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Prognostication and response assessment in liver and pancreatic tumors:The new imaging 被引量:10
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作者 Riccardo De Robertis Paolo Tinazzi Martini +9 位作者 Emanuele Demozzi gino puntel Silvia Ortolani Sara Cingarlini Andrea Ruzzenente Alfredo Guglielmi Giampaolo Tortora Claudio Bassi Paolo Pederzoli Mirko D'Onofrio 《World Journal of Gastroenterology》 SCIE CAS 2015年第22期6794-6808,共15页
Diffusion-weighted imaging(DWI), dynamic contrastenhanced magnetic resonance imaging(DCE-MRI) and perfusion computed tomography(CT) are technical improvements of morphologic imaging that can evaluate functional proper... Diffusion-weighted imaging(DWI), dynamic contrastenhanced magnetic resonance imaging(DCE-MRI) and perfusion computed tomography(CT) are technical improvements of morphologic imaging that can evaluate functional properties of hepato-bilio-pancreatic tumors during conventional MRI or CT examinations.Nevertheless, the term "functional imaging" is commonly used to describe molecular imaging techniques, as positron emission tomography(PET)CT/MRI, which still represent the most widely used methods for the evaluation of functional properties of solid neoplasms; unlike PET or single photon emission computed tomography, functional imaging techniques applied to conventional MRI/CT examinations do not require the administration of radiolabeled drugs or specific equipments. Moreover, DWI and DCE-MRI can be performed during the same session, thus providing a comprehensive "one-step" morphological and functional evaluation of hepato-bilio-pancreatic tumors. Literature data reveal that functional imaging techniques could be proposed for the evaluation of these tumors before treatment, given that they may improve staging and predict prognosis or clinical outcome. Microscopic changes within neoplastic tissues induced by treatments can be detected and quantified with functional imaging,therefore these techniques could be used also for posttreatment assessment, even at an early stage. The aim of this editorial is to describe possible applications of new functional imaging techniques apart frommolecular imaging to hepatic and pancreatic tumors through a review of up-to-date literature data, with a particular emphasis on pathological correlations,prognostic stratification and post-treatment monitoring. 展开更多
关键词 Diffusion magnetic resonance IMAGING PERFUSION IMAGING HEPATOCELLULAR carcinoma Liverneoplasms PANCREATIC NEOPLASMS
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Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis 被引量:5
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作者 Simone Perandini Gian Alberto Soardi +9 位作者 Massimiliano Motton Raffaele Augelli Chiara Dallaserra gino puntel Arianna Rossi Giuseppe Sala Manuel Signorini Laura Spezia Federico Zamboni Stefania Montemezzi 《World Journal of Radiology》 CAS 2016年第8期729-734,共6页
The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomogr... The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization. 展开更多
关键词 SOLITARY pulmonary NODULE COMPUTER-AIDED diagnosis Lung NEOPLASMS MULTIDETECTOR COMPUTED tomography Bayesian prediction
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