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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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Fujimoto K, Shiinoki T, Kawazoe Y, Yuasa Y, Mukaidani W, Manabe Y, Kajima M, Tanaka H. Biomechanical imaging biomarker during chemoradiotherapy predicts treatment response in head and neck squamous cell carcinoma. Phys Med Biol 2024; 69:055033. [PMID: 38359451 DOI: 10.1088/1361-6560/ad29b9] [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: 06/23/2023] [Accepted: 02/15/2024] [Indexed: 02/17/2024]
Abstract
Objective. For response-adapted adaptive radiotherapy (R-ART), promising biomarkers are needed to predict post-radiotherapy (post-RT) responses using routine clinical information obtained during RT. In this study, a patient-specific biomechanical model (BM) of the head and neck squamous cell carcinoma (HNSCC) was proposed using the pre-RT maximum standardized uptake value (SUVmax) of18F-fluorodeoxyglucose (FDG) and tumor structural changes during RT as evaluated using computed tomography (CT). In addition, we evaluated the predictive performance of BM-driven imaging biomarkers for the treatment response of patients with HNSCC who underwent concurrent chemoradiotherapy (CCRT).Approach. Patients with histologically confirmed HNSCC treated with definitive CCRT were enrolled in this study. All patients underwent CT two times as follows: before the start of RT (pre-RT) and 3 weeks after the start of RT (mid-RT). Among these patients, 67 patients who underwent positron emission tomography/CT during the pre-RT period were included in the final analysis. The locoregional control (LC), progression-free survival (PFS), and overall survival (OS) prediction performances of whole tumor stress change (TS) between pre- and mid-RT computed using BM were assessed using univariate, multivariate, and Kaplan-Meier survival curve analyses, respectively. Furthermore, performance was compared with the pre and post-RT SUVmax, tumor volume reduction rate (TVRR) during RT, and other clinical prognostic factors.Main results. For both univariate, multivariate, and survival curve analyses, the significant prognostic factors were as follows (p< 0.05): TS and TVRR for LC; TS and pre-RT FDG-SUVmaxfor PFS; and TS only for OS. In addition, for 2 year LC, PFS, and OS prediction, TS showed a comparable predictive performance to post-RT FDG-SUVmax.Significance. BM-driven TS is an effective prognostic factor for tumor treatment response after CCRT. The proposed method can be a feasible functional imaging biomarker that can be acquired during RT using only routine clinical data and may provide useful information for decision-making during R-ART.
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Affiliation(s)
- Koya Fujimoto
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Takehiro Shiinoki
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yusuke Kawazoe
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
- Department of Radiological Technology, Yamaguchi University Hospital, Ube, Japan
| | - Yuki Yuasa
- Department of Radiological Technology, Yamaguchi University Hospital, Ube, Japan
| | - Wataru Mukaidani
- Department of Radiological Technology, Yamaguchi University Hospital, Ube, Japan
| | - Yuki Manabe
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Miki Kajima
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Hidekazu Tanaka
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
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Trada Y, Keall P, Jameson M, Moses D, Lin P, Chlap P, Holloway L, Min M, Forstner D, Fowler A, Lee MT. Changes in serial multiparametric MRI and FDG-PET/CT functional imaging during radiation therapy can predict treatment response in patients with head and neck cancer. Eur Radiol 2023; 33:8788-8799. [PMID: 37405500 PMCID: PMC10667402 DOI: 10.1007/s00330-023-09843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 07/06/2023]
Abstract
OBJECTIVES To test if tumour changes measured using combination of diffusion-weighted imaging (DWI) MRI and FDG-PET/CT performed serially during radiotherapy (RT) in mucosal head and neck carcinoma can predict treatment response. METHODS Fifty-five patients from two prospective imaging biomarker studies were analysed. FDG-PET/CT was performed at baseline, during RT (week 3), and post RT (3 months). DWI was performed at baseline, during RT (weeks 2, 3, 5, 6), and post RT (1 and 3 months). The ADCmean from DWI and FDG-PET parameters SUVmax, SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were measured. Absolute and relative change (%∆) in DWI and PET parameters were correlated to 1-year local recurrence. Patients were categorised into favourable, mixed, and unfavourable imaging response using optimal cut-off (OC) values of DWI and FDG-PET parameters and correlated to local control. RESULTS The 1-year local, regional, and distant recurrence rates were 18.2% (10/55), 7.3% (4/55), and 12.7% (7/55), respectively. ∆Week 3 ADCmean (AUC 0.825, p = 0.003; OC ∆ > 24.4%) and ∆MTV (AUC 0.833, p = 0.001; OC ∆ > 50.4%) were the best predictors of local recurrence. Week 3 was the optimal time point for assessing DWI imaging response. Using a combination of ∆ADCmean and ∆MTV improved the strength of correlation to local recurrence (p ≤ 0.001). In patients who underwent both week 3 MRI and FDG-PET/CT, significant differences in local recurrence rates were seen between patients with favourable (0%), mixed (17%), and unfavourable (78%) combined imaging response. CONCLUSIONS Changes in mid-treatment DWI and FDG-PET/CT imaging can predict treatment response and could be utilised in the design of future adaptive clinical trials. CLINICAL RELEVANCE STATEMENT Our study shows the complementary information provided by two functional imaging modalities for mid-treatment response prediction in patients with head and neck cancer. KEY POINTS •FDG-PET/CT and DWI MRI changes in tumour during radiotherapy in head and neck cancer can predict treatment response. •Combination of FDG-PET/CT and DWI parameters improved correlation to clinical outcome. •Week 3 was the optimal time point for DWI MRI imaging response assessment.
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Affiliation(s)
- Yuvnik Trada
- Department of Radiation Oncology, Calvary Mater Newcastle, Edith St, Waratah, NSW, 2298, Australia.
