1. Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer
- Author
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Shaolei Yan, Haiyong Peng, Qiujie Yu, Xiaodan Chen, Yue Liu, Ye Zhu, Kaige Chen, Ping Wang, Yujiao Li, Xiushi Zhang, and Wei Meng
- Subjects
Adult ,Aged, 80 and over ,Cancer Research ,Breast Neoplasms ,General Medicine ,Middle Aged ,Magnetic Resonance Imaging ,Neoadjuvant Therapy ,ROC Curve ,Oncology ,Predictive Value of Tests ,Radiologists ,Image Processing, Computer-Assisted ,Humans ,Female ,Aged ,Retrospective Studies - Abstract
Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.
- Published
- 2022