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Qin K, Gong C, Cheng Y, Li L, Liu C, Yang F, Rao J, Li Q. Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma. AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2024; 14:239-252. [PMID: 39309414 PMCID: PMC11411193 DOI: 10.62347/jmkv7596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 08/08/2024] [Indexed: 09/25/2024]
Abstract
OBJECTIVE To explore the connection between TGF-β1 expression and the survival of patients with head and neck squamous cell carcinoma (HNSCC), as well as whether non-invasive CT-based Radiomics can predict TGF-β1 expression in HNSCC patients. METHODS Data on transcriptional profiling and clinical information were acquired from the TCGA database and subsequently categorized based on the TGF-β1 expression cutoff value. Based on the completeness of enhanced arterial phase CT scans, 139 HNSCC patients were selected. The PyRadiomics package was used to extract radiomic features, and the 3D Slicer software was used for image segmentation. Using the mRMR_RFE and Repeat LASSO algorithms, the optimal features for establishing the corresponding gradient enhancement prediction models were identified. RESULTS A survival analysis was performed on 483 patients, who were divided into two groups based on the TGF-β1 expression cut-off. The Kaplan-Meier curve indicated that TGF-β1 was a significant independent risk factor that reduced patient survival. To construct gradient enhancement prediction models, we used the mRMR_RFE algorithm and the Repeat_LASSO algorithm to obtain two features (glrlm and ngtdm) and three radiation features (glrlm, first order_10percentile, and gldm). In both the training and validation cohorts, the two established models demonstrated strong predictive potential. Furthermore, there was no statistically significant difference in the calibration curve, DCA diagram, or AUC values between the mRMR_RFE_GBM model and the LASSO_GBM model, suggesting that both models fit well. CONCLUSION Based on these findings, TGF-β1 was shown to be significantly associated with a poor prognosis and to be a potential risk factor for HNSCC. Furthermore, by employing the mRMR_RFE_GBM and Repeat_LASSO_GBM models, we were able to effectively predict TGF-β1 expression levels in HNSCC through non-invasive CT-based Radiomics.
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Affiliation(s)
- Kai Qin
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Chen Gong
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Yi Cheng
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Li Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Chengxia Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Feng Yang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Jie Rao
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Qianxia Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
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Chen LL, Lauwers I, Verduijn G, Philippens M, Gahrmann R, Capala ME, Petit S. MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review. Cancers (Basel) 2024; 16:2105. [PMID: 38893224 PMCID: PMC11171338 DOI: 10.3390/cancers16112105] [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: 04/22/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV-) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV- tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV- ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV- than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV- OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation.
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Affiliation(s)
- Linda L. Chen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Iris Lauwers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Gerda Verduijn
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Marielle Philippens
- Department of Radiotherapy, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Marta E. Capala
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Steven Petit
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
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Sijtsema ND, Lauwers I, Verduijn GM, Hoogeman MS, Poot DH, Hernandez-Tamames JA, van der Lugt A, Capala ME, Petit SF. Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma. Phys Imaging Radiat Oncol 2024; 30:100574. [PMID: 38633282 PMCID: PMC11021835 DOI: 10.1016/j.phro.2024.100574] [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: 01/19/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Background and purpose Diffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response. Materials and methods Thirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease. Results ADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed. Conclusion The results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.
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Affiliation(s)
- Nienke D. Sijtsema
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Iris Lauwers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Gerda M. Verduijn
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mischa S. Hoogeman
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Medical Physics and Informatics, HollandPTC, Delft, the Netherlands
| | - Dirk H.J. Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Juan A. Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marta E. Capala
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Steven F. Petit
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Yan Q, Yan X, Yang X, Li S, Song J. The use of PET/MRI in radiotherapy. Insights Imaging 2024; 15:63. [PMID: 38411742 PMCID: PMC10899128 DOI: 10.1186/s13244-024-01627-6] [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: 09/19/2023] [Accepted: 01/21/2024] [Indexed: 02/28/2024] Open
Abstract
Positron emission tomography/magnetic resonance imaging (PET/MRI) is a hybrid imaging technique that quantitatively combines the metabolic and functional data from positron emission tomography (PET) with anatomical and physiological information from MRI. As PET/MRI technology has advanced, its applications in cancer care have expanded. Recent studies have demonstrated that PET/MRI provides unique advantages in the field of radiotherapy and has become invaluable in guiding precision radiotherapy techniques. This review discusses the rationale and clinical evidence supporting the use of PET/MRI for radiation positioning, target delineation, efficacy evaluation, and patient surveillance.Critical relevance statement This article critically assesses the transformative role of PET/MRI in advancing precision radiotherapy, providing essential insights into improved radiation positioning, target delineation, efficacy evaluation, and patient surveillance in clinical radiology practice.Key points• The emergence of PET/MRI will be a key bridge for precise radiotherapy.• PET/MRI has unique advantages in the whole process of radiotherapy.• New tracers and nanoparticle probes will broaden the use of PET/MRI in radiation.• PET/MRI will be utilized more frequently for radiotherapy.
