Percentage apparent diffusion coefficient change for early prediction of response to neoadjuvant chemotherapy in...

Neoadjuvant chemotherapy (NACT) is an important part of comprehensive breast cancer treatment. However, considering that not all patients respond well, how to identify responders as soon as possible is a significant issue. Magnetic resonance imaging (MRI) shows excellent diagnostic accuracy in breast cancer, while whether it is able to identify NACT responders is not clear. In this study, we aim to evaluate the role of early percentage apparent diffusion coefficient change (ΔADC%) in predicting response to NACT in patients with breast cancer.

We searched studies in PubMed, Web of Science, and Cochrane Library up to April 28, 2025. The inclusion criteria were: predicting response to NACT in breast cancer; using ΔADC% as prediction parameter; diffusion-weighted imaging (DWI)-MRI images were acquired before NACT beginning and early during NACT (after 1 or 2 cycles); providing exact criteria for responders. The exclusion criteria were: not in English or Chinese; animal studies; review/meta-analysis/abstract/case report; the number of true-positive (TP), false-negative (FN), false-positive (FP), and true-negative (TN) findings cannot be extracted directly or indirectly. The methodological quality of the included studies was assessed by Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2. Sensitivity, specificity, and summary receiver operating characteristic (SROC) curve were used to evaluate the prediction accuracy. Sub-group analyses were used to find the source of heterogeneity. For the retrospective study, 47 patients with breast cancer from our hospital were included. We used the Mann-Whitney U test to compare the ΔADC% between the pathological complete response (pCR) and non-pCR group. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the prediction ability.

In the meta-analysis, 11 studies and 681 patients were included. Pooled sensitivity and specificity were 0.71 [95% confidence interval (CI): 0.60-0.79] and 0.78 (95% CI: 0.68-0.85), respectively. AUC was 0.81 (95% CI: 0.77-0.84). There was no obvious difference between the sub-groups. In the validation study, ΔADC% was significantly higher in the pCR group than in the non-pCR group. When 20.3% was chosen as the cut-off value, ΔADC% had a sensitivity of 64.7% and a specificity of 73.3% in predicting pCR.

ΔADC% may be valuable in early prediction of response to NACT in breast cancer. The results need further validation in a larger population.