Further evaluation of quantitative nuclear image features for classification of lung carcinomas.
The usefulness of quantitative nuclear image features (QNI) for the histological classification of lung carcinomas was investigated. As no clear distinction could be established between the distributions of these features for the nuclei of squamous cell, adenocarcinoma, and large cell carcinoma, the attention was restricted to the discrimination between small cell lung carcinoma (SCLC) and non-small cell carcinoma (NSCLC). This discrimination is the crucial one in discussions about the choice of treatment. The differences between SCLC and NSCLC are statistically highly significant for various QNI features. The use of more than one QNI feature hardly raised the discriminatory performance with respect to the distinction between SCLC and NSCLC. Inferences were made about the probability and confidence interval of SCLC for a given QNI feature. It is concluded that in cases of uncertainty or disagreement, nuclear characteristics are useful for the discrimination between SCLC and NSCLC.
Thunnissen
F B
FB
Department of Pathology, University Hospital, Maastricht, The Netherlands.
Diegenbach
P C
PC
van Hattum
A H
AH
Tolboom
J
J
van der Sluis
D M
DM
Schaafsma
W
W
Houthoff
H J
HJ
Baak
J P
JP
D000230:Adenocarcinoma / Q000145:classification / Q000175:diagnosis / Q000648:ultrastructure; D002277:Carcinoma / Q000145:classification / Q000175:diagnosis / Q000648:ultrastructure; D002289:Carcinoma, Non-Small-Cell Lung / Q000145:classification / Q000175:diagnosis / Q000648:ultrastructure; D018288:Carcinoma, Small Cell / Q000145:classification / Q000175:diagnosis / Q000648:ultrastructure; D002294:Carcinoma, Squamous Cell / Q000145:classification / Q000175:diagnosis / Q000648:ultrastructure; D002467:Cell Nucleus / Q000648:ultrastructure*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008175:Lung Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000648:ultrastructure*