prognosis based on clinical and MR radiomics data, subsequently to develop a nomogram for practical application. METHODS Retrospective analysis was conducted on clinical and imaging data collected between May 2010 and August 2018, involving 211 patients diagnosed with histologically confirmed NC who received concurrent chemoradiotherapy or radical surgery in Xiangyang No. 1 People's Hospital. According to 5-10 years of follow-up results, the patients were divided into two groups: the study group (n= 76), which experienced recurrence, metastasis, or death, and the control group (n= 135), characterized by normal survival. Training and testing subsets were established at a 7:3 ratio, with a predefined time cutoff. In the training set, three prediction models were established: a clinical data model, an imaging model, and a combined model using the integrated variation in clinical characteristics along with MR radiomics parameters (Delta-Radscore) observed before and after concurrent chemoradiotherapy. Model performance was compared using Delong's test, and net clinical benefit was assessed via decision curve analysis (DCA). Then, external validation was conducted on the test set, and finally a nomogram predicting NC prognosis was created. RESULTS Univariate analysis identified that the risk factors impacting the prognosis of NC included gender, pathological type, neutrophil to lymphocyte ratio (NLR), degree of tumor differentiation, MR enhancement pattern, and Delta-Radscore (P< 0.05). The combined model established based on the abovementioned factors exhibited significantly higher predictive performance [AUC: 0.874, 95% CI (0.810-0.923)] than that of the clinical data model [AUC: 0.650, 95% CI (0.568-0.727)] and imaging model [AUC: 0.824, 95% CI (0.753-0.882)]. DCA also demonstrated superior clinical net benefit in the combined model, a finding further verified by results from the test set. The developed nomogram, based on the combined model, exhibited promising performance in clinical applications. CONCLUSION The Delta-Radscore derived from MR radiomics data before and after concurrent chemoradiotherapy helps enhance the performance of the NC prognostic model. The combined model and resultant nomogram provide valuable support for clinical decision-making in NC treatment, ultimately contributing to an improved survival rate.
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Affiliation(s)
- Qiuyang Wang
- Department of ENT, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Peng An
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, China
| | - Lina Song
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, China
| | - Junjie Liu
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, China
| | - Jisheng Liu
- Department of ENT, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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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.
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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.
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3
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Wishart LR, Ward EC, Galloway G. Advances in and applications of imaging and radiomics in head and neck cancer survivorship. Curr Opin Otolaryngol Head Neck Surg 2023; 31:368-373. [PMID: 37548514 DOI: 10.1097/moo.0000000000000918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
PURPOSE OF REVIEW Radiological imaging is an essential component of head/neck cancer (HNC) care. Advances in imaging modalities (including CT, PET, MRI and ultrasound) and analysis have enhanced our understanding of tumour characteristics and prognosis. However, the application of these methods to evaluate treatment-related toxicities and functional burden is still emerging. This review showcases recent literature applying advanced imaging and radiomics to the assessment and management of sequelae following chemoradiotherapy for HNC. RECENT FINDINGS Whilst primarily early-stage/exploratory studies, recent investigations have showcased the feasibility of using radiological imaging, particularly advanced/functional MRI (including diffusion-weighted and dynamic contrast-enhanced MRI), to quantify treatment-induced tissue change in the head/neck musculature, and the clinical manifestation of lymphoedema/fibrosis and dysphagia. Advanced feature analysis and radiomic studies have also begun to give specific focus to the prediction of functional endpoints, including dysphagia, trismus and fibrosis. SUMMARY There is demonstrated potential in the use of novel imaging techniques, to help better understand pathophysiology, and improve assessment and treatment of functional deficits following HNC treatment. As larger studies emerge, technologies continue to progress, and pathways to clinical translation are honed, the application of these methods offers an exciting opportunity to transform clinical practices and improve outcomes for HNC survivors.
