Literature DB >> 15886307

Functional imaging in lung cancer.

Lalitha K Shankar1, Daniel C Sullivan.   

Abstract

Accurate detection of the presence and extent of disease is vital in the management of non-small-cell lung cancer. While computed tomography and magnetic resonance imaging tend to be the routine diagnostic modalities used in the management of lung cancer, there have been significant advances in the field of functional and molecular imaging. In this article, we review the performance of the functional imaging techniques that are currently available for the evaluation of non-small-cell lung cancer. The techniques range from evaluation of glucose metabolism in tumors with fluorodeoxyglucose, to evaluation of proliferation with fluorothymidine and evaluation of tumor hypoxia with agents such as fluoromisonidazole. Magnetic resonance imaging with an emphasis on dynamic contrast enhancement of tumors as well as detecting of malignant lymph nodes with targeted contrast agents is discussed. Emerging technologies such as lung imaging fluorescence endoscopy are considered. The role of functional imaging in planning, predicting response to, and evaluating effects of, various therapies is explored.

Entities:  

Mesh:

Year:  2005        PMID: 15886307     DOI: 10.1200/JCO.2005.08.854

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  5 in total

Review 1.  Imaging of lung cancer in the era of molecular medicine.

Authors:  Mizuki Nishino; David M Jackman; Hiroto Hatabu; Pasi A Jänne; Bruce E Johnson; Annick D Van den Abbeele
Journal:  Acad Radiol       Date:  2011-01-28       Impact factor: 3.173

2.  Detection of small pulmonary nodules in high-field MR at 3 T: evaluation of different pulse sequences using porcine lung explants.

Authors:  M Regier; S Kandel; M G Kaul; B Hoffmann; H Ittrich; P M Bansmann; J Kemper; C Nolte-Ernsting; M Heller; G Adam; J Biederer
Journal:  Eur Radiol       Date:  2006-09-30       Impact factor: 5.315

3.  Automated tracking of quantitative assessments of tumor burden in clinical trials.

Authors:  Daniel L Rubin; Debra Willrett; Martin J O'Connor; Cleber Hage; Camille Kurtz; Dilvan A Moreira
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

4.  Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory.

Authors:  Walker H Land; Dan Margolis; Ronald Gottlieb; Elizabeth A Krupinski; Jack Y Yang
Journal:  BMC Genomics       Date:  2010-12-01       Impact factor: 3.969

5.  ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.

Authors:  Daniel L Rubin; Mete Ugur Akdogan; Cavit Altindag; Emel Alkim
Journal:  Tomography       Date:  2019-03
  5 in total

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