title: Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients.
abstract: OBJECTIVE
  The aim of this study was to investigate the diagnostic ability of a completely automated computer-assisted diagnosis (CAD) system to detect metastases in bone scans by two patterns: one was per region, and the other was per patient.
  
  
  METHODS
  This study included 406 patients with suspected metastatic bone tumors who underwent whole-body bone scans that were analyzed by the automated CAD system. The patients were divided into four groups: a group with prostatic cancer (N = 71), breast cancer (N = 109), males with other cancers (N = 153), and females with other cancers (N = 73). We investigated the bone scan index and artificial neural network (ANN), which are parameters that can be used to classify bone scans to determine whether there are metastases. The sensitivities, specificities, positive predictive value (PPV), negative predictive value (NPV), and accuracies for the four groups were compared. Receiver operating characteristic (ROC) analyses of region-based ANN were performed to compare the diagnostic performance of the automated CAD system.
  
  
  RESULTS
  There were no significant differences in the sensitivity, specificity, or NPV between the four groups. The PPVs of the group with prostatic cancer (51.0 %) were significantly higher than those of the other groups (P < 0.01). The accuracy of the group with prostatic cancer (81.5 %) was significantly higher than that of the group with breast cancer (68.6 %) and the females with other cancers (65.9 %) (P < 0.01). For the evaluation of the ROC analysis of region-based ANN, the highest Az values for the groups with prostatic cancer, breast cancer, males with other cancers, and females with other cancers were 0.82 (ANN = 0.4, 0.5, 0.6, 0.7, and 0.8), 0.83 (ANN = 0.7), 0.81 (ANN = 0.5), and 0.81 (ANN = 0.6), respectively.
  
  
  CONCLUSIONS
  The special CAD system "BONENAVI" trained with a Japanese database appears to have significant potential in assisting physicians in their clinical routine. However, an improved CAD system depending on the primary lesion of the cancer is required to decrease the proportion of false-positive findings.
authors.affiliation: Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minamikogushi, Ube, Yamaguchi, 755-8505, Japan, tokugama@gmail.com.
authors.forename: Osamu
authors.initials: O
authors.lastname: Tokuda
authors.forename: Yuko
authors.initials: Y
authors.lastname: Harada
authors.forename: Yona
authors.initials: Y
authors.lastname: Ohishi
authors.forename: Naofumi
authors.initials: N
authors.lastname: Matsunaga
authors.forename: Lars
authors.initials: L
authors.lastname: Edenbrandt
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001859:Bone Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D001943:Breast Neoplasms / Q000473:pathology; D005189:False Positive Reactions; D005500:Follow-Up Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011471:Prostatic Neoplasms / Q000473:pathology; D012372:ROC Curve; D011877:Radionuclide Imaging; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed; D051598:Whole Body Imaging / Q000379:methods*