Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: A meta-analysis

Wei Chu, Weiwei Jin, Daihong Liu, Jian Wang, Chengjun Geng, Lihua Chen and Xuequan Huang _

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Oncotarget. 2018; 9:7088-7100. https://doi.org/10.18632/oncotarget.23195

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Wei Chu1,*, Weiwei Jin2,*, Daihong Liu3, Jian Wang3, Chengjun Geng4, Lihua Chen4 and Xuequan Huang3

1Department of Radiology, Wuxi Huishan District People’s Hospital, Jiangsu Province, 214187, China

2Department of Radiology, Wuxi Second Traditional Chinese Medicine Hospital, Jiangsu Province, 214121, China

3Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China

4Department of Radiology, PLA No.101 Hospital, Wuxi, Jiangsu Province, 214044, China

*These authors contributed equally to this work

Correspondence to:

Xuequan Huang, email: [email protected]

Lihua Chen, email: [email protected]

Keywords: magnetic resonance imaging; diffusion-weighted imaging; neoadjuvant chemotherapy; breast cancer; meta-analysis

Received: October 27, 2017     Accepted: December 01, 2017     Published: December 11, 2017


Background: Diffusion-weighted imaging (DWI) is increasingly used to identify pathological complete responses (pCRs) to neoadjuvant chemotherapy (NAC) in breast cancer. The aim of the present study was to assess the utility of DWI using a pooled analysis.

Materials and Methods: Literature databases were searched prior to July 2017. Fifteen studies with a total of 1181 patients were included. The data were extracted to perform pooled analysis, heterogeneity testing, threshold effect testing, sensitivity analysis, publication bias analysis and subgroup analyses.

Result: The methodological quality was moderate. Remarkable heterogeneity was detected, primarily due to a threshold effect. The pooled weighted values were a sensitivity of 0.88 (95% confidence interval (CI): 0.81, 0.92), a specificity of 0.79 (95% CI: 0.70, 0.86), a positive likelihood ratio of 4.1 (95% CI: 2.9, 5.9), a negative likelihood ratio of 0.16 (95% CI: 0.10, 0.24), and a diagnostic odds ratio of 26 (95% CI: 15, 46). The area under the receiver operator characteristic curve was 0.91 (95% CI: 0.88, 0.93). In the subgroup analysis, the pooled specificity of change in the apparent diffusion coefficient (ADC) subgroup was higher than that in the pre-treatment ADC subgroup (0.80 [95% CI: 0.71, 087] vs. 0.63 [95% CI: 0.52, 0.73], P = 0.027).

Conclusions: DWI may be an accurate and nonradioactive imaging technique for identifying pCRs to NAC in breast cancer. Nonetheless, there are a variety of issues when assessing DWI techniques for estimating breast cancer responses to NAC, and large scale and well-designed clinical trials are needed to assess the technique’s diagnostic value.

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