Literature DB >> 25891905

Role of the Apparent Diffusion Coefficient in the Prediction of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer.

Enida Bufi1, Paolo Belli2, Melania Costantini2, Antonio Cipriani2, Marialuisa Di Matteo3, Angelo Bonatesta2, Gianluca Franceschini4, Daniela Terribile4, Antonino Mulé3, Luigia Nardone5, Lorenzo Bonomo2.   

Abstract

BACKGROUND: We evaluated the diagnostic performance of the baseline diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) in the prediction of a complete pathologic response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer stratified according to the tumor phenotype. PATIENTS AND METHODS: We retrospectively studied 225 patients with stage II, III, and IV breast cancer who had undergone contrast-enhanced magnetic resonance imaging (MRI) and DWI before and after NAC, followed by breast surgery.
RESULTS: The tumor phenotypes were luminal (n = 143; 63.6%), triple-negative (TN) (n = 37; 16.4%), human epidermal growth factor receptor 2 (HER2)-enriched (n = 17; 7.6%), and hybrid (hormone receptor-positive/HER2(+); n = 28; 12.4%). After NAC, a pCR was observed in 39 patients (17.3%). No statistically significant difference was observed in the mean ADC value between a pCR and no pCR in the general population (1.132 ± 0.191 × 10(-3) mm(2)/s vs. 1.092 ± 0.189 × 10(-3) mm(2)/s, respectively; P = .23). The optimal ADC cutoff value in the general population was 0.975 × 10(-3) mm(2)/s (receiver operating characteristic [ROC] area under the curve [AUC], 0.587 for the prediction of a pCR). After splitting the population into subgroups according to tumor phenotype, we observed a significant or nearly significant difference in the mean ADC value among the responders versus the nonresponders in the TN (P = .06) and HER2(+) subgroups (P = .05). No meaningful difference was seen in the luminal and hybrid subgroups (P = .59 and P = .53, respectively). In contrast, in the TN and HER2(+) subgroups (cutoff value, 0.995 × 10(-3) mm(2)/s and 0.971 × 10(-3) mm(2)/s, respectively), we observed adequate ROC AUCs (0.766 and 0.813, respectively).
CONCLUSION: The pretreatment ADC value is not capable of predicting the pCR in the overall population of patients with locally advanced breast cancer. Nonetheless, an ameliorated diagnostic performance was observed in specific phenotype subgroups (ie, TN and HER2(+) tumors).
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast cancer phenotypes; Diffusion weighted imaging; Magnetic resonance imaging; Response prediction

Mesh:

Substances:

Year:  2015        PMID: 25891905     DOI: 10.1016/j.clbc.2015.02.002

Source DB:  PubMed          Journal:  Clin Breast Cancer        ISSN: 1526-8209            Impact factor:   3.225


  24 in total

1.  A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features.

Authors:  Valentina Giannini; Simone Mazzetti; Agnese Marmo; Filippo Montemurro; Daniele Regge; Laura Martincich
Journal:  Br J Radiol       Date:  2017-07-14       Impact factor: 3.039

Review 2.  Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting.

Authors:  Anna G Sorace; Sara Harvey; Anum Syed; Thomas E Yankeelov
Journal:  Semin Oncol Nurs       Date:  2017-09-15       Impact factor: 2.315

Review 3.  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

4.  Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial.

Authors:  David C Newitt; Zheng Zhang; Jessica E Gibbs; Savannah C Partridge; Thomas L Chenevert; Mark A Rosen; Patrick J Bolan; Helga S Marques; Sheye Aliu; Wen Li; Lisa Cimino; Bonnie N Joe; Heidi Umphrey; Haydee Ojeda-Fournier; Basak Dogan; Karen Oh; Hiroyuki Abe; Jennifer Drukteinis; Laura J Esserman; Nola M Hylton
Journal:  J Magn Reson Imaging       Date:  2018-10-22       Impact factor: 4.813

5.  DWI in the Assessment of Breast Lesions.

Authors:  Savannah C Partridge; Nita Amornsiripanitch
Journal:  Top Magn Reson Imaging       Date:  2017-10

6.  A Novel Marker, Based on Ultrasound Tomography, for Monitoring Early Response to Neoadjuvant Chemotherapy.

Authors:  Neb Duric; Peter Littrup; Mark Sak; Cuiping Li; Di Chen; Olivier Roy; Lisa Bey-Knight; Rachel Brem
Journal:  J Breast Imaging       Date:  2020-10-27

7.  Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial.

Authors:  Savannah C Partridge; Zheng Zhang; David C Newitt; Jessica E Gibbs; Thomas L Chenevert; Mark A Rosen; Patrick J Bolan; Helga S Marques; Justin Romanoff; Lisa Cimino; Bonnie N Joe; Heidi R Umphrey; Haydee Ojeda-Fournier; Basak Dogan; Karen Oh; Hiroyuki Abe; Jennifer S Drukteinis; Laura J Esserman; Nola M Hylton
Journal:  Radiology       Date:  2018-09-04       Impact factor: 29.146

8.  Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models.

Authors:  Shiteng Suo; Yan Yin; Xiaochuan Geng; Dandan Zhang; Jia Hua; Fang Cheng; Jie Chen; Zhiguo Zhuang; Mengqiu Cao; Jianrong Xu
Journal:  J Transl Med       Date:  2021-06-02       Impact factor: 5.531

9.  Diffusion-weighted MR imaging in prediction of response to neoadjuvant chemotherapy in patients with breast cancer.

Authors:  Xue-Ying Hu; Ying Li; Guan-Qiao Jin; Shao-Lv Lai; Xiang-Yang Huang; Dan-Ke Su
Journal:  Oncotarget       Date:  2017-07-05

10.  Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients.

Authors:  Gene Y Cho; Lucas Gennaro; Elizabeth J Sutton; Emily C Zabor; Zhigang Zhang; Dilip Giri; Linda Moy; Daniel K Sodickson; Elizabeth A Morris; Eric E Sigmund; Sunitha B Thakur
Journal:  Eur J Radiol Open       Date:  2017-08-18
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