Dynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer

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2024年12月30日

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Xinyi Wang,
Yuting Zhang,
Mengting Yang,
Nan Wu,
Shan Wang,
Hong Chen,
Tianyang Zhou,
Ying Zhang,
Xiaolan Wang,
Zining Jin,
Ang Zheng,
Fan Yao,
Dianlong Zhang,
Feng Jin,
Pan Qin,
Jia Wang

摘要

Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model. This retrospective study included 304 EBC patients recruited from multiple centers. All enrollees had completed NACT regimens, and underwent US examinations at baseline and at each NACT cycle. We subsequently determined that percentage reduction of tumor maximum diameter from baseline to third cycle of NACT serves to independent predictor for pCR, enabling creation of a nomogram ( \(\text{AUC}=0.75\) ). Our predictive accuracy further improved ( \(\text{AUC}=0.868\) ) by combining dynamic US data and clinicopathological features in a machine learning model. Such models may offer a means of accurately predicting NACT responses in this setting, helping to individualize patient therapy. Our study may provide additional insights into the US-based response prediction by focusing on the dynamic changes of the tumor in the early and full NACT cycle.