Accurate prediction of neoadjuvant chemotherapy pathological complete remission (pCR) for the four sub-types of breast cancer

• Published in: IEEE access Vol. 7; p. 1
• Main Authors: Feng, Xin, Song, Lelian, Wang, Shaofei, Song, Haoqiu, Chen, Hang, Liu, Yuxuan, Lou, Chenwei, Zhao, Jian, Liu, Quewang, Liu, Yang, Zhao, Ruixue, Xing, Kai, Li, Sijie, Yu, Yunhe, Liu, Zhenyu, Yin, Chengyang, Han, Bing, Du, Ye, Xin, Ruihao, Huang, Lan, Fan, Zhimin, Zhou, Fengfeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: biomarker detection; Breast cancer; Cancer therapies; Chemotherapy; Classification algorithms; feature selection; neoadjuvant chemotherapy; pathological complete response (pCR); Pathology; Predictions; Predictive models; Tumors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2941543

Neoadjuvant chemotherapy (NAC) has become the main treatment option for breast cancer. Its adverse drug reactions (ADRs) make NAC painful both physiologically and psychologically. The factor pathological complete remission (pCR) describes how well a series of six or more chemotherapeutic treatments works on a patient. This study investigated the possibility of predicting pCR using only the nodal sizes of the first three treatments. A best feature combination for each breast cancer subtype was screened from the real nodal sizes of the first three treatments and the nodal sizes' of the next three treatments predicted from those of the first three ones. The prediction was evaluated by the metrics Avc=(sensitivity+specificity)/2. A triple-negative breast cancer (TN) patient may have an estimation of pCR Avc=0.8696 after taking just three treatments. At least Avc=0.7594 was achieved for all the four breast cancer subtypes investigated in this study.