title: Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.
abstract: BACKGROUND
  CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings.
  
  
  METHODS
  The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives.
  
  
  RESULTS
  The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant.
  
  
  CONCLUSIONS
  From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
authors.affiliation: Department of Information and Computer Science, Federal Institute of Rio Grande do Norte (IFRN), Natal, Brazil. jose.macedo@ifrn.edu.br.
authors.forename: Macedo
authors.initials: M
authors.lastname: Firmino
authors.affiliation: Laboratory of Technological Innovation in Healthcare, University Hospital Onofre Lopes (HUOL), Natal, Brazil. giovani.angelo@gmail.com.
authors.forename: Giovani
authors.initials: G
authors.lastname: Angelo
authors.affiliation: Department of Information and Computer Science, Federal Institute of Rio Grande do Norte (IFRN), Natal, Brazil. higor.morais@ifrn.edu.br.
authors.forename: Higor
authors.initials: H
authors.lastname: Morais
authors.affiliation: Laboratory of Technological Innovation in Healthcare, University Hospital Onofre Lopes (HUOL), Natal, Brazil. ribeirodantasdm@gmail.com.
authors.forename: Marcel R
authors.initials: MR
authors.lastname: Dantas
authors.affiliation: Laboratory of Technological Innovation in Healthcare, University Hospital Onofre Lopes (HUOL), Natal, Brazil. ricardo.valentim@ufrnet.br.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Valentim
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D021621:Imaging, Three-Dimensional; D016013:Likelihood Functions; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed