Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials. © The Authors.Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

3069 Background: We sought to gain new insight into triple-negative breast cancer (TNBC), an aggressive, clinically distinct subgroup of breast cancers, by applying a sequence of computational approaches to tumor segmentation, three-dimensional anatomic characterization, and tumor subtyping. We extracted algorithmically-derived quantitative imaging (radiomics) features from each TNBC lesion in breast magnetic resonance imaging (MRI) to identify underlying subtypes. Methods: We evaluated tumors on pre-treatment, post-contrast MRI from 90 patients with non-metastatic TNBC. We employed active contour segmentation and semi-automated identification of tumor regions-of-interest. We extracted 900 radiomics features from each segmented tumor using an algorithm that characterizes the size, shape, texture, and edge sharpness of tumors at the voxel level. We applied k-means consensus clustering, a statistical tool for unsupervised discovery, and performed 1000 bootstraps with resampling on the feature vectors to examine all resulting clusters from k=2 to 10. Based on two diagnostic metrics of consensus stability, we selected the optimum cluster number. We performed Significance Analysis of Microarrays to identify statistically significant radiomics features for each cluster. Results: We identified three distinct image-based clusters in 117 tumors from 90 TNBC patients (multifocal lesions in n=13). Cluster 1 (n=97) was distinguished by 330 radiomics features (False Discovery Rate [FDR] <5%) and Cluster 2 (n=13) by 85 features (FDR<5%), whereas Cluster 3 (n=7) was not significantly associated with features. Clinical characteristics did not differ across the three clusters, with mean age (49.1±11.7) and clinical stage distributions (stage I: 20.7%, II: 55.4%, III: 23.9%) for the cohort mirroring those of individual clusters. Among those who received neoadjuvant therapy, we observed pathologic complete response in 50% (23 of 46, 95% CI, 0.36-0.64) of patients in Cluster 1, 83% (5 of 6, 95% CI, 0.54-1.0) in Cluster 2, and 0% (0 of 3) in Cluster 3. Conclusions: Radiomics features providing voxel-level characteristics of tumor morphology differentiated TNBC into three distinct subtypes. These subtypes, defined by radiomics biomarkers, may be associated with clinical response to neoadjuvant therapy.