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Affiliation(s)
- Qi Yan
- Cancer Center, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Taiyuan, China
| | - Xia Yan
- Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory for Translational Nuclear Medicine and Precision Protection, Taiyuan, China
| | - Xin Yang
- Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Sijin Li
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, China.
| | - Jianbo Song
- Cancer Center, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Taiyuan, China.
- Shanxi Provincial Key Laboratory for Translational Nuclear Medicine and Precision Protection, Taiyuan, China.
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Deantonio L, Castronovo F, Paone G, Treglia G, Zilli T. Metabolic Imaging for Radiation Therapy Treatment Planning: The Role of Hybrid PET/MR Imaging. Magn Reson Imaging Clin N Am 2023; 31:637-654. [PMID: 37741647 DOI: 10.1016/j.mric.2023.06.005] [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: 09/25/2023]
Abstract
The use of hybrid PET/MR imaging for radiotherapy treatment planning has the potential to reduce tumor and organ displacements caused by different scan times and setup changes. Although with mixed results mainly due to single-center studies with small sample size, PET/MR imaging could provide better target delineation, especially by reducing coregistration discrepancies on computed tomography simulation scan and offering better soft tissue contrast. The main limitation to drive stronger conclusions is due to the relatively low availability of hybrid PET/MR imaging systems, mainly limited to large academic centers.
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Affiliation(s)
- Letizia Deantonio
- Radiation Oncology Clinic, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona 6500, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano 6900, Switzerland
| | - Francesco Castronovo
- Radiation Oncology Clinic, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona 6500, Switzerland
| | - Gaetano Paone
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano 6900, Switzerland; Clinic for Nuclear Medicine and Molecular Imaging, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona 6500, Switzerland
| | - Giorgio Treglia
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano 6900, Switzerland; Clinic for Nuclear Medicine and Molecular Imaging, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona 6500, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1015, Switzerland
| | - Thomas Zilli
- Radiation Oncology Clinic, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona 6500, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano 6900, Switzerland; Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland.
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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.
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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
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Histogram analysis of synthetic magnetic resonance imaging: Correlations with histopathological factors in head and neck squamous cell carcinoma. Eur J Radiol 2023; 160:110715. [PMID: 36753947 DOI: 10.1016/j.ejrad.2023.110715] [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: 12/31/2022] [Accepted: 01/24/2023] [Indexed: 01/30/2023]
Abstract
PURPOSE To analyse the association between histogram parameters derived from synthetic MRI (SyMRI) and different histopathological factors in head and neck squamous cell carcinoma (HNSCC). METHOD Sixty-one patients with histologically proven primary HNSCC were prospectively enrolled. The correlations between histogram parameters of SyMRI (T1, T2 and proton density (PD) maps) and histopathological factors were analysed using Spearman analysis. The Mann-Whitney U test or Student's t test was utilized to differentiate histological grades and human papillomavirus (HPV) status. The ROC curves and leave-one-out cross-validation (LOOCV) were used to evaluate the differentiation performance. Bootstrapping was applied to avoid overfitting. RESULTS Several histogram parameters were associated with histological grade: T1 map (r = 0.291) and PD map (r = 0.294 - 0.382/-0.343), and PD_75th Percentile showed the highest differentiation performance (AUC: 0.721 (ROC) and 0.719 (LOOCV)). Moderately negative correlations were found between p16 status and the histogram parameters: T1 map (r = -0.587 - -0.390), T2 map (r = -0.649 - -0.357) and PD map (r = -0.537 - -0.338). In differentiating HPV infection, Entropy was the most discriminative parameter in each map and T2_Entropy showed the highest diagnostic performance (AUC: 0.851 [ROC] and 0.851 [LOOCV]). Additionally, several histogram parameters were correlated with Ki-67 (r = -0.379/-0.397), epidermal growth factor receptor (EGFR) (r = 0.318/0.322) status and p53 (r = 0.452 - 0.665/-0.607) status. CONCLUSIONS Histogram parameters derived from SyMRI may serve as a potential biomarker for discriminating relevant histopathological features, including histological differentiation grade, HPV infection, Ki-67, EGFR and p53 statuses.
