Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.

TitleDevelopment of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.
Publication TypeJournal Article
Year of Publication2023
AuthorsShen B, Saito A, Ueda A, Fujita K, Nagamatsu Y, Hashimoto M, Kobayashi M, Mirza AH, Graf HPeter, Cosatto E, Hazama S, Nagano H, Sato E, Matsubayashi J, Nagao T, Cheng E, Hoda SAf, Ishikawa T, Kuroda M
JournalJ Pathol Clin Res
Date Published2023 May
KeywordsArtificial Intelligence, Breast Neoplasms, Chemotherapy, Adjuvant, Female, Humans, Machine Learning, Neoadjuvant Therapy

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.

Alternate JournalJ Pathol Clin Res
PubMed ID36896856
PubMed Central IDPMC10073928
Related Faculty: 
Esther Cheng, D.O. Syed Hoda, M.D.

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