3D Pyramid Pooling Network for Abdominal MRI Series Classification.

Recognizing and organizing different series in an MRI examination is important both for clinical review and research, but it is poorly addressed by the current generation of picture archiving and communication systems (PACSs) and post-processing workstations. In this paper, we study the problem of using deep convolutional neural networks for automatic classification of abdominal MRI series to one of many series types. Our contributions are three-fold. First, we created a large abdominal MRI dataset containing 3717 MRI series including 188,665 individual images, derived from liver examinations. 30 different series types are represented in this dataset. The dataset was annotated by consensus readings from two radiologists. Both the MRIs and the annotations were made publicly available. Second, we proposed a 3D pyramid pooling network, which can elegantly handle abdominal MRI series with varied sizes of each dimension, and achieved state-of-the-art classification performance. Third, we performed the first ever comparison between the algorithm and the radiologists on an additional dataset and had several meaningful findings.

Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study.

PURPOSE: To develop a deep convolutional neural network (CNN) model to categorize multiphase CT and MRI liver observations using the liver imaging reporting and data system (LI-RADS) (version 2014). METHODS: A pre-existing dataset comprising 314 hepatic observations (163 CT, 151 MRI) with corresponding diameters and LI-RADS categories (LR-1-5) assigned in consensus by two LI-RADS steering committee members was used to develop two CNNs: pre-trained network with an input of triple-phase images (training with transfer learning) and custom-made network with an input of quadruple-phase images (training from scratch). The dataset was randomly split into training, validation, and internal test sets (70:15:15 split). The overall accuracy and area under receiver operating characteristic curve (AUROC) were assessed for categorizing LR-1/2, LR-3, LR-4, and LR-5. External validation was performed for the model with the better performance on the internal test set using two external datasets (EXT-CT and EXT-MR: 68 and 44 observations, respectively). RESULTS: The transfer learning model outperformed the custom-made model: overall accuracy of 60.4% and AUROCs of 0.85, 0.90, 0.63, 0.82 for LR-1/2, LR-3, LR-4, LR-5, respectively. On EXT-CT, the model had an overall accuracy of 41.2% and AUROCs of 0.70, 0.66, 0.60, 0.76 for LR-1/2, LR-3, LR-4, LR-5, respectively. On EXT-MR, the model had an overall accuracy of 47.7% and AUROCs of 0.88, 0.74, 0.69, 0.79 for LR-1/2, LR-3, LR-4, LR-5, respectively. CONCLUSION: Our study shows the feasibility of CNN for assigning LI-RADS categories from a relatively small dataset but highlights the challenges of model development and validation.

Targeting oncogenic ALK and MET: a promising therapeutic strategy for glioblastoma.

Glioblastoma is the most common aggressive, highly glycolytic, and lethal brain tumor. In fact, it is among the most commonly diagnosed lethal malignancies, with thousands of new cases reported in the United States each year. Glioblastoma's lethality is derived from a number of factors including highly active pro-mitotic and pro-metastatic pathways. Two factors increasingly associated with the intracellular signaling and transcriptional machinery required for such changes are anaplastic lymphoma kinase (ALK) and the hepatocyte growth factor receptor (HGFR or, more commonly MET). Both receptors are members of the receptor tyrosine kinase (RTK) family, which has itself gained much attention for its role in modulating mitosis, migration, and survival in cancer cells. ALK was first described as a vital oncogene in lymphoma studies, but it has since been connected to many carcinomas, including non-small cell lung cancer and glioblastoma. As the receptor for HGF, MET has also been highly characterized and regulates numerous developmental and wound healing events which, when upregulated in cancer, can promote tumor progression. The wealth of information gathered over the last 30 years regarding these RTKs suggests three downstream cascades that depend upon activation of STAT3, Ras, and AKT. This review outlines the significance of ALK and MET as they relate to glioblastoma, explores the significance of STAT3, Ras, and AKT downstream of ALK/MET, and touches on the potential for new chemotherapeutics targeting ALK and MET to improve glioblastoma patient prognosis.