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Woo C, Jo KH, Sohn B, Park K, Cho H, Kang WJ, Kim J, Lee SK. Development and Testing of a Machine Learning Model Using 18F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma. Korean J Radiol 2023; 24:51-61. [PMID: 36606620 PMCID: PMC9830147 DOI: 10.3348/kjr.2022.0397] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 09/28/2022] [Accepted: 10/31/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. MATERIALS AND METHODS This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. RESULTS In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. CONCLUSION Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.
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Affiliation(s)
- Changsoo Woo
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kwan Hyeong Jo
- Department of Nuclear Medicine, Korea University Guro Hospital, Seoul, Korea.
| | - Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Kisung Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.,Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Hojin Cho
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Won Jun Kang
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, 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, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Predictive Value of 18F-Fluorodeoxyglucose Positron-Emission Tomography Metabolic and Volumetric Parameters for Systemic Metastasis in Tonsillar Cancer. Cancers (Basel) 2022; 14:cancers14246242. [PMID: 36551727 PMCID: PMC9777518 DOI: 10.3390/cancers14246242] [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: 11/21/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Although the prognosis of tonsillar cancer (human papillomavirus-positive oropharyngeal squamous cell carcinoma) is improving, disease control failure (distant metastasis) still occurs in some cases. We explored whether several 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) parameters can predict metastasis. We retrospectively reviewed the medical records of 55 patients with tonsil squamous cell carcinoma who underwent pretreatment 18F-FDG positron-emission tomography/computed tomography (PET/CT) followed by primary surgery. During the follow-up period, systemic metastases were found in 7 of the 55 patients. The most common sites were the lungs (33%), bone (22%), brain/skull base (22%), small bowel (11%), and liver (11%). Pathologically, P53 mutation was less common in patients with systemic metastasis (41.7% vs. 14.3%, p = 0.054) than without systemic metastasis. In terms of PET parameters, the metabolic tumor volume (MTV2.5) and total lesion glycolysis (TLG2.5) values were lower in the primary tumor, and higher in the metastatic lymph nodes, of human papillomavirus (HPV)-positive compared to HPV-negative patients (all p < 0.05). The MTV2.5, TLG2.5, and tumor−to−liver uptake ratio were 36.07 ± 54.24 cm3, 183.46 ± 298.62, and 4.90 ± 2.77, respectively, in the systemic metastasis group, respectively; all of these values were higher than those of the patients without systemic metastasis (all p < 0.05). The MTV2.5 value was significantly different between the groups even when the values for the primary tumor and metastatic lymph nodes were summed (53.53 ± 57.78 cm3, p = 0.036). The cut-off value, area under the curve (95% confidence interval), sensitivity, and specificity of MTV2.5 for predicting systemic metastasis were 11.250 cm3, 0.584 (0.036−0.832), 0.571, and 0.565, respectively. The MTV2.5 of metastatic lymph nodes and summed MTV2.5 values of the primary tumor and metastatic lymph nodes were significantly higher in tonsillar cancer patients with than without systemic metastases. We suggest PET/CT scanning for pre-treatment cancer work-up and post-treatment surveillance to consider additional systemic therapy in patients with a high risk of disease control failure.
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The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment. Neuroradiology 2022; 64:1639-1647. [PMID: 35459957 PMCID: PMC9271107 DOI: 10.1007/s00234-022-02959-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Purpose
Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic. Methods Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated. Results After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals. Conclusion Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-022-02959-0.
