کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8460009 1548913 2018 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Whole Tumor Histogram-profiling of Diffusion-Weighted Magnetic Resonance Images Reflects Tumorbiological Features of Primary Central Nervous System Lymphoma
ترجمه فارسی عنوان
بررسی هیستوگرام کل تومور تصاویری از رزونانس مغناطیسی با توزیع وزن نشان دهنده ویژگی های توموری در سیستم لنفوم سیستم عصبی مرکزی
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی تحقیقات سرطان
چکیده انگلیسی
PURPOSE: Diffusion weighted imaging (DWI) quantifies motion of hydrogen nuclei in biological tissues and hereby has been used to assess the underlying tissue microarchitecture. Histogram-profiling of DWI provides more detailed information on diffusion characteristics of a lesion than the standardly calculated values of the apparent diffusion coefficient (ADC)-minimum, mean and maximum. Hence, the aim of our study was to investigate, which parameters of histogram-profiling of DWI in primary central nervous system lymphoma can be used to specifically predict features like cellular density, chromatin content and proliferative activity. PROCEDURES: Pre-treatment ADC maps of 21 PCNSL patients (8 female, 13 male, 28-89 years) from a 1.5T system were used for Matlab-based histogram profiling. Results of histopathology (H&E staining) and immunohistochemistry (Ki-67 expression) were quantified. Correlations between histogram-profiling parameters and neuropathologic examination were calculated using SPSS 23.0. RESULTS: The lower percentiles (p10 and p25) showed significant correlations with structural parameters of the neuropathologic examination (cellular density, chromatin content). The highest percentile, p90, correlated significantly with Ki-67 expression, resembling proliferative activity. Kurtosis of the ADC histogram correlated significantly with cellular density. CONCLUSIONS: Histogram-profiling of DWI in PCNSL provides a comprehensible set of parameters, which reflect distinct tumor-architectural and tumor-biological features, and hence, are promising biomarkers for treatment response and prognosis.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Translational Oncology - Volume 11, Issue 2, April 2018, Pages 504-510
نویسندگان
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