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Objectives <p>To assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma.</p> Methods <p>We conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy.</p> Results <p>NAC responders had higher DCE-MRI-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-MRI-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status (OR, 0.187; 95% CI: 0.038, 0.914; <i>P</i> = 0.038) and tumor margin (OR, 20.643; 95% CI: 2.892, 147.365; <i>P</i> = 0.003) were identified as independent factors influencing the lack of significant efficacy of neoadjuvant chemotherapy (NAC) in breast cancer.</p> Conclusions <p>Combining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights.</p> Clinical trial number <p>Not applicable.</p>

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Prediction of neoadjuvant chemotherapy efficacy in breast cancer: integrating multimodal imaging and clinical features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月14日

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  • Xianglong Chen,
  • Yong Luo,
  • Zhiming Xie,
  • Yun Wen,
  • Fangsheng Mou,
  • Wenbing Zeng

摘要

Objectives

To assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma.

Methods

We conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy.

Results

NAC responders had higher DCE-MRI-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-MRI-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status (OR, 0.187; 95% CI: 0.038, 0.914; P = 0.038) and tumor margin (OR, 20.643; 95% CI: 2.892, 147.365; P = 0.003) were identified as independent factors influencing the lack of significant efficacy of neoadjuvant chemotherapy (NAC) in breast cancer.

Conclusions

Combining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights.

Clinical trial number

Not applicable.