Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects.
BACKGROUND
The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance.


METHODS
The relevant literature related to "CADe for lung cancer" was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized.


CONCLUSIONS
Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research.


CONCLUSIONS
Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.
Department of Information and Computer Science, Federal Institute of Rio Grande do Norte (IFRN), Natal, Brazil. jose.macedo@ifrn.edu.br.
Macedo
M
Firmino
Antônio H
AH
Morais
Roberto M
RM
Mendoça
Marcel R
MR
Dantas
Helio R
HR
Hekis
Ricardo
R
Valentim
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*