Quantification of diverse subcellular immunohistochemical markers with clinicobiological relevancies: validation of a new computer-assisted image analysis procedure.
Tissue microarray technology and immunohistochemical techniques have become a routine and indispensable tool for current anatomical pathology diagnosis. However, manual quantification by eye is relatively slow and subjective, and the use of digital image analysis software to extract information of immunostained specimens is an area of ongoing research, especially when the immunohistochemical signals have different localization in the cells (nuclear, membrane, cytoplasm). To minimize critical aspects of manual quantitative data acquisition, we generated semi-automated image-processing steps for the quantification of individual stained cells with immunohistochemical staining of different subcellular location. The precision of these macros was evaluated in 196 digital colour images of different Hodgkin lymphoma biopsies stained for different nuclear (Ki67, p53), cytoplasmic (TIA-1, CD68) and membrane markers (CD4, CD8, CD56, HLA-Dr). Semi-automated counts were compared to those obtained manually by three separate observers. Paired t-tests demonstrated significant differences between intra- and inter-observer measurements, with more substantial variability when the cellular density of the digital images was > 100 positive cells/image. Overall, variability was more pronounced for intra-observer than for inter-observer comparisons, especially for cytoplasmic and membrane staining patterns (P < 0.0001 and P = 0.050). The comparison between the semi-automated and manual microscopic measurement methods indicates significantly lower variability in the results yielded by the former method. Our semi-automated computerized method eliminates the major causes of observer variability and may be considered a valid alternative to manual microscopic quantification for diagnostic, prognostic and therapeutic purposes.
Lejeune
Marylène
M
Department of Pathology, Hospital de Tortosa Verge de la Cinta, Tortosa, Spain. mlejeune.htvc.ics@gencat.net
Jaén
Joaquín
J
Pons
Lluis
L
López
Carlos
C
Salvadó
Maria-Teresa
MT
Bosch
Ramón
R
García
Marcial
M
Escrivà
Patricia
P
Baucells
Jordi
J
Cugat
Xavier
X
Alvaro
Tomás
T
D000941:Antigens / Q000032:analysis*; D015703:Antigens, CD / Q000032:analysis; D015214:Antigens, Differentiation, Myelomonocytic / Q000032:analysis; D014408:Biomarkers, Tumor / Q000032:analysis*; D015704:CD4 Antigens / Q000032:analysis; D019002:CD56 Antigen / Q000032:analysis; D016827:CD8 Antigens / Q000032:analysis; D006684:HLA-DR Antigens / Q000032:analysis; D006689:Hodgkin Disease / Q000175:diagnosis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007150:Immunohistochemistry; D019394:Ki-67 Antigen / Q000032:analysis; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D039102:Poly(A)-Binding Proteins / Q000032:analysis; D012986:Software Validation; D000074019:T-Cell Intracellular Antigen-1; D016159:Tumor Suppressor Protein p53 / Q000032:analysis; D000097382:CD68 Molecule
D000941:Antigens; D015703:Antigens, CD; D015214:Antigens, Differentiation, Myelomonocytic; D014408:Biomarkers, Tumor; D015704:CD4 Antigens; D019002:CD56 Antigen; D016827:CD8 Antigens; D006684:HLA-DR Antigens; D019394:Ki-67 Antigen; D039102:Poly(A)-Binding Proteins; D000074019:T-Cell Intracellular Antigen-1; D016159:Tumor Suppressor Protein p53; C067980:CD68 antigen, human; D000097382:CD68 Molecule; C071558:TIA1 protein, human