Validation of nuclear texture, density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma.

摘要

OBJECTIVE: To evaluate the performance of karyometry and histometry in the prediction of survival, recurrence and response of early-stage invasive cervical carcinoma. STUDY DESIGN: Nuclear morphometry, chromatin texture and tissue architecture (characterized by syntactic structure analysis) were measured using a semiautomated image analysis system on 46 cases of Feulgen-stained tissue sections. The performance of the features was compared to that of clinical features, reported to be the best prognosticators until now, such as age, lympho-vascular permeation, histologic type, stage and grade. A K nearest neighbor classifier was used for classification. RESULTS: In the prediction of three-year survival, recurrence and response, syntactic structure analysis proved to be the best performer. Classification rates were, respectively, 100%, 94.4% and 94.5%. In all classifications, karyometric and histometric features outperformed clinical features. In general, the best performing features described differences in second-order population statistics (standard deviations). CONCLUSION: The results show that a quantitative analysis based on nuclear morphology, chromatin texture and histology can be considered an excellent aid in the prognosis of invasive cervical carcinoma. The measurements are not hampered by the need to undertake complete resections and are suited to daily practice when implemented in a semiautomated image analysis system.