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
| | - Paul Keall
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- ACRF Image X Institute, University of Sydney, Sydney, NSW, Australia
| | - Michael Jameson
- GenesisCare St Vincents Hospital, Sydney, NSW, Australia
- St Vincents Clinical School, Faculty of Medicine, University of Sydney, Sydney, NSW, Australia
| | - Daniel Moses
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
- Department of Medical Imaging, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Peter Lin
- Department of Nuclear Medicine and PET, Liverpool Hospital, Liverpool, NSW, Australia
- School of Medicine, Western Sydney University, Sydney, NSW, Australia
| | - Phillip Chlap
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
- South Western Clinical School, School of Medicine, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Lois Holloway
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
- South Western Clinical School, School of Medicine, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Myo Min
- University of Sunshine Coast, Birtinya, QLD, Australia
- Sunshine Coast University Hospital, Sunshine Coast, QLD, Australia
- Griffith University, Sunshine Coast, QLD, Australia
| | - Dion Forstner
- GenesisCare St Vincents Hospital, Sydney, NSW, Australia
- St Vincents Clinical School, Faculty of Medicine, University of Sydney, Sydney, NSW, Australia
| | - Allan Fowler
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | - Mark T Lee
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
- South Western Clinical School, School of Medicine, University of New South Wales, Sydney, NSW, Australia
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Ge X, Xu H, Weng S, Zhang Y, Liu L, Wang L, Xing Z, Ba Y, Liu S, Li L, Wang Y, Han X. Systematic analysis of transcriptome signature for improving outcomes in lung adenocarcinoma. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04814-y. [PMID: 37160628 DOI: 10.1007/s00432-023-04814-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/23/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE The updated guidelines highlight gene expression-based multigene panel as a critical tool to assess overall survival (OS) and improve treatment for lung adenocarcinoma (LUAD) patients. Nevertheless, genome-wide expression signatures are still limited in real clinical utility because of insufficient data utilization, a lack of critical validation, and inapposite machine learning algorithms. METHODS 2330 primary LUAD samples were enrolled from 11 independent cohorts. Seventy-six algorithm combinations based on ten machine learning algorithms were applied. A total of 108 published gene expression signatures were collected. Multiple pharmacogenomics databases and resources were utilized to identify precision therapeutic drugs. RESULTS We comprehensively developed a robust machine learning-derived genome-wide expression signature (RGS) according to stably OS-associated RNAs (OSRs). RGS was an independent risk element and remained robust and reproducible power by comparing it with general clinical parameters, molecular characteristics, and 108 published signatures. RGS-based stratification possessed different biological behaviors, molecular mechanisms, and immune microenvironment patterns. Integrating multiple databases and previous studies, we identified that alisertib was sensitive to the high-risk group, and RITA was sensitive to the low-risk group. CONCLUSION Our study offers an appealing platform to screen dismal prognosis LUAD patients to improve clinical outcomes by optimizing precision therapy.
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Affiliation(s)
- Xiaoyong Ge
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Long Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Libo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhe Xing
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuhao Ba
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shutong Liu
- Department of Clinical Medicine, Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Lifeng Li
- Medical School, Huanghe Science and Technology University, 666 Zi Jing Shan Road, Zhengzhou, 450000, Henan, China
| | - Yuhui Wang
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, No. 7, Kangfu Front Street, Erqi District, Zhengzhou, 450052, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer. Eur Radiol 2023; 33:2850-2860. [PMID: 36460924 DOI: 10.1007/s00330-022-09255-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 09/27/2022] [Accepted: 10/02/2022] [Indexed: 12/05/2022]
Abstract
OBJECTIVES To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. METHODS Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. RESULTS Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). CONCLUSION The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. KEY POINTS • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.
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Boot PA, Mes SW, de Bloeme CM, Martens RM, Leemans CR, Boellaard R, van de Wiel MA, de Graaf P. Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma. Oral Oncol 2023; 137:106307. [PMID: 36657208 DOI: 10.1016/j.oraloncology.2023.106307] [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: 10/22/2022] [Revised: 12/28/2022] [Accepted: 01/08/2023] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics. MATERIALS AND METHODS Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis. RESULTS Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72). CONCLUSION Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics.
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Affiliation(s)
- Paulien A Boot
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Steven W Mes
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands; Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Otolaryngology - Head and Neck Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Christiaan M de Bloeme
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Roland M Martens
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - C René Leemans
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands; Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Otolaryngology - Head and Neck Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Mark A van de Wiel
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Epidemiology and Biostatistics, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Pim de Graaf
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands.
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Current Role of Delta Radiomics in Head and Neck Oncology. Int J Mol Sci 2023; 24:ijms24032214. [PMID: 36768535 PMCID: PMC9916410 DOI: 10.3390/ijms24032214] [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: 11/27/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
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Bao D, Zhao Y, Wu W, Zhong H, Yuan M, Li L, Lin M, Zhao X, Luo D. Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy. Insights Imaging 2022; 13:197. [PMID: 36528686 PMCID: PMC9759610 DOI: 10.1186/s13244-022-01338-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/20/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND This study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). RESULTS Pretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis. The final best prediction model selected by backward stepwise elimination with Akaike information criteria as the stopping rule included kurtosis, maximum energy, range, and total energy. A Rad-score was established by combining the four variables, and it provided areas under the curve (AUCs) of 0.95 (95% confidence interval [CI] 0.91-0.98) and 0.89 (95% CI 0.81-0.97) in the training and validation cohorts, respectively. The combined model, integrating the Rad-score with the T stage (p = 0.02), showed a favorable prediction performance in the training and validation cohorts (AUC = 0.96 and 0.87, respectively). The calibration curves showed a good agreement between the predicted and actual RTLI occurrences. CONCLUSIONS Pretreatment histogram analysis of ADC maps and their combination with the T stage showed a satisfactory ability to predict RTLI in NPC after IMRT.