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Affiliation(s)
- Laurelie R Wishart
- Centre for Functioning & Health Research, Metro South Hospital & Health Service
- School of Health and Rehabilitation Sciences, The University of Queensland
| | - Elizabeth C Ward
- Centre for Functioning & Health Research, Metro South Hospital & Health Service
- School of Health and Rehabilitation Sciences, The University of Queensland
| | - Graham Galloway
- Translational Research Institute
- Herston Imaging Research Facility, The University of Queensland, Brisbane, Australia
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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5
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Xu X, Chen M, Zhang J, Jiang Y, Chao H, Zha J. Can the apparent transverse relaxation rate (R2 *) evaluate the efficacy of concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma? a preliminary experience. BMC Med Imaging 2023; 23:69. [PMID: 37264331 DOI: 10.1186/s12880-023-01029-y] [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: 01/25/2023] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND The use of the apparent transverse relaxation rate (R2*) in nasopharyngeal carcinoma (NPC) has not been previously reported in the literature. The aim of this study was to investigate the role of the R2* value in evaluating response to concurrent chemoradiotherapy (CCRT) in patients with NPC. METHODS Forty-one patients with locoregionally advanced NPC confirmed by pathology were examined by blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) before and after CCRT, and conventional MRI was performed 3 months after the completion of CCRT. All patients were divided into a responding group (RG) and a nonresponding group (NRG), according to MRI findings 3 months after the end of treatment. The R2* values before (R2*preT) and after (R2*postT) CCRT and the ΔR2* (ΔR2*=R2*postT - R2*preT) were calculated in the tumor. RESULTS Among the 41 patients, 26 were in the RG and 15 were in the NRG. There was no statistical difference in the R2*preT between RG and NRG (P = 0.307); however, there were significant differences in R2*postT and ΔR2* (P < 0.001). The area under the curve of R2*postT and ΔR2* for predicting the therapeutic response of NPC was 0.897 and 0.954, respectively, with cutoff values of 40.95 and 5.50 Hz, respectively. CONCLUSION The R2* value can be used as a potential imaging indicator to evaluate the therapeutic response of locoregionally advanced NPC.
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Affiliation(s)
- Xinhua Xu
- Department of Radiology, Changzhou Cancer Hospital of Soochow University, 68 Honghe Road, Changzhou, 213000, Jiangsu, PR China
| | - Ming Chen
- Department of Radiology, Changzhou Cancer Hospital of Soochow University, 68 Honghe Road, Changzhou, 213000, Jiangsu, PR China.
| | - Jin Zhang
- Department of Radiology, Changzhou Cancer Hospital of Soochow University, 68 Honghe Road, Changzhou, 213000, Jiangsu, PR China
| | - Yunzhu Jiang
- Department of Radiology, Changzhou Cancer Hospital of Soochow University, 68 Honghe Road, Changzhou, 213000, Jiangsu, PR China
| | - Hua Chao
- Department of Radiology, Changzhou Cancer Hospital of Soochow University, 68 Honghe Road, Changzhou, 213000, Jiangsu, PR China
| | - Jianfeng Zha
- Department of Radiology, Changzhou Cancer Hospital of Soochow University, 68 Honghe Road, Changzhou, 213000, Jiangsu, PR China
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Li H, Gong G, Wang L, Su Y, Lu J, Yin Y. The therapeutic utility of combining dynamic contrast-enhanced magnetic resonance imaging with arterial spin labeling in the staging of nasopharyngeal carcinoma. BMC Med Imaging 2023; 23:61. [PMID: 37138205 PMCID: PMC10155316 DOI: 10.1186/s12880-023-01016-3] [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: 11/11/2022] [Accepted: 04/19/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND To research the pathological and clinical staging uses of arterial spin labeling (ASL) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS 64 newly diagnosed nasopharyngeal carcinoma (NPC) patients were enrolled from December 2020 to January 2022, and 3.0 T MRI (Discovery 750W, GE Healthcare, USA) were used for ASL and DCE-MRI scans. The DCE-MRI and ASL raw data were processed post-acquisition on the GE image processing workstation (GE Healthcare, ADW 4.7, USA). The volume transfer constant (Ktrans), blood flow (BF), and accompanying pseudo-color images were generated automatically. Draw the region of interest (ROIs), and the Ktrans and BF values for each ROI were recorded separately. Based on pathological information and the most recent AJCC staging criteria, patients were divided into low T stage groups = T1-2 and high T stage groups = T3-4, low N stage groups = N0-1 and high N stage groups = N2-3, and low AJCC stage group = stage I-II and high AJCC stage group = stage III-IV. The association between the Ktranst and BF parameters and the T, N, and AJCC stages was compared using an independent sample t-test. Using a receiver operating characteristic (ROC) curve, the sensitivity, specificity, and AUC of Ktranst, BFt, and their combined use in T and AJCC staging of NPC were investigated and assessed. RESULT The tumor-BF (BFt) (t = - 4.905, P < 0.001) and tumor-Ktrans (Ktranst) (t = - 3.113, P = 0.003) in the high T stage group were significantly higher than those in the low T stage group. The Ktranst in the high N stage group was significantly higher than that in the low N stage group (t = - 2.071, P = 0.042). The BFt (t = - 3.949, P < 0.001) and Ktranst (t = - 4.467, P < 0.001) in the high AJCC stage group were significantly higher than those in the low AJCC stage group. BFt was moderately positively correlated with the T stage (r = 0.529, P < 0.001) and AJCC stage (r = 0.445, P < 0.001). Ktranst was moderately positively correlated with T staging (r = 0.368), N staging (r = 0.254), and AJCC staging (r = 0.411). There was also a positive correlation between BF and Ktrans in gross tumor volume (GTV) (r = 0.540, P < 0.001), parotid (r = 0.323, P < 0.009) and lateral pterygoid muscle (r = 0.445, P < 0.001). The sensitivity of the combined application of Ktranst and BFt for AJCC staging increased from 76.5 and 78.4 to 86.3%, and the AUC value increased from 0.795 and 0.819 to 0.843, respectively. CONCLUSION Combining Ktrans and BF measures may make it possible to identify the clinical stages in NPC patients.
