Flow chart of the study design. Deep convolutional neural networks were trained on hematoxylin and eosin stained tissue microarray spots from a nationwide breast cancer series (FinProg) to predict the ERBB2 gene amplification status of the primary tumor. The networks were trained using a transfer learning approach with ImageNet pretrained weights, and only the deepest layers (colored in yellow) were finetuned by minimizing the focal loss, weakly supervised by the ground truth ERBB2 gene amplification status as determined by chromogenic in situ hybridization. At the test phase, the networks generate probabilities of ERBB2 amplification (the H&E-ERBB2 score). The classification accuracy was summarized with receiver operating characteristic and precision-recall curves. Additionally, we applied Kaplan–Meier plots and Cox regression analysis to correlate the H&E-ERBB2 scores with patient treatment outcome data. Credit: Scientific Reports volume 11, Article number: 4037 (2021)


AI predicts efficacy of breast cancer treatment directly from tumor architecture


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