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Affiliation(s)
- Dan Bao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Yanfeng Zhao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Wenli Wu
- Medical Imaging Center, Liaocheng Tumor Hospital, Shandong, 252000 China
| | - Hongxia Zhong
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Meng Yuan
- grid.506261.60000 0001 0706 7839Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Lin Li
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Meng Lin
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Xinming Zhao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Dehong Luo
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China ,grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116 China
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Quantitative Diffusion-Weighted Imaging Analyses to Predict Response to Neoadjuvant Immunotherapy in Patients with Locally Advanced Head and Neck Carcinoma. Cancers (Basel) 2022; 14:cancers14246235. [PMID: 36551718 PMCID: PMC9776484 DOI: 10.3390/cancers14246235] [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: 10/14/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Neoadjuvant immune checkpoint blockade (ICB) prior to surgery may induce early pathological responses in head and neck squamous cell carcinoma (HNSCC) patients. Routine imaging parameters fail to diagnose these responses early on. Magnetic resonance (MR) diffusion-weighted imaging (DWI) has proven to be useful for detecting HNSCC tumor mass after (chemo)radiation therapy. METHODS 32 patients with stage II-IV, resectable HNSCC, treated at a phase Ib/IIa IMCISION trial (NCT03003637), were retrospectively analyzed using MR-imaging before and after two doses of single agent nivolumab (anti-PD-1) (n = 6) or nivolumab with ipilimumab (anti-CTLA-4) ICB (n = 26). The primary tumors were delineated pre- and post-treatment. A total of 32 features were derived from the delineation and correlated with the tumor regression percentage in the surgical specimen. RESULTS MR-DWI data was available for 24 of 32 patients. Smaller baseline tumor diameter (p = 0.01-0.04) and higher sphericity (p = 0.03) were predictive of having a good pathological response to ICB. Post-treatment skewness and the change in skewness between MRIs were negatively correlated with the tumor's regression (p = 0.04, p = 0.02). CONCLUSION Pre-treatment DWI tumor diameter and sphericity may be quantitative biomarkers for the prediction of an early pathological response to ICB. Furthermore, our data indicate that ADC skewness could be a marker for individual response evaluation.
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Prospective Investigation of 18FDG-PET/MRI with Intravoxel Incoherent Motion Diffusion-Weighted Imaging to Assess Survival in Patients with Oropharyngeal or Hypopharyngeal Carcinoma. Cancers (Basel) 2022; 14:cancers14246104. [PMID: 36551590 PMCID: PMC9775681 DOI: 10.3390/cancers14246104] [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: 10/23/2022] [Revised: 12/04/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
To prospectively investigate the prognostic value of 18F-FDG PET/MRI in patients with oropharyngeal or hypopharyngeal squamous cell carcinomas (OHSCC) treated by chemoradiotherapy. The study cohort consisted of patients with OHSCC who had undergone integrated PET/MRI prior to chemoradiotherapy or radiotherapy. Imaging parameters derived from intravoxel incoherent motion (IVIM), dynamic contrast-enhanced MRI (DCE-MRI), and 18F-FDG PET were analyzed in relation to overall survival (OS) and recurrence-free survival (RFS). In multivariable analysis, T classification (p < 0.001), metabolic tumor volume (p = 0.013), and pseudo-diffusion coefficient (p = 0.008) were identified as independent risk factors for OS. The volume transfer rate constant (p = 0.015), initial area under the curve (p = 0.043), T classification (p = 0.018), and N classification (p = 0.018) were significant predictors for RFS. The Harrell’s c-indices of OS and RFS obtained from prognostic models incorporating clinical and PET/MRI predictors were significantly higher than those derived from the traditional TNM staging system (p = 0.001). The combination of clinical risk factors with functional parameters derived from IVIM and DCE-MRI plus metabolic PET parameters derived from 18F-FDG PET in integrated PET/MRI outperformed the information provided by traditional TNM staging in predicting the survival of patients with OHSCC.
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Mierzwa ML, Aryal M, Lee C, Schipper M, VanTil M, Rivera KM, Swiecicki PL, Casper KA, Malloy KM, Spector ME, Shuman AG, Chinn SB, Prince ME, Stucken CL, Rosko AJ, Lawrence TS, Brenner JC, Rosen B, Schonewolf CA, Shah J, Eisbruch A, Worden FP, Cao Y. Randomized Phase II Study of Physiologic MRI-Directed Adaptive Radiation Boost in Poor Prognosis Head and Neck Cancer. Clin Cancer Res 2022; 28:5049-5057. [PMID: 36107219 PMCID: PMC9773159 DOI: 10.1158/1078-0432.ccr-22-1522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/06/2022] [Accepted: 09/13/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE We conducted a randomized phase II multicenter clinical trial to test the hypothesis that physiologic MRI-based radiotherapy (RT) dose escalation would improve the outcome of patients with poor prognosis head and neck cancer. PATIENTS AND METHODS MRI was acquired at baseline and at RT fraction 10 to create low blood volume/apparent diffusion coefficient maps for RT boost subvolume definition in gross tumor volume. Patients were randomized to receive 70 Gy (standard RT) or 80 Gy to the boost subvolume (RT boost) with concurrent weekly platinum. The primary endpoint was disease-free survival (DFS) with significance defined at a one-sided 0.1 level, and secondary endpoints included locoregional failure (LRF), overall survival (OS), comparison of adverse events and patient reported outcomes (PRO). RESULTS Among 81 randomized patients, neither the primary endpoint of DFS (HR = 0.849, P = 0.31) nor OS (HR = 1.19, P = 0.66) was significantly improved in the RT boost arm. However, the incidence of LRF was significantly improved with the addition of the RT boost (HR = 0.43, P = 0.047). Two-year estimates [90% confidence interval (CI)] of the cumulative incidence of LRF were 40% (27%-53%) in the standard RT arm and 18% (10%-31%) in the RT boost arm. Two-year estimates (90% CI) for DFS were 48% (34%-60%) in the standard RT arm and 57% (43%-69%) in the RT boost arm. There were no significant differences in toxicity or longitudinal differences seen in EORTC QLQ30/HN35 subscales between treatment arms in linear mixed-effects models. CONCLUSIONS Physiologic MRI-based RT boost decreased LRF without a significant increase in grade 3+ toxicity or longitudinal PRO differences, but did not significantly improve DFS or OS. Additional improvements in systemic therapy are likely necessary to realize improvements in DFS and OS.
