Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

Massard Prairie Restoration and Soil Microbiome Succession

We have initially sequenced soil microbial DNA from 4 restored and 4 virgin tallgrass prairie soil samples from Ben Geren Park and Massard Prairie (Fort Smith, AR), respectively. As expected, the soil microbiomes are distinct, with several lineages of nitrogen-fixing bacteria more common in virgin tall grass prairie. However, we predict that as restoration of tallgrass prairie in Ben Geren Park progresses, the soil microbiome of restored prairie will more closely mirror those of the virgin prairie

Arkansas AI-Campus Method for the 2019 Kidney Tumor Segmentation Challenge

Our Arkansas AI-Campus team participants the 2019 Kidney Tumor Segmentation Challenge (KiTS19) during the past 4 months. Here the paper provides a summary of our methods and validation results for this grand challenge in biomedical imaging analysis