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Head and NeckSquamous CellCarcinoma: DiagnosticPerformance ofDiffusion-weighted MRImaging for thePrediction ofTreatment Response Ann D. KingSteven Kwok Keung ChowKwok‐Hung Yu2012 год

Head and Neck Squamous Cell Carcinoma: Diagnostic Performance of Diffusion-weighted MR Imaging for the Prediction of Treatment Response
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Аннотация: Purpose To determine the diagnostic performance of diffusion-weighted (DW) imaging for the prediction of treatment failure in primary head and neck squamous cell carcinoma (HNSCC). Materials and Methods The study was approved by the local institutional ethics committee and conducted with informed written consent in patients with primary HNSCC treated with radiation therapy and chemotherapy. DW imaging of the primary tumor was performed before treatment in 37 patients and was repeated within 2 weeks of treatment in 30 patients. Histograms of apparent diffusion coefficients (ADCs) were analyzed, and mean ADC, kurtosis, skewness, and their respective percentage change were correlated for local failure and local control at 2 years by using the Student t test. Univariate and multivariate analyses of the ADC parameters, T stage, and tumor volume were performed by using logistic regression for prediction of local failure. Results Local failure occurred in 16 of 37 (43%) patients and local control occurred in 21 of 37 (57%) patients. Pretreatment ADC parameters showed no correlation with local failure. There was significant intratreatment increase in mean ADC and a decrease in skewness and kurtosis (P < .001, P < .001, P = .024, respectively) for the whole group of patients when compared with those before treatment. During treatment, primary tumors showed a significantly lower increase in percentage change of mean ADC, higher skewness, and higher kurtosis for local failure than for local control (P = .016, .015, and .040, respectively). These ADC parameters also were significant for predicting local failure with use of univariate but not multivariate analysis. Conclusion Early intratreatment DW imaging has the potential to allow prediction of treatment response at the primary site in patients with HNSCC. © RSNA, 2012
Год издания: 2012
Авторы: Ann D. King, Steven Kwok Keung Chow, Kwok‐Hung Yu, Frankie Mo, David K.W. Yeung, Jing Yuan, Kunwar S. Bhatia, Alexander C. Vlantis, Anil T. Ahuja
Издательство: Radiological Society of North America
Источник: Radiology
Ключевые слова: MRI in cancer diagnosis, Radiomics and Machine Learning in Medical Imaging, Bone and Joint Diseases
Другие ссылки: Radiology (HTML)
PubMed (HTML)