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Affiliation(s)
- Michelle L Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Madhava Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Matthew Schipper
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Monica VanTil
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | | | - Paul L. Swiecicki
- Department of Internal Medicine, Medical Oncology, University of Michigan, Ann Arbor, Michigan
| | - Keith A. Casper
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Kelly M. Malloy
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Matthew E. Spector
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Andrew G. Shuman
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Steven B. Chinn
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Mark E.P. Prince
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Chaz L. Stucken
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Andrew J. Rosko
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | | | - J Chad Brenner
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Jennifer Shah
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Avraham Eisbruch
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Francis P. Worden
- Department of Internal Medicine, Medical Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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Prognostic Value of 18F-Fluorodeoxyglucose–Positron Emission Tomography/Magnetic Resonance Imaging in Patients With Hypopharyngeal Squamous Cell Carcinoma. J Comput Assist Tomogr 2022; 46:968-977. [DOI: 10.1097/rct.0000000000001365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Shen J, Zhang W, Zhu JJ, Xue L, Yuan M, Xu H, Xu XQ, Yu TF, Wu FY. Multiparametric Magnetic Resonance Imaging for Assessing Thymic Epithelial Tumors: Correlation With Pathological Subtypes and Clinical Stages. J Magn Reson Imaging 2022; 56:1487-1496. [PMID: 35417074 DOI: 10.1002/jmri.28198] [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: 01/23/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND World Health Organization classification and Masaoka-Koga stage are widely used for thymic epithelial tumors (TETs). Reduced field-of-view (rFOV) diffusion-weighed imaging (DWI) proved to improve the image quality. Dynamic contrast-enhanced (DCE) MRI was commonly used in evaluating tumors. PURPOSE To investigate the value of multiparametric MRI in evaluating TETs. STUDY TYPE Retrospective. SUBJECTS Eighty-seven participants including 38 low risk (52.08 ± 14.19 years), 30 high risk (52.40 ± 11.35 years), and 19 thymic carcinoma patients (59.76 ± 10.78 years). FIELD STRENGTH/SEQUENCE A 3 T, turbo spin echo imaging, echo planar imaging, volumetric interpolated breath-hold examination with radial acquisition trajectory. ASSESSMENT DCE-MRI and apparent diffusion coefficient (ADC) variables were compared. Diagnostic performances of single significant factor and combined model were compared. STATISTICAL TESTS Parameters were compared using one-way ANOVA or independent-samples t test. Logistic regression was employed to investigate the combined model. Receiver operating curves (ROC) and DeLong's test were used to compare the diagnostic efficiency. RESULTS ADC, Ktrans , and kep values were significantly different among low-risk, high-risk and carcinoma group (ADC, 1.279 ± 0.345 × 10-3 mm2 /sec, 0.978 ± 0.260 × 10-3 mm2 /sec, 0.661 ± 0.134 × 10-3 mm2 /sec; Ktrans 0.167 ± 0.071 min-1 , 0.254 ± 0.136 min-1 , 0.393 ± 0.110 min-1 ; kep 0.345 ± 0.113 min-1 , 0.560 ± 0.269 min-1 , 0.872 ± 0.149 min-1 ). They were significantly different for early stage and advanced stage (ADC, 1.270 ± 0.356 × 10-3 mm2 /sec vs. 0.845 ± 0.251 × 10-3 mm2 /sec; Ktrans 0.179 ± 0.092 min-1 vs. 0.304 ± 0.142 min-1 ; kep 0.370 ± 0.181 min-1 vs. 0.674 ± 0.362 min-1 ). The combination of them had highest diagnostic efficiency for WHO classification (AUC, 0.925; sensitivity, 83.7%; specificity, 89.5%), clinical stage (AUC, 0.879; sensitivity, 80.9%; specificity, 82.5%). DATA CONCLUSION Multiparametric MRI model may be useful for discriminating WHO classification and clinical stage of TETs. EVIDENCE LEVEL 4 TECHNICAL EFFICIENCY: Stage 2.
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Affiliation(s)
- Jie Shen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jia-Jia Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xue
- Department of Thoracic surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tong-Fu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Jin A, Lin X, Yin X, Cui Y, Ma L. Prognostic value of MTV and TLG of 18 F-FDG PET in patients with head and neck squamous cell carcinoma: A meta-analysis. Medicine (Baltimore) 2022; 101:e30798. [PMID: 36181127 PMCID: PMC9524907 DOI: 10.1097/md.0000000000030798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The current systematic review and meta-analysis explored the value of metabolic tumor volume (MTV) as well as total lesion glycolysis (TLG) in predicting the prognosis of head and neck squamous cell carcinoma (HNSCC) using 18 F-FDG PET parameters. METHODS This work identified relevant studies in the English language by searching several electronic databases, like Cochrane Library, EMBASE, and PubMed. In addition, pooled hazard ratios (HRs) were also calculated to analyze whether MTV and TLG were significant in predicting prognosis. RESULTS The present study included 15 primary studies involving HNSCC cases. As for the elevated TLG, it attained the pooled HR of 1.85 (95% confidence interval [CI], 1.16-2.94; P = .000; I2 = 78.3%) in predicting overall survival (OS), whereas that for elevated MTV was1.22 (95%CI, 1.09-1.36; P = .000; I2 = 82.4%). Besides, for elevated MTV, it attained the pooled HR of 1.34 (95%CI, 1.15-1.56, P = .000; I2 = 86.0%) in predicting disease-free survival (DFS); while the elevated TLG was related to DFS. Sensitivity analysis confirmed that our results are reliable. As for MTV, the ROC-stratified subgroups for DFS and multivariate analyses-stratified subgroups for OS showed statistically significant differences, with no obvious heterogeneities across different studies. For TLG, other methods-stratified subgroups for OS showed statistically significant differences, with no obvious heterogeneity across different studies. CONCLUSION This work indicated that PET/CT is of predictive significance across HNSCC cases. Although the included articles used different methods and recruited HNSCC cases with high clinical heterogeneity; however, our findings confirmed that an elevated MTV can predict the increased risk of side reactions or even death among HNSCC cases and that an elevated TLG can predict a higher death risk.
