Literature DB >> 17255264

Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy.

Kuei C Lee1, Bradford A Moffat, Anne F Schott, Rachel Layman, Steven Ellingworth, Rebecca Juliar, Amjad P Khan, Mark Helvie, Charles R Meyer, Thomas L Chenevert, Alnawaz Rehemtulla, Brian D Ross.   

Abstract

PURPOSE: The American Cancer Society estimates that in 2006, 212,920 women will be diagnosed with breast cancer and that 40,970 women will die from the disease. The development of more efficacious chemotherapies has improved outcomes, but the rapid assessment of clinical benefit from these agents remains challenging. In breast cancer patients receiving neoadjuvant chemotherapy, treatment response is traditionally assessed by physical examination and volumetric-based measurements, which are subjective and require macroscopic changes in tumor morphology. In this study, we evaluate the feasibility of using diffusion magnetic resonance imaging (MRI) as a reliable and quantitative measure for the early assessment of response in a breast cancer model. EXPERIMENTAL
DESIGN: Mice implanted with human breast cancer (MX-1) were treated with cyclophosphamide and evaluated using diffusion MRI and growth kinetics. Histologic analyses using terminal nucleotidyl transferase-mediated nick end labeling and H&E were done on tumor samples for correlation with imaging results.
RESULTS: Cyclophosphamide treatment resulted in a significant reduction in tumor volumes compared with controls. The mean apparent diffusion change for treated tumors at days 4 and 7 posttreatment was 44 +/- 5% and 94 +/- 7%, respectively, which was statistically greater (P < 0.05) than the control tumors at the same time intervals. The median time-to-progression for control and treated groups was 11 and 32 days, respectively (P < 0.05).
CONCLUSION: Diffusion MRI was shown to detect early changes in the tumor microenvironment, which correlated with standard measures of tumor response as well as overall outcome. Moreover, these findings show the feasibility of using diffusion MRI for assessing treatment response of a breast tumor model in a neoadjuvant setting.

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Year:  2007        PMID: 17255264     DOI: 10.1158/1078-0432.CCR-06-1888

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  36 in total

Review 1.  Applications of molecular imaging.

Authors:  Craig J Galbán; Stefanie Galbán; Marcian E Van Dort; Gary D Luker; Mahaveer S Bhojani; Alnawaz Rehemtulla; Brian D Ross
Journal:  Prog Mol Biol Transl Sci       Date:  2010       Impact factor: 3.622

2.  Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma.

Authors:  Guido H Jajamovich; Chandni R Valiathan; Razvan Cristescu; Sangeetha Somayajula
Journal:  J Neurooncol       Date:  2016-07-08       Impact factor: 4.130

3.  Diffusion-Weighted Imaging for Predicting and Monitoring Primary Central Nervous System Lymphoma Treatment Response.

Authors:  W-Y Huang; J-B Wen; G Wu; B Yin; J-J Li; D-Y Geng
Journal:  AJNR Am J Neuroradiol       Date:  2016-07-07       Impact factor: 3.825

4.  Predictive value of pre-treatment apparent diffusion coefficient (ADC) in radio-chemiotherapy treated head and neck squamous cell carcinoma.

Authors:  Mariangela Lombardi; Teresa Cascone; Elena Guenzi; Alessandro Stecco; Francesco Buemi; Marco Krengli; Alessandro Carriero
Journal:  Radiol Med       Date:  2017-02-10       Impact factor: 3.469

5.  Image registration for quantitative parametric response mapping of cancer treatment response.

Authors:  Jennifer L Boes; Benjamin A Hoff; Nola Hylton; Martin D Pickles; Lindsay W Turnbull; Anne F Schott; Alnawaz Rehemtulla; Ryan Chamberlain; Benjamin Lemasson; Thomas L Chenevert; Craig J Galbán; Charles R Meyer; Brian D Ross
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

6.  Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumour regression grade at histology.

Authors:  Francesco De Cobelli; Francesco Giganti; Elena Orsenigo; Michaela Cellina; Antonio Esposito; Giulia Agostini; Luca Albarello; Elena Mazza; Alessandro Ambrosi; Carlo Socci; Carlo Staudacher; Alessandro Del Maschio
Journal:  Eur Radiol       Date:  2013-04-16       Impact factor: 5.315

7.  Serial diffusion MRI to monitor and model treatment response of the targeted nanotherapy CRLX101.

Authors:  Thomas S C Ng; David Wert; Hargun Sohi; Daniel Procissi; David Colcher; Andrew A Raubitschek; Russell E Jacobs
Journal:  Clin Cancer Res       Date:  2013-03-26       Impact factor: 12.531

8.  Diffusion MRI with Semi-Automated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis.

Authors:  Renu M Stephen; Abhinav K Jha; Denise J Roe; Theodore P Trouard; Jean-Philippe Galons; Matthew A Kupinski; Georgette Frey; Haiyan Cui; Scott Squire; Mark D Pagel; Jeffrey J Rodriguez; Robert J Gillies; Alison T Stopeck
Journal:  Magn Reson Imaging       Date:  2015-08-15       Impact factor: 2.546

9.  Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck.

Authors:  Sungheon Kim; Laurie Loevner; Harry Quon; Eric Sherman; Gregory Weinstein; Alex Kilger; Harish Poptani
Journal:  Clin Cancer Res       Date:  2009-02-01       Impact factor: 12.531

10.  Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy.

Authors:  Hyeon-Man Baek; Jeon-Hor Chen; Ke Nie; Hon J Yu; Shadfar Bahri; Rita S Mehta; Orhan Nalcioglu; Min-Ying Su
Journal:  Radiology       Date:  2009-03-10       Impact factor: 11.105

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