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Affiliation(s)
- Haodong Li
- Department of Graduate, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, 250000, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Guanzhong Gong
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Lizhen Wang
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Ya Su
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Jie Lu
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Yong Yin
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
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Ng WT, Chow JCH, Beitler JJ, Corry J, Mendenhall W, Lee AWM, Robbins KT, Nuyts S, Saba NF, Smee R, Stokes WA, Strojan P, Ferlito A. Current Radiotherapy Considerations for Nasopharyngeal Carcinoma. Cancers (Basel) 2022; 14:5773. [PMID: 36497254 PMCID: PMC9736118 DOI: 10.3390/cancers14235773] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Radiotherapy is the primary treatment modality for nasopharyngeal carcinoma (NPC). Successful curative treatment requires optimal radiotherapy planning and precise beam delivery that maximizes locoregional control while minimizing treatment-related side effects. In this article, we highlight considerations in target delineation, radiation dose, and the adoption of technological advances with the aim of optimizing the benefits of radiotherapy in NPC patients.
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Affiliation(s)
- Wai Tong Ng
- Clinical Oncology Center and Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518009, China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - James C. H. Chow
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | | | - June Corry
- Division of Radiation Oncology, GenesisCare Radiation Oncology, St. Vincent’s Hospital, Melbourne, VIC 3065, Australia
| | - William Mendenhall
- Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, FL 32610, USA
| | - Anne W. M. Lee
- Clinical Oncology Center and Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518009, China
| | - K Thomas Robbins
- Department of Otolaryngology-Head and Neck Surgery, Southern Illinois University School of Medicine, Springfield, IL 62794-9620, USA
| | - Sandra Nuyts
- Department of Radiation Oncology, University Hospitals Leuven, KU Leuven—University of Leuven, 3000 Leuven, Belgium
| | - Nabil F. Saba
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Robert Smee
- Department of Radiation Oncology, The Prince of Wales Cancer Centre, Sydney, NSW 2031, Australia
| | - William A. Stokes
- Department of Radiation Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Primož Strojan
- Department of Radiation Oncology, Institute of Oncology, 1000 Ljubljana, Slovenia
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, 35100 Padua, Italy
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Dong W, Volk A, Djaroum M, Girot C, Balleyguier C, Lebon V, Garcia G, Ammari S, Temam S, Gorphe P, Wei L, Pitre-Champagnat S, Lassau N, Bidault F. Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer. J Magn Reson Imaging 2022. [PMID: 36269053 DOI: 10.1002/jmri.28486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). PURPOSE To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. STUDY TYPE Prospective. POPULATION A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. ASSESSMENT Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. STATISTICAL TESTS Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. RESULTS Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). DATA CONCLUSION We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Wanxin Dong
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Andreas Volk
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Meriem Djaroum
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Charly Girot
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Corinne Balleyguier
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Vincent Lebon
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Gabriel Garcia
- Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Samy Ammari
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Stéphane Temam
- Department of Head and Neck Oncology, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Philippe Gorphe
- Department of Head and Neck Oncology, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Lecong Wei
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Stéphanie Pitre-Champagnat
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Nathalie Lassau
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - François Bidault
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
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9
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Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:3790269. [PMID: 35677026 PMCID: PMC9159821 DOI: 10.1155/2022/3790269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022]
Abstract
The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image super-resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U-net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant (Ktrans), rate constant (Kep), volume fraction (Ve), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U-net network model. Meanwhile, the difference was statistically significant (P < 0.05). Ktrans, Kep, and Ve in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side (P < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). The diagnostic sensitivity of DCE-MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE-MRI (P < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE-MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE-MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma.
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Wong KL, Cheng KH, Lam SK, Liu C, Cai J. Review of functional magnetic resonance imaging in the assessment of nasopharyngeal carcinoma treatment response. PRECISION RADIATION ONCOLOGY 2022. [DOI: 10.1002/pro6.1161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Kwun Lam Wong
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
- Department of Radiotherapy Hong Kong Sanatorium & Hospital HKSH Medical Group Hong Kong SAR People's Republic of China
| | - Ka Hei Cheng
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
| | - Sai Kit Lam
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
| | - Chenyang Liu
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
| | - Jing Cai
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
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