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

OBJECTIVE: To evaluate the performance of karyometry and histometry in the prediction of survival, recurrence and response of early-stage invasive cer vical carcinoma. STUDY DESIGN: Nuclear morphometry, chromatin texture and tissue architectur e (characterized by syntactic structure analysis) were measured using a sem iautomated image analysis system on 46 cases of Feulgen-stained tissue sect ions. The performance of the features was compared to that of clinical feat ures, reported to be the best prognosticators until now, such as age, lymph o-vascular permeation, histologic type, stage and grade. A K nearest neighb or 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. Classificati on rates were, respectively, 100%, 94.4% and 94.5%. In all classifications, karyometric and histometric features outperformed clinical features. In ge neral, the best performing features described differences in second-order p opulation 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 n ot hampered by the Meed to undertake complete resections and are suited to daily practice when implemented in a semiautomated image analysis system.