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Affiliation(s)
- Aihua Jin
- Department of Clinical Laboratory, Yanbian University Hospital, Yanji, Jilin Province, China
| | - Xing Lin
- Department of Thoracic Surgery, Yanbian University Hospital, Yanji, Jilin Province, China
| | - Xuezhe Yin
- Department of Respiration Medicine, Yanbian University Hospital, Yanji, Jilin Province, China
| | - Yinfeng Cui
- Department of Stomatology, Medical College of Yanbian University, Jilin Province, China
- *Correspondence: Liguang Ma and Yinfeng Cui, Department of College of Yanbian University, Jilin Province 133000, China (e-mail: and )
| | - Liguang Ma
- Department of Stomatology, Medical College of Yanbian University, Jilin Province, China
- *Correspondence: Liguang Ma and Yinfeng Cui, Department of College of Yanbian University, Jilin Province 133000, China (e-mail: and )
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Quantitative parameters derived from 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging can accurately estimate the histologic grade of hypopharyngeal squamous cell carcinoma preoperatively. Neuroradiology 2022; 64:2153-2162. [PMID: 36121469 DOI: 10.1007/s00234-022-03052-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/10/2022] [Indexed: 10/14/2022]
Abstract
PURPOSE Among head and neck cancers, hypopharyngeal squamous cell carcinoma (HSCC) shows the highest malignancy, which is associated with histologic grading. This study was designed to investigate whether quantitative parameters derived from 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) can preoperatively estimate the histologic grade of HSCC. METHODS 18F-FDG PET/MRI of neck was successfully performed in 21 patients with histologically proven HSCC including poorly differentiated group (ten patients) and well-moderately differentiated group (eleven patients). Quantitative parameters derived from FDG-PET, diffusion-weighted imaging (DWI), and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) were calculated based on volume of interest drawn on the tumor and compared between two groups. The efficacy of quantitative parameters for the estimation of histologic grades of HSCC was evaluated. RESULTS There were statistically significant differences in mean value of standard uptake value (SUV), apparent diffusion coefficient (ADC), and Ktrans derived from 18F-FDG PET/MRI of HSCC between two groups (p < 0.05). There was no statistically significant difference in other quantitative parameters derived from 18F-FDG PET/MRI of HSCC between two groups. The area under the curve (AUC) of the combination of SUVmean, ADCmean, and Ktrans in the estimation of histologic grade of HSCC was 0.936 with sensitivity of 90.0% and specificity of 81.8%. CONCLUSION The combination of SUVmean, ADCmean, and Ktrans derived from 18F-FDG PET/MRI can accurately predict the histologic grade of HSCC preoperatively.
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Touska P, Connor S. Imaging of human papilloma virus associated oropharyngeal squamous cell carcinoma and its impact on diagnosis, prognostication, and response assessment. Br J Radiol 2022; 95:20220149. [PMID: 35687667 PMCID: PMC9815738 DOI: 10.1259/bjr.20220149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/22/2022] [Accepted: 06/07/2022] [Indexed: 01/13/2023] Open
Abstract
The clinical behaviour and outcomes of patients with oropharyngeal cancer (OPC) may be dichotomised according to their association with human papilloma virus (HPV) infection. Patients with HPV-associated disease (HPV+OPC) have a distinct demographic profile, clinical phenotype and demonstrate considerably better responses to chemoradiotherapy. This has led to a reappraisal of staging and treatment strategies for HPV+OPC, which are underpinned by radiological data. Structural modalities, such as CT and MRI can provide accurate staging information. These can be combined with ultrasound-guided tissue sampling and functional techniques (such as diffusion-weighted MRI and 18F-fludeoxyglucose positron emission tomography-CT) to monitor response to treatment, derive prognostic information, and to identify individuals who might benefit from intensification or deintensification strategies. Furthermore, advanced MRI techniques, such as intravoxel incoherent motion and perfusion MRI as well as application of artificial intelligence and radiomic techniques, have shown promise in treatment response monitoring and prognostication. The following review will consider the contemporary role and knowledge on imaging in HPV+OPC.
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Affiliation(s)
- Philip Touska
- Department of Radiology, Guy’s and St. Thomas’ NHS Foundation Trust, London, United Kingdom
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Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50th percentile of DPT, the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size.
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Hou W, Xue Y, Qian Y, Pan H, Xu M, Shen Y, Li X, Yu Y. Application of Intravoxel Incoherent Motion Diffusion-Weighted Imaging in Predicting and Monitoring Early Efficacy of Anti-Angiogenic Therapy in the C6 Glioma Rat Model. Front Oncol 2022; 11:842169. [PMID: 35155219 PMCID: PMC8831888 DOI: 10.3389/fonc.2021.842169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 12/30/2022] Open
Abstract
Objective To investigate the feasibility of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in evaluating early effects of anti-angiogenic therapy in the C6 glioma rat model. Methods Twenty-six rats of the C6 glioma model were randomly divided into a treatment group (received bevacizumab) and a control group (physiological saline). IVIM-DWI was performed on days 0, 1, 3, 5, and 7 after anti-angiogenic therapy and tumor growth and IVIM-DWI parameters were dynamically observed. Hematoxylin and eosin, CD34 microvessel density (MVD), proliferation of cell nuclear antigen (PCNA), and Hif-α staining were performed on day 7. One-way ANOVA was used to compare intra-group differences and an independent-samples t-test was used to compare inter-group differences of MRI parameters. Correlations between IVIM-DWI parameters, tumor size, and pathological results were analyzed. Results The relative change in tumor volume (ΔVolume) in the two groups differed significantly on days 5 and 7 (p = 0.038 and p < 0.001). The perfusion-related parameters D*- and f-values decreased in the treatment group and demonstrated significant differences compared with the control group on days 3, 5, and 7 (p = 0.033, p < 0.001, and p < 0.001, respectively). The diffusion-related parameters ADC and D-values increased in the treatment group and were found to be significantly differently different from the control group on days 5 and 7 (both p < 0.001). The initial D-value showed a negative correlation with ΔVolume (γ = −0.744, p < 0.001), whereas the initial D*-value and relative change of D-value had a positive correlation with ΔVolume (γ = 0.718, p < 0.001 and γ = 0.800, p < 0.001, respectively). MVD was strongly positively correlated with D*-value (r = 0.886, p = 0.019), PCNA was negatively correlated with ADC- and D-values (r = −0.848, p = 0.033; and r = −0.928 p = 0.008, respectively), and Hif-1α was strongly negatively correlated with D*-value (r = −0.879, p = 0.010). Conclusion IVIM-DWI was sensitive and accurate in predicting and monitoring the effects of early anti-angiogenesis therapy in a C6 glioma rat model.
