NCI Cancer Research Data Commons: Cloud-Based Analytic Resources

Pot, David; Worman, Zelia; Baumann, Alexander; Pathak, Shirish; Beck, Rowan; Beck, Erin; Thayer, Katherine; Davidsen, Tanja M.; Kim, Erika; Davis-Dusenbery, Brandi; Otridge, John; Pihl, Todd; ,; Barnholtz-Sloan, Jill S.; Kerlavage, Anthony R.

doi:10.1158/0008-5472.can-23-2657

Cited by 6 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of using the public cloud services directly, our approach used the infrastructure built on top of the generic capabilities provided by the cloud providers to support scientific analysis workflows. We implemented the processing pipeline described earlier as a portable workflow compatible with the CRDC Terra (available under FireCloud within CRDC; further referred to as Terra for the sake of brevity) and Seven Bridges Cancer Genomics Cloud (SB-CGC) cloud resources (CRDC CRs) 9 . CRDC CRs are analytical components of CRDC that aim to simplify access to cloud-ready tools and enable cloud-based analysis of the data available in CRDC.…”

Section: Methodsmentioning

confidence: 99%

“…We present a detailed investigation of the development and optimization of a computational workflow to perform NLST segmentation efficiently - both in terms of processing time and costs - using the Google Cloud Platform and the components of the NCI Cancer Research Data Commons (CRDC) 9 - a cloud-based data science infrastructure that provides secure access to a large, comprehensive, and expanding collection of cancer research data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cloud-based large-scale curation of medical imaging data using AI segmentation

Thiriveedhi,

Krishnaswamy,

Clunie

et al. 2024

Preprint

View full text Add to dashboard Cite

Rapid advances in medical imaging Artificial Intelligence (AI) offer unprecedented opportunities for automatic analysis and extraction of data from large imaging collections. Computational demands of such modern AI tools may be difficult to satisfy with the capabilities available on premises. Cloud computing offers the promise of economical access and extreme scalability. Few studies examine the price/performance tradeoffs of using the cloud, in particular for medical image analysis tasks. We investigate the use of cloud-provisioned compute resources for AI-based curation of the National Lung Screening Trial (NLST) Computed Tomography (CT) images available from the National Cancer Institute (NCI) Imaging Data Commons (IDC). We evaluated NCI Cancer Research Data Commons (CRDC) Cloud Resources - Terra (FireCloud) and Seven Bridges-Cancer Genomics Cloud (SB-CGC) platforms - to perform automatic image segmentation with TotalSegmentator and pyradiomics feature extraction for a large cohort containing >126,000 CT volumes from >26,000 patients. Utilizing >21,000 Virtual Machines (VMs) over the course of the computation we completed analysis in under 9 hours, as compared to the estimated 522 days that would be needed on a single workstation. The total cost of utilizing the cloud for this analysis was $1,011.05. Our contributions include: 1) an evaluation of the numerous tradeoffs towards optimizing the use of cloud resources for large-scale image analysis; 2) CloudSegmentator, an open source reproducible implementation of the developed workflows, which can be reused and extended; 3) practical recommendations for utilizing the cloud for large-scale medical image computing tasks. We also share the results of the analysis: the total of 9,565,554 segmentations of the anatomic structures and the accompanying radiomics features in IDC as of release v18.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud-based large-scale curation of medical imaging data using AI segmentation

Thiriveedhi,

Krishnaswamy,

Clunie

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Artificial intelligence for drug repurposing against infectious diseases

Singh

2024

Artificial Intelligence Chemistry

View full text Add to dashboard Cite

NCI Cancer Research Data Commons: Core Standards and Services

Brady,

Charbonneau,

Grossman

et al. 2024

Cancer Research

View full text Add to dashboard Cite

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) is a collection of data commons, analysis platforms, and tools that make existing cancer data more findable and accessible by the cancer research community. In practice, the two biggest hurdles to finding and using data for discovery are the wide variety of models and ontologies used to describe data, and the dispersed storage of that data. Here, we outline core CRDC services to aggregate descriptive information from multiple studies for findability via a single interface, and to provide a single access method that spans multiple data commons.

show abstract

NCI Cancer Research Data Commons: Cloud-Based Analytic Resources

Cited by 6 publications

References 21 publications

Cloud-based large-scale curation of medical imaging data using AI segmentation

Cloud-based large-scale curation of medical imaging data using AI segmentation

Artificial intelligence for drug repurposing against infectious diseases

NCI Cancer Research Data Commons: Core Standards and Services

Contact Info

Product

Resources

About