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Freihat O, Zoltán T, Pinter T, Kedves A, Sipos D, Repa I, Kovács Á, Zsolt C. Correlation between Tissue Cellularity and Metabolism Represented by Diffusion-Weighted Imaging (DWI) and 18F-FDG PET/MRI in Head and Neck Cancer (HNC). Cancers (Basel) 2022; 14:cancers14030847. [PMID: 35159115 PMCID: PMC8833888 DOI: 10.3390/cancers14030847] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/02/2022] [Accepted: 02/04/2022] [Indexed: 01/02/2023] Open
Abstract
Simple Summary We report on the correlation between the diffusion-weighted imaging (DWI) and the metabolic volume parameters derived from a PET scan, to determine the correlation between these parameters and the tumor cellularity in head and neck primary tumors. Our findings implied that there was no correlation between the information derived from the DWI and the information derived from the FDG metabolic parameters. Thus, both imaging techniques might play a complementary role in HNC diagnosis and assessment. This is significant because the treatment plan of patients with HNC should be well evaluated by using all the available diagnosis techniques, for a better understanding of how the tumor will react. Abstract Background: This study aimed to assess the association of 18F-Fluorodeoxyglucose positron-emission-tomography (18F-FDG/PET) and DWI imaging parameters from a primary tumor and their correlations with clinicopathological factors. Methods: We retrospectively analyzed primary tumors in 71 patients with proven HNC. Primary tumor radiological parameters: DWI and FDG, as well as pathological characteristics were analyzed. Spearman correlation coefficient was used to assess the correlation between DWI and FDG parameters, ANOVA or Kruskal–Wallis, independent sample t-test, Mann–Whitney test, and multiple regression were performed on the clinicopathological features that may affect the 18F- FDG and apparent-diffusion coefficient (ADC) of the tumor. Results: No significant correlations were observed between DWI and any of the 18F-FDG parameters (p > 0.05). SUVmax correlated with N-stages (p = 0.023), TLG and MTV correlated with T-stages (p = 0.006 and p = 0.001), and ADC correlated with tumor grades (p = 0.05). SUVmax was able to differentiate between N+ and N− groups (p = 0.004). Conclusions: Our results revealed a non-significant correlation between the FDG-PET and ADC-MR parameters. FDG-PET-based glucose metabolic and DWI-MR-derived cellularity data may represent different biological aspects of HNC.
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Affiliation(s)
- Omar Freihat
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary;
- Correspondence: (O.F.); (Á.K.); Tel.: +36-52-411-600 (Á.K.)
| | - Tóth Zoltán
- Doctoral School of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (T.Z.); (A.K.); (I.R.); (C.Z.)
- MEDICOPUS Healthcare Provider and Public Nonprofit Ltd., Somogy County Moritz Kaposi Teaching Hospital, 7400 Kaposvár, Hungary
| | - Tamas Pinter
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, “Moritz Kaposi” Teaching Hospital, 7400 Kaposvár, Hungary;
| | - András Kedves
- Doctoral School of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (T.Z.); (A.K.); (I.R.); (C.Z.)
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, “Moritz Kaposi” Teaching Hospital, 7400 Kaposvár, Hungary;
- Institute of Information Technology and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, 7621 Pécs, Hungary
| | - Dávid Sipos
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary;
- Doctoral School of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (T.Z.); (A.K.); (I.R.); (C.Z.)
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, “Moritz Kaposi” Teaching Hospital, 7400 Kaposvár, Hungary;
| | - Imre Repa
- Doctoral School of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (T.Z.); (A.K.); (I.R.); (C.Z.)
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, “Moritz Kaposi” Teaching Hospital, 7400 Kaposvár, Hungary;
| | - Árpád Kovács
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary;
- Doctoral School of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (T.Z.); (A.K.); (I.R.); (C.Z.)
- Department of Oncoradiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
- Correspondence: (O.F.); (Á.K.); Tel.: +36-52-411-600 (Á.K.)
| | - Cselik Zsolt
- Doctoral School of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (T.Z.); (A.K.); (I.R.); (C.Z.)
- Csolnoky Ferenc County Hospital, 8200 Veszprém, Hungary
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