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Affiliation(s)
- Weishu Hou
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yangyang Xue
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hongli Pan
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Man Xu
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yujun Shen
- Department of Basic Medical Sciences, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Xiaohu Li
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Xiaohu Li, ; Yongqiang Yu,
| | - Yongqiang Yu
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Xiaohu Li, ; Yongqiang Yu,
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Schouten JPE, Noteboom S, Martens RM, Mes SW, Leemans CR, de Graaf P, Steenwijk MD. Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN. Cancer Imaging 2022; 22:8. [PMID: 35033188 PMCID: PMC8761340 DOI: 10.1186/s40644-022-00445-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.
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Affiliation(s)
- Jens P E Schouten
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Samantha Noteboom
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Steven W Mes
- Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - C René Leemans
- Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands. .,, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.
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Early Response Prediction of Multiparametric Functional MRI and 18F-FDG-PET in Patients with Head and Neck Squamous Cell Carcinoma Treated with (Chemo)Radiation. Cancers (Basel) 2022; 14:cancers14010216. [PMID: 35008380 PMCID: PMC8750157 DOI: 10.3390/cancers14010216] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Patients with locally-advanced head and neck squamous cell carcinoma (HNSCC) have variable responses to (chemo)radiotherapy. A reliable early prediction of outcomes allows for enhancing treatment efficacy and follow-up monitoring. Early tumoral changes can be captured by functional imaging (DWI/IVIM/DCE/18F-FDG-PET-CT) parameters, which allow for the construction of accurate patient-specific prognostic models for locoregional recurrence-free survival, distant metastasis-free survival and overall survival. We also present clinical applicable risk stratification in high/medium/low risks for these patient outcomes. This can enable personalized treatment (adaptation) management early on during treatment, improve counseling and enhance patient-specific post-therapy monitoring. Abstract Background: Patients with locally-advanced head and neck squamous cell carcinoma (HNSCC) have variable responses to (chemo)radiotherapy. A reliable prediction of outcomes allows for enhancing treatment efficacy and follow-up monitoring. Methods: Fifty-seven histopathologically-proven HNSCC patients with curative (chemo)radiotherapy were prospectively included. All patients had an MRI (DW,-IVIM, DCE-MRI) and 18F-FDG-PET/CT before and 10 days after start-treatment (intratreatment). Primary tumor functional imaging parameters were extracted. Univariate and multivariate analysis were performed to construct prognostic models and risk stratification for 2 year locoregional recurrence-free survival (LRFFS), distant metastasis-free survival (DMFS) and overall survival (OS). Model performance was measured by the cross-validated area under the receiver operating characteristic curve (AUC). Results: The best LRFFS model contained the pretreatment imaging parameters ADC_kurtosis, Kep and SUV_peak, and intratreatment imaging parameters change (Δ) Δ-ADC_skewness, Δ-f, Δ-SUV_peak and Δ-total lesion glycolysis (TLG) (AUC = 0.81). Clinical parameters did not enhance LRFFS prediction. The best DMFS model contained pretreatment ADC_kurtosis and SUV_peak (AUC = 0.88). The best OS model contained gender, HPV-status, N-stage, pretreatment ADC_skewness, D, f, metabolic-active tumor volume (MATV), SUV_mean and SUV_peak (AUC = 0.82). Risk stratification in high/medium/low risk was significantly prognostic for LRFFS (p = 0.002), DMFS (p < 0.001) and OS (p = 0.003). Conclusions: Intratreatment functional imaging parameters capture early tumoral changes that only provide prognostic information regarding LRFFS. The best LRFFS model consisted of pretreatment, intratreatment and Δ functional imaging parameters; the DMFS model consisted of only pretreatment functional imaging parameters, and the OS model consisted ofHPV-status, gender and only pretreatment functional imaging parameters. Accurate clinically applicable risk stratification calculators can enable personalized treatment (adaptation) management, early on during treatment, improve counseling and enhance patient-specific post-therapy monitoring.
