Literature DB >> 18384182

Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.

Uma Sharma1, Karikanni Kalathil A Danishad, Vurthaluru Seenu, Naranamangalam R Jagannathan.   

Abstract

Measurements of tumor apparent diffusion coefficient (ADC), volume and diameter in assessing the response of patients with locally advanced breast cancer (LABC) (n = 56) undergoing neoadjuvant chemotherapy (NACT) at four time periods (before treatment and after three cycles of NACT) were carried out at 1.5 T using diffusion-weighted imaging (DWI) and MRI. Ten benign tumors and 15 controls were also investigated. The MR tumor response was compared with the clinical response. Mean ADC before treatment of malignant breast tissue was significantly lower than that of controls, disease-free contralateral tissue of the patients, and benign lesions, and gradually increased during the course of NACT. Analysis of the percentage change in ADC, volume and diameter after each cycle of NACT between clinical responders and non-responders showed that the change in ADC after the first cycle was statistically significant compared with volume and diameter, indicating its potential in assessing early response. After the third cycle, the sensitivity for differentiating responders from non-responders was 89% for volume and diameter and 68% for ADC, and the respective specificities were 50%, 70% and 100%. A sensitivity of 84% (specificity of 60% with an accuracy of 76%) was achieved when all three variables were taken together to predict the response. A cut-off value of ADC was also calculated using receiver operator characteristics analysis to discriminate between normal, benign and malignant breast tissue. Similarly, a cut-off value for ADC, volume and diameter was obtained after the second and third cycles of NACT to predict tumor response. The results show that ADC is more useful for predicting early tumor response to NACT than morphological variables, suggesting its potential in effective treatment management.

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Year:  2009        PMID: 18384182     DOI: 10.1002/nbm.1245

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  131 in total

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Journal:  Invest Radiol       Date:  2015-04       Impact factor: 6.016

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Journal:  Eur J Nucl Med Mol Imaging       Date:  2011-11-10       Impact factor: 9.236

3.  Squamous cell carcinoma of the head and neck: diffusion-weighted MR imaging for prediction and monitoring of treatment response.

Authors:  Ann D King; Frankie K F Mo; Kwok-Hung Yu; David K W Yeung; Hua Zhou; Kunwar S Bhatia; Gary M K Tse; Alexander C Vlantis; Jeffrey K T Wong; Anil T Ahuja
Journal:  Eur Radiol       Date:  2010-03-23       Impact factor: 5.315

4.  Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.

Authors:  Nkiruka C Atuegwu; Lori R Arlinghaus; Xia Li; E Brian Welch; Bapsi A Chakravarthy; John C Gore; Thomas E Yankeelov
Journal:  Magn Reson Med       Date:  2011-09-28       Impact factor: 4.668

5.  Diffusion weighted imaging in predicting progression free survival in patients with squamous cell carcinomas of the head and neck treated with induction chemotherapy.

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Review 6.  Imaging-based tumor treatment response evaluation: review of conventional, new, and emerging concepts.

Authors:  Hee Kang; Ho Yun Lee; Kyung Soo Lee; Jae-Hun Kim
Journal:  Korean J Radiol       Date:  2012-06-18       Impact factor: 3.500

7.  [Therapy monitoring of neoadjuvant therapy with MRI. RECIST and functional imaging].

Authors:  S Grandl; M Ingrisch; K Hellerhoff
Journal:  Radiologe       Date:  2014-03       Impact factor: 0.635

Review 8.  Whole-body diffusion-weighted and proton imaging: a review of this emerging technology for monitoring metastatic cancer.

Authors:  Michael A Jacobs; Li Pan; Katarzyna J Macura
Journal:  Semin Roentgenol       Date:  2009-04       Impact factor: 0.800

9.  Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Xia Li; Hakmook Kang; Lori R Arlinghaus; Richard G Abramson; A Bapsi Chakravarthy; Vandana G Abramson; Jaime Farley; Melinda Sanders; Thomas E Yankeelov
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

Review 10.  Diffusion-weighted breast MRI: Clinical applications and emerging techniques.

Authors:  Savannah C Partridge; Noam Nissan; Habib Rahbar; Averi E Kitsch; Eric E Sigmund
Journal:  J Magn Reson Imaging       Date:  2016-09-30       Impact factor: 4.813

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