The volume, variety, and velocity of medical imaging data are exploding, making it impractical for clinicians to properly utilize such available information resources in an efficient fashion. At the same time, the interpretation of such a large amount of medical imaging data by humans is significantly error prone, reducing the possibility of extracting informative data. The ability to process such large amounts of data promises to decipher encrypted information within medical images, develop predictive and prognosis models to design personalized diagnosis, allow comprehensive study of tumor phenotype, and allow the assessment of tissue heterogeneity for diagnosis of different types of cancers.

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 – 0.91. The team’s predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm’s correction).

Since 2014, the NCI has launched a series of data commons as part of the Cancer Research Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies. This review describes each data commons (Genomic Data Commons, Proteomic Data Commons, Integrated Canine Data Commons, Cancer Data Service, Imaging Data Commons, and Clinical and Translational Data Commons), including their unique and shared features, accomplishments, and challenges. Also discussed is how the CRDC data commons implement Findable, Accessible, Interoperable, Reusable (FAIR) principles and promote data sharing in support of the new NIH Data Management and Sharing Policy. See related articles by Brady et al., p. 1384, Pot et al., p. 1396, and Kim et al., p. 1404

On April 17, 2010, scientists from academia, the National Cancer Institute (NCI), and the Food and Drug Administration (FDA) assembled at “The NCI Image Guided Drug Delivery Summit,” in Washington D.C., to discuss recent advances, barriers, opportunities, and regulatory issues related to the field. The meeting included a scientific session and an NCI/FDA session, followed by a panel discussion of speakers from both sessions. Image-guided drug delivery (IGDD) in cancer is a form of individualized therapy where imaging methods are used in guidance and monitoring of localized and targeted delivery of therapeutics to the tumor. So, a systematic approach to IGDD requires mechanisms for targeting, delivery, activation, and monitoring of the process. Although the goal in IGDD is to optimize the therapeutic ratio through personalized image-guided treatments, a major challenge is in overcoming the biological barriers to the delivery of therapeutics into tumors and cells. Speakers discussed potential challenges to clinical translation of nano-based drug delivery systems including in vivo characterization of nanocarriers, preclinical validation of targeting and delivery, studies of biodistribution, pharmacokinetics, pharmacodynamics, and toxicity as well as scale-up manufacturing of delivery systems. Physiologic and quantitative imaging techniques may serve as enabling tools that could potentially transform many existing challenges into opportunities for advancement of the field. Cancer Res; 71(2); 314–7. ©2011 AACR.

The Quantitative Imaging Network of the National Cancer Institute is in its 10th year of operation, and research teams within the network are developing and validating clinical decision support software tools to measure or predict the response of cancers to various therapies. As projects progress from development activities to validation of quantitative imaging tools and methods, it is important to evaluate the performance and clinical readiness of the tools before committing them to prospective clinical trials. A variety of tests, including special challenges and tool benchmarking, have been instituted within the network to prepare the quantitative imaging tools for service in clinical trials. This article highlights the benchmarking process and provides a current evaluation of several tools in their transition from development to validation.