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Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research

Journal of Digital Imaging volume 29, pages 716–729 (2016)Cite this article

Abstract

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.

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Notes

  1. Available at www.morpheusdata.com/ [Online; accessed on June 14, 2016]

  2. Available at www.speedtest.net [Online; accessed on June 14, 2016]

Abbreviations

3DTA:

3D texture attributes

API:

Application programming interface

BSON:

Binary JavaScript Object Notation

CAD:

Computer-aided diagnosis

CBIR:

Content-based image retrieval

CR:

Computed radiography

CT:

Computed tomography

DBaaS:

Database as a Service

DBMS:

Database management system

DICOM:

Digital Imaging and Communications in Medicine

DX:

Digital radiography

FDA:

Food and Drug Administration

IDRI:

Image Database Resource Initiative

GLCM:

Gray-level co-occurrence matrix

GUI:

Graphical user interface

HIPAA:

Health Insurance Portability and Accountability Act

IP:

Internet protocol

JSON:

JavaScript Object Notation

LIDC:

Lung Image Database Consortium

NCI:

National Cancer Institute

NLST:

National Lung Screening Trial

NoSQL:

Not only Structured Query Language

NSCLC:

Nonsmall cell lung cancer

PR:

(DICOM) PResentation state

PT:

Positron emission tomography

QIBA:

Quantitative Imaging Biomarkers Alliance

RDBMS:

Relational Database Management System

RIDER:

Reference Image Database to Evaluate Therapy Response

SEG:

(DICOM) SEGmentation

SR:

(DICOM) Structured Report document

ROI:

Region of interest

TCIA:

The Cancer Imaging Archive

XaaS:

Everything as a Service

XML:

eXtensible Markup Language

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Acknowledgments

We thank the Brazilian institutions Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Pesquisa do Estado de Alagoas (FAPEAL) for the financial support in the form of a master scholarship (grant number 20130603-002-0040-0063).

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Affiliations

  1. Lab of Telemedicine and Medical Informatics, University Hospital Prof. Alberto Antunes, Institute of Computing, Federal University of Alagoas, Av. Lourival Melo Mota, Cidade Universitária, 57072-900, Maceió, Alagoas, Brazil

    José Raniery Ferreira Junior & Marcelo Costa Oliveira

  2. Center of Imaging Sciences and Medical Physics, Internal Medicine Department, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, Monte Alegre, Ribeirão Preto, São Paulo, Brazil

    Paulo Mazzoncini de Azevedo-Marques

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  1. José Raniery Ferreira Junior
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  2. Marcelo Costa Oliveira
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  3. Paulo Mazzoncini de Azevedo-Marques
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Correspondence to José Raniery Ferreira Junior.

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Ferreira Junior, J.R., Oliveira, M.C. & de Azevedo-Marques, P.M. Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research. J Digit Imaging 29, 716–729 (2016). https://doi.org/10.1007/s10278-016-9894-9

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  • DOI: https://doi.org/10.1007/s10278-016-9894-9

Keywords

  • Lung cancer
  • Pulmonary nodule
  • Lung Image Database Consortium
  • Image Database Resource Initiative
  • Computer-aided diagnosis
  • Computer-aided detection
  • 3D texture analysis
  • NoSQL
  • Document-oriented nonrelational database
  • MongoDB
  • Cloud computing
  • Database as a Service
  • Reproducible research