In The journal of pathology. Clinical research
In recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy. Application of AI to pathological images typically uses a single machine-learning model such as support vector machines (SVMs) or deep convolutional neural networks (CNNs). However, cancer tissues are extremely diverse and learning with a realistic number of cases limits the prediction accuracy of a single model. In this study, we propose a novel pipeline system that uses three independent models each focusing on different characteristics of cancer atypia. Our system uses a CNN model to learn structural atypia from image patches and SVM and random forest models to learn nuclear atypia from fine-grained nuclear features extracted by image analysis methods. It was able to predict the NAC response with 95.15% accuracy on a test set of 103 unseen cases. We believe that this AI pipeline system will contribute to the adoption of personalized medicine in NAC therapy for breast cancer.
Shen Bin, Saito Akira, Ueda Ai, Fujita Koji, Nagamatsu Yui, Hashimoto Mikihiro, Kobayashi Masaharu, Mirza Aashiq H, Graf Hans Peter, Cosatto Eric, Hazama Shoichi, Nagano Hiroaki, Sato Eiichi, Matsubayashi Jun, Nagao Toshitaka, Cheng Esther, Hoda Syed Af, Ishikawa Takashi, Kuroda Masahiko
2023-Mar-10
artificial intelligence, breast cancer, digital pathology, neoadjuvant chemotherapy
