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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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Panyaping T, Tepkidakarn N, Kiatthanabumrung S, Wattanatranon D, Tritanon O. Usefulness of apparent diffusion coefficient values for distinguishing between squamous cell carcinoma and malignant salivary gland tumor of the head and neck. Neuroradiol J 2023; 36:548-554. [PMID: 36898679 PMCID: PMC10569202 DOI: 10.1177/19714009231163561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
Abstract
PURPOSE The aim of the study was to assess the usefulness of apparent diffusion coefficient (ADC) values for differentiating between SCC and malignant salivary gland tumors of the head and neck. MATERIALS AND METHODS This retrospective cross-sectional study was conducted on 29 patients with SCCs and 10 patients with malignant salivary gland tumors who underwent pretreatment MRI of the head and neck. The minimum and average ADC values of the tumors were measured, with normalized tumor to spinal cord ADC ratios generated. Comparisons of ADC values and normalized ADC ratios between the two tumor types were performed using the unpaired t-test. RESULTS The minimum and average ADC values and normalized average ADC ratios of SCCs (753.17 ± 214.47 × 10-6 mm2/s, 848.79 ± 250.13 × 10-6 mm2/s, and 0.92 ± 0.25, respectively) were significantly lower than those of malignant salivary gland tumors (1084.90 ± 242.60 × 10-6 mm2/s, 1305.90 ± 270.99 × 10-6 mm2/s, and 1.58 ± 0.31, respectively; all p < 0.001). A cutoff value of 1.31 for the normalized average ADC ratio was used to distinguish between SCCs and malignant salivary gland tumors and achieved an area under the curve of 0.93, sensitivity of 96.6%, specificity of 90%, and accuracy of 94.6%. CONCLUSION ADC value measurement could help differentiate between SCCs and malignant salivary gland tumors.
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Affiliation(s)
- Theeraphol Panyaping
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Ratchathewi, Thailand
| | - Nawanwat Tepkidakarn
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Ratchathewi, Thailand
| | - Sivaporn Kiatthanabumrung
- Department of Otolaryngology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Ratchathewi, Thailand
| | - Duangkamon Wattanatranon
- Department of Anatomical Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Ratchathewi, Thailand
| | - Oranan Tritanon
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Ratchathewi, Thailand
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Cai L, Li X, Wu L, Wang B, Si M, Tao X. A Prognostic Model Generated from an Apparent Diffusion Coefficient Ratio Reliably Predicts the Outcomes of Oral Tongue Squamous Cell Carcinoma. Curr Oncol 2022; 29:9031-9045. [PMID: 36547122 PMCID: PMC9777250 DOI: 10.3390/curroncol29120708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/07/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022] Open
Abstract
This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.
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Affiliation(s)
- Lingling Cai
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Xiaoguang Li
- Department of Oral Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Disease, Shanghai 201999, China
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Lizhong Wu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Bocheng Wang
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Mingjue Si
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
- Correspondence: (M.S.); (X.T.)
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
- Correspondence: (M.S.); (X.T.)
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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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Liu X, Wang Y, Wei J, Li S, Xue C, Deng J, Liu H, Sun Q, Zhang X, Zhou J. Role of diffusion-weighted imaging in differentiating angiomatous meningioma from atypical meningioma. Clin Neurol Neurosurg 2022; 221:107406. [PMID: 35932585 DOI: 10.1016/j.clineuro.2022.107406] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/22/2022] [Accepted: 08/01/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Conventional magnetic resonance imaging (MRI) characteristics of angiomatous meningioma (AM) and atypical meningioma (ATM) sometimes overlap. However, there are significant differences in the treatment and prognosis of the two tumors. The aim of the study was to assess the role of diffusion-weighted imaging (DWI) in differentiating AM from ATM. METHODS Clinical, MRI, and pathological data of 25 patients with AM and 30 patients with ATM were retrospectively analyzed. Main clinical indexes, conventional MRI characteristics, and apparent diffusion coefficient (ADC) values were compared between the two groups, and receiver operating characteristic (ROC) curves were drawn to determine the diagnostic performance of ADC values in distinguishing AM from ATM. RESULTS The minimum ADC value (ADCmin), average ADC value (ADCmean), and relative ADC value (rADC) for AM (908.00 ± 117.00 × 10-6 mm2/s, 921.04 ± 67.09 × 10-6 mm2/s, and 1.15 ± 0.09, respectively) were significantly higher than those for ATM (710.50 ± 79.80 × 10-6 mm2/s, 748.50 ± 67.27 × 10-6 mm2/s, and 0.96 ± 0.09, respectively; all P < 0.05). ROC analysis showed that ADCmin had the best diagnostic performance in distinguishing AM from ATM, with an area under the curve value of 0.977. When using 759.00 × 10-6 mm2/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 90.00 %, 96.00 %, 92.72 %, 96.40 %, and 88.90 %, respectively. CONCLUSIONS DWI plays an important role in differentiating AM from ATM, and ADCmin is the most promising potential parameter that can improve the preoperative diagnostic accuracy of both tumors.
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Affiliation(s)
- Xianwang Liu
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Yuzhu Wang
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
| | - Jinyan Wei
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Shenglin Li
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Caiqiang Xue
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Juan Deng
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Hong Liu
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Qiu Sun
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Xueling Zhang
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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