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Objectives: To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI.Methods: A total of 244 patients were analyzed, 99 in Training Dataset scanned at 1.5T, 83 in Testing-1 and 62 in Testing-2 scanned at 3T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2−), HER2+ and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the Training dataset, by using a conventional CNN and the convolutional long short term memory (CLSTM). Then, transfer learning was applied to re-tune the model using Testing-1(2) and evaluated in Testing-2(1). Results:In the Training dataset, the mean accuracy evaluated using 10-fold cross-validation was higher by using CLSTM (0.91) than CNN (0.79). When the developed model was applied to the independent Testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in Testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in Testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%.

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Abstract. It is challenging to image fluorescence objects with high spatial resolution in a highly scattering medium. Recently reported temperature-sensitive indocyanine green-loaded pluronic nanocapsules can potentially alleviate this problem. Here we demonstrate a frequency-domain temperature-modulated fluorescence tomography system that could acquire images at high intensity-focused ultrasound resolution with use of these nanocapsules. The system is experimentally verified with a phantom study, where a 3-mm fluorescence object embedded 2 cm deep in a turbid medium is successfully recovered based on both intensity and lifetime contrast.