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21
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MRT und 18F-FDG-PET haben einen hohen komplementären Wert für die Abschätzung der Prognose. ROFO-FORTSCHR RONTG 2021. [DOI: 10.1055/a-1395-2013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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22
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Bos P, van der Hulst HJ, van den Brekel MWM, Schats W, Jasperse B, Beets-Tan RGH, Castelijns JA. Prognostic functional MR imaging parameters in head and neck squamous cell carcinoma: A systematic review. Eur J Radiol 2021; 144:109952. [PMID: 34562743 DOI: 10.1016/j.ejrad.2021.109952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/10/2021] [Accepted: 08/31/2021] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Functional MR imaging has demonstrated potential for predicting treatment response. This systematic review gives an extensive overview of the current level of evidence for pre-treatment MR-based perfusion and diffusion imaging parameters that are prognostic for treatment outcome in head and neck squamous cell carcinoma (HNSCC) (PROSPERO registrationCRD42020210689). MATERIALS AND METHODS According to the PRISMA statements, Medline, Embase and Scopus were queried for articles with a maximum date of October 19th, 2020. Studies investigating the predictive performance of pre-treatment MR-based perfusion and/or diffusion imaging parameters in HNSCC treatment response were included. All prognosticators were extracted from the primary tumor. Risk of bias was assessed using the QUIPS tool. Results were summarized in tables and forest plots. RESULTS 31 unique studies met the inclusion criteria; among them, 11 articles described perfusion (n = 529 patients) and 28 described diffusion (n = 1626 patients) MR-imaging, eight studies were included in both categories. Higher Ktrans and Kep were associated with better treatment response for OS and DFS, respectively. Study findings for Vp and Ve were inconsistent or not significant. High-level controversy was observed between studies examining the MR diffusion parameters mean and median ADC. CONCLUSION For HNSCC patients, the accurate and consistent results of pre-treatment MR-based perfusion parameters Ktrans and Kep are potential for clinical applicability predictive of OS and DFS and treatment decision guidance. Significant heterogeneity in study designs might affect high discrepancy in study results for parameters extracted from diffusion imaging. Furthermore, recommendations for future research were summarized.
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Affiliation(s)
- Paula Bos
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology - University of Maastricht, Maastricht, the Netherlands.
| | - Hedda J van der Hulst
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology - University of Maastricht, Maastricht, the Netherlands
| | - Michiel W M van den Brekel
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center (AUMC), Amsterdam, the Netherlands
| | - Winnie Schats
- Scientific Information Service, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Bas Jasperse
- Department of Radiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology - University of Maastricht, Maastricht, the Netherlands; Department of Regional Health Research, University of Southern Denmark, Denmark
| | - Jonas A Castelijns
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
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23
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Salzillo TC, Taku N, Wahid KA, McDonald BA, Wang J, van Dijk LV, Rigert JM, Mohamed ASR, Wang J, Lai SY, Fuller CD. Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology. Semin Radiat Oncol 2021; 31:371-388. [PMID: 34455992 DOI: 10.1016/j.semradonc.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While there has been an overall decline of tobacco and alcohol-related head and neck cancer in recent decades, there has been an increased incidence of HPV-associated oropharyngeal cancer (OPC). Recent research studies and clinical trials have revealed that the cancer biology and clinical progression of HPV-positive OPC is unique relative to its HPV-negative counterparts. HPV-positive OPC is associated with higher rates of disease control following definitive treatment when compared to HPV-negative OPC. Thus, these conditions should be considered unique diseases with regards to treatment strategies and survival. In order to sufficiently characterize HPV-positive OPC and guide treatment strategies, there has been a considerable effort to diagnose, prognose, and track the treatment response of HPV-associated OPC through advanced imaging research. Furthermore, HPV-positive OPC patients are prime candidates for radiation de-escalation protocols, which will ideally reduce toxicities associated with radiation therapy and has prompted additional imaging research to detect radiation-induced changes in organs at risk. This manuscript reviews the various imaging modalities and current strategies for tackling these challenges as well as provides commentary on the potential successes and suggested improvements for the optimal treatment of these tumors.
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Affiliation(s)
- Travis C Salzillo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Nicolette Taku
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jarey Wang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jillian M Rigert
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
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24
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López F, Mäkitie A, de Bree R, Franchi A, de Graaf P, Hernández-Prera JC, Strojan P, Zidar N, Strojan Fležar M, Rodrigo JP, Rinaldo A, Centeno BA, Ferlito A. Qualitative and Quantitative Diagnosis in Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11091526. [PMID: 34573868 PMCID: PMC8466857 DOI: 10.3390/diagnostics11091526] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/14/2021] [Accepted: 08/20/2021] [Indexed: 12/11/2022] Open
Abstract
The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.
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Affiliation(s)
- Fernando López
- Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo CIBERONC-ISCIII, 33011 Oviedo, Spain
- Correspondence:
| | - Antti Mäkitie
- Department of Otorhinolaryngology–Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland;
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, 3584CX Utrecht, The Netherlands;
| | - Alessandro Franchi
- Department of Translational Research, School of Medicine, University of Pisa, 56124 Pisa, Italy;
| | - Pim de Graaf
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 Amsterdam, The Netherlands;
| | | | - Primoz Strojan
- Department of Radiation Oncology, Institute of Oncology, 1000 Ljubljana, Slovenia;
| | - Nina Zidar
- Department of Head and Neck Pathology, Faculty of Medicine, Institute of Pathology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Margareta Strojan Fležar
- Department of Cytopathology, Faculty of Medicine, Institute of Pathology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Juan P. Rodrigo
- Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo CIBERONC-ISCIII, 33011 Oviedo, Spain
| | | | - Barbara A. Centeno
- Department of Pathology, Moffitt Cancer Center, Tampa, FL 33612, USA; (J.C.H.-P.); (B.A.C.)
