Objectives-To develop and validate a proof-of-concept convolutional neural network (CNN)based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.Methods-A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.

Objectives-To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods-A convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the relative contribution of a feature to the predicted lesion classification.

Antiangiogenic therapy for solid tumors clearly destroys tumor vasculature and reduces tumor growth. As an unexpected bonus, drugs that neutralize VEGF signaling generate a "normalization window" for tumor vasculature. This occurs via the recruitment of pericytes to the tumor vasculature, an effect associated with the transient stabilization of vessels and improved oxygen delivery to hypoxic zones. The normalization process is mediated by angiopoietin-1 and matrix metalloproteinases and creates a window of opportunity for improved sensitivity to ionizing radiation and the delivery of chemotherapeutic drugs.

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