title: A complementary method for the detection of osteoblastic metastases on digitized radiographs.
abstract: OBJECTIVE
  This study was conducted to evaluate the diagnostic usefulness of gray level parameters in order to distinguish healthy bone from osteoblastic metastases on digitized radiographs.
  
  
  METHODS
  Skeletal radiographs of healthy bone (n = 144) and osteoblastic metastases (n = 35) were digitized using pixels 0.175 mm in size and 4,096 gray levels. We obtained an optimized healthy bone classification to compare with pathological bone: cortical, trabecular, and flat bone. The osteoblastic metastases (OM) were classified in nonflat and flat bone. These radiological images were analyzed by using a computerized method. The parameters (gray scale) calculated were: mean, standard deviation, and coefficient of variation (MGL, SDGL, and CVGL, respectively) based on gray level histogram analysis. Diagnostic utility was quantified by measurement of parameters on healthy and pathological bone, yielding quantification of area under the receiver operating characteristic (ROC) curve, AUC.
  
  
  RESULTS
  All three image parameters showed high and significant values of AUC when comparing healthy trabecular bone and nonflat bone OM, showing MGL the best discriminatory ability (0.97). As for flat bones, MGL showed no ability to distinguish between healthy and flat bone OM (0.50). This could be achieved by using SDGL or CVGL, with both showing a similar diagnostic ability (0.85 and 0.83, respectively).
  
  
  CONCLUSIONS
  Our results show that the use of gray level parameters quantify healthy bone and osteoblastic metastases zones on digitized radiographs. This may be helpful as a complementary method for differential diagnosis. Moreover, our method will allow us to study the evolution of osteoblastic metastases under medical treatment.
authors.affiliation: Department of Physiological Sciences II, Faculty of Medicine, University of Barcelona, C/ Feixa Llarga s/n Pavelló de Govern, Lab. 41.57, 08907 Hospitalet de Llobregat, Barcelona, Spain. angelgonzalez@ub.edu
authors.forename: Angel
authors.initials: A
authors.lastname: González-Sistal
authors.forename: Alicia
authors.initials: A
authors.lastname: Baltasar Sánchez
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D001859:Bone Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D050281:Bones of Lower Extremity / Q000000981:diagnostic imaging / Q000473:pathology; D001133:Bones of Upper Extremity / Q000000981:diagnostic imaging / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018215:Osteoblastoma / Q000000981:diagnostic imaging* / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D012886:Skull / Q000000981:diagnostic imaging / Q000473:pathology; D013030:Spain / Q000453:epidemiology; D013131:Spine / Q000000981:diagnostic imaging / Q000473:pathology; D013909:Thorax / Q000473:pathology