Literature DB >> 21610469

Head and neck cancer as a model for advances in imaging prognosis, early assessment, and posttherapy evaluation.

David M Brizel1.   

Abstract

Novel noninvasive functional imaging methods are necessary to predict therapeutic outcome and thereby improve the ability to properly select patients for treatment with both conventional and targeted therapies, to better evaluate therapeutic effectiveness during the early phases of treatment, and to enhance a priori risk assessment for treatment induced toxicity. Functional metabolic imaging typically involves pretreatment baseline magnetic resonance imaging (MRI) and/or positron emission tomographic (PET) scans and performance of subsequent scans during and/or after treatment. Imaging parameter changes are routinely attributed to the intervening therapy and clinical outcomes subsequently correlated with these changes. The physiologic parameter(s) that best correlate with clinical outcome and the relative utility of MRI versus PET are unknown, however. Furthermore, tumor vascular physiology and metabolic parameters are heterogeneous and dynamic processes. Large daily fluctuations often occur in the absence of treatment. The magnitude of this temporal variability is not established for MRI or for PET. Routine and meaningful clinical application of functional imaging requires understanding and quantification of the intrinsic variability of the underlying biologic processes and a demonstration that treatment-induced changes exceed intrinsic temporal variation.

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Mesh:

Year:  2011        PMID: 21610469     DOI: 10.1097/PPO.0b013e31821e8a09

Source DB:  PubMed          Journal:  Cancer J        ISSN: 1528-9117            Impact factor:   3.360


  3 in total

1.  Management of the Neck in Squamous Cell Carcinoma of the Oral Cavity and Oropharynx: ASCO Clinical Practice Guideline.

Authors:  Shlomo A Koyfman; Nofisat Ismaila; Doug Crook; Anil D'Cruz; Cristina P Rodriguez; David J Sher; Damian Silbermins; Erich M Sturgis; Terance T Tsue; Jared Weiss; Sue S Yom; F Christopher Holsinger
Journal:  J Clin Oncol       Date:  2019-02-27       Impact factor: 44.544

Review 2.  Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology.

Authors:  Muhammad Kashif Rahim; Sung Eun Kim; Hyeongryul So; Hyung Jun Kim; Gi Jeong Cheon; Eun Seong Lee; Keon Wook Kang; Dong Soo Lee
Journal:  Nucl Med Mol Imaging       Date:  2014-01-22

3.  Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application.

Authors:  Chunhao Wang; Chenyang Liu; Yushi Chang; Kyle Lafata; Yunfeng Cui; Jiahan Zhang; Yang Sheng; Yvonne Mowery; David Brizel; Fang-Fang Yin
Journal:  Front Oncol       Date:  2020-08-18       Impact factor: 6.244

  3 in total

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