Evaluation of diagnostic efficiency of computerized image analysis based quantitative nuclear parameters in papillary and follicular thyroid tumors using paraffin-embedded tissue sections.
Computerized image analysis (IA) system has emerged in recent years as a very powerful tool for objective and reproducible quantification of histological features. It has shown considerable potential for diagnostic application in diverse histological situations. The objectives of the present study were to evaluate the discriminatory diagnostic efficiency of computerized image analysis based quantitative subvisual nuclear parameters in papillary and follicular neoplasms of thyroid. A total of 60 cases were studied. Forty-four cases belonged to training set and 16 cases belonged to a test set. A minimum of 100 nuclei was analyzed in each case using uniform 5 m mm thick hematoxylin stained sections. The IA workstation comprised of an Olympus microscope, a 10 bit digital video camera, an image grabber card and a pentium 120 MHz computer. Optimas 5.2 software was utilized for data collection on 8 morphometric and 8 densitometric parameters. Multivariate stepwise discriminant statistical analysis of data was done with the help of BMDP statistical software release 7.0. Results from a training set revealed correct classification rates of 98.0%, 84.5% and 61.2% for the histological groups of hyperplastic papillae versus papillae of papillary carcinoma (group I), follicular variant of papillary carcinoma versus the broad category of follicular neoplasms consisting of both follicular adenoma and follicular carcinoma (group II) and follicular adenoma versus follicular carcinoma (group III), respectively. Results of test set revealed correct classification rates of 100%, 80% and 50% for groups I, II and III respectively. It was concluded that computerized nuclear IA parameters have potential usefulness for discriminating benign versus malignant papillary lesions of thyroid, follicular variant of papillary carcinoma versus follicular adenoma and/or follicular carcinoma but are of no value in discriminating between follicular adenoma and follicular carcinoma.
Gupta
N
N
All India Institute of Medical Sciences, Department of Pathology, New Delhi - 110029, India.
Sarkar
C
C
Singh
R
R
Karak
A K
AK
D018263:Adenocarcinoma, Follicular / Q000175:diagnosis*; D000236:Adenoma / Q000175:diagnosis*; D002291:Carcinoma, Papillary / Q000175:diagnosis*; D002467:Cell Nucleus / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016612:Paraffin Embedding; D012189:Retrospective Studies; D013964:Thyroid Neoplasms / Q000473:pathology*