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, 35100 Padua, Italy;
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Connor S, Anjari M, Burd C, Guha A, Lei M, Guerrero-Urbano T, Pai I, Bassett P, Goh V. The impact of human papilloma virus status on the prediction of head and neck cancer chemoradiotherapy outcomes using the pre-treatment apparent diffusion coefficient. Br J Radiol 2021; 95:20210333. [PMID: 34111977 PMCID: PMC8822554 DOI: 10.1259/bjr.20210333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Objective: To determine the impact of Human Papilloma Virus (HPV) oropharyngeal cancer (OPC) status on the prediction of head and neck squamous cell cancer (HNSCC) chemoradiotherapy (CRT) outcomes with pre-treatment quantitative diffusion-weighted magnetic resonance imaging (DW-MRI). Methods: Following ethical approval, 65 participants (53 male, age 59.9 ± 7.86) underwent pre-treatment DW-MRI in this prospective cohort observational study. There were 46 HPV OPC and 19 other HNSCC cases with Stage III/IV HNSCC. Regions of interest (ROIs) (volume, largest area, core) at the primary tumour (n = 57) and largest pathological node (n = 59) were placed to analyse ADCmean and ADCmin. Unpaired t-test or Mann–Whitney test evaluated the impact of HPV OPC status and clinical parameters on their prediction of post-CRT 2 year locoregional and disease-free survival (LRFS and DFS). Multivariate logistic regression compared significant variables with 2 year outcomes. Results: On univariate analysis of all participants, the primary tumour area ADCmean was predictive of 2 year LRFS (p = 0.04). However, only the HPV OPC diagnosis (LFRS p = 0.03; DFS p = 0.02) predicted outcomes on multivariate analysis. None of the pre-treatment ADC values were predictive of 2 year DFS in the HPV OPC subgroup (p = 0.21–0.68). Amongst participants without 2 year disease-free survival, HPV-OPC was found to have much lower primary tumour ADCmean values than other HNSCC. Conclusion: Knowledge of HPV OPC status is required in order to determine the impact of the pre-treatment ADC values on post-CRT outcomes in HNSCC. Advances in knowledge: Pre-treatment ADCmean and ADCmin values acquired using different ROI methods are not predictive of 2 year survival outcomes in HPV OPC.
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Affiliation(s)
- Steve Connor
- School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College, London, SE1 7EH, United Kingdom.,Department of Neuroradiology, King's College Hospital, London, SE5 9RS, United Kingdom.,Department of Radiology, Guy's Hospital, 2nd Floor, Tower Wing, Great Maze Pond, London, SE1 9RT, United Kingdom
| | - Mustafa Anjari
- Department of Radiology, Guy's Hospital, 2nd Floor, Tower Wing, Great Maze Pond, London, SE1 9RT, United Kingdom
| | - Christian Burd
- Department of Radiology, Guy's Hospital, 2nd Floor, Tower Wing, Great Maze Pond, London, SE1 9RT, United Kingdom
| | - Amrita Guha
- Department of Radio-diagnosis, Tata Memorial Hospital, Parel, Homi Bhabha National Institute, Mumbai, India
| | - Mary Lei
- Department of Oncology, Guy's Hospital, 2nd Floor, Tower Wing, Great Maze Pond, London, SE1 9RT UK5, United Kingdom
| | - Teresa Guerrero-Urbano
- Department of Oncology, Guy's Hospital, 2nd Floor, Tower Wing, Great Maze Pond, London, SE1 9RT UK5, United Kingdom
| | - Irumee Pai
- School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College, London, SE1 7EH, United Kingdom.,Department of Ear, Nose and Throat Surgery, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Paul Bassett
- Freelance medical statistician, London, United Kingdom
| | - Vicky Goh
- School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College, London, SE1 7EH, United Kingdom.,Department of Radiology, Guy's Hospital, 2nd Floor, Tower Wing, Great Maze Pond, London, SE1 9RT, United Kingdom
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26
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Chodyla M, Demircioglu A, Schaarschmidt BM, Bertram S, Morawitz J, Bauer S, Podleska L, Rischpler C, Forsting M, Herrmann K, Umutlu L, Grueneisen J. Evaluation of the Predictive Potential of 18F-FDG PET and DWI Data Sets for Relevant Prognostic Parameters of Primary Soft-Tissue Sarcomas. Cancers (Basel) 2021; 13:cancers13112753. [PMID: 34206128 PMCID: PMC8199532 DOI: 10.3390/cancers13112753] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 05/25/2021] [Accepted: 05/27/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND To evaluate the potential of simultaneously acquired 18F-FDG PET- and MR-derived quantitative imaging data sets of primary soft-tissue sarcomas for the prediction of neoadjuvant treatment response, the metastatic status and tumor grade. METHODS A total of 52 patients with a high-risk soft-tissue sarcoma underwent a 18F-FDG PET/MR examination within one week before the start of neoadjuvant treatment. For each patient, the maximum tumor size, metabolic activity (SUVs), and diffusion-restriction (ADC values) of the tumor manifestations were determined. A Mann-Whitney-U test was used, and ROC analysis was performed to evaluate the potential to predict histopathological treatment response, the metastatic status or tumor grade. The results from the histopathological analysis served as reference standard. RESULTS Soft-tissue sarcomas with a histopathological treatment response revealed a significantly higher metabolic activity than tumors in the non-responder group. In addition, grade 3 tumors showed a significant higher 18F-FDG uptake than grade 2 tumors. Furthermore, no significant correlation between the different outcome variables and tumor size or calculated ADC-values could be identified. CONCLUSION Measurements of the metabolic activity of primary and untreated soft-tissue sarcomas could non-invasively deliver relevant information that may be used for treatment planning and risk-stratification of high-risk sarcoma patients in a pretherapeutic setting.
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Affiliation(s)
- Michal Chodyla
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (M.C.); (A.D.); (B.M.S.); (M.F.); (L.U.)
| | - Aydin Demircioglu
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (M.C.); (A.D.); (B.M.S.); (M.F.); (L.U.)
| | - Benedikt M. Schaarschmidt
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (M.C.); (A.D.); (B.M.S.); (M.F.); (L.U.)
| | - Stefanie Bertram
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany;
| | - Janna Morawitz
- Department of Diagnostic and Interventional Radiology, University Hospital Dusseldorf, University of Dusseldorf, 40225 Dusseldorf, Germany;
| | - Sebastian Bauer
- Sarcoma Center, Western German Cancer Center, University of Duisburg-Essen, 45147 Essen, Germany;
| | - Lars Podleska
- Sarcoma Surgery Division, Department of General, Visceral and Transplantation Surgery, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany;
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (C.R.); (K.H.)
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (M.C.); (A.D.); (B.M.S.); (M.F.); (L.U.)
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (C.R.); (K.H.)
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (M.C.); (A.D.); (B.M.S.); (M.F.); (L.U.)
| | - Johannes Grueneisen
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany; (M.C.); (A.D.); (B.M.S.); (M.F.); (L.U.)
- Correspondence: ; Tel.: +49-(0)201/723-1501
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