Diagnosis and prognosis of neuroendocrine tumours of the lung by means of high resolution image analysis

Neuroendocrine tumours (NET) of the lung are divided in subtypes with different malignant potential. The first is the benign or low-grade malignant tumours, well-differentiated, called typical carcinoids (TC) and the second is the high-grade malignant tumours, poorly differentiated of small (SCLC) or large cell type (LCLC). Between these tumour types lies the well- differentiated carcinoma with a lower grade of malignancy (WDNEC). In clinical routine it is very important with regard to prognosis to distinguish patients with low malignant potential from those with higher ones. In this study 32 cases of SCLC, 13 of WDNEC and 14 of TC with a follow-up time up to 7 years were collected. Sections 4 μm thick from paraffin embedded tissue were Feulgen stained. By means of high resolution image analysis 100 nuclei per case were randomly gathered to extract morphometric, densitometric and textural quantitative features. To investigate the ploidy status of the turnout the corrected DNA distribution was calculated. Stepwise linear discriminant analysis to differentiate the classes and Cox regression analysis for the survival time analysis were applied. Using chromatin textural and morphometric features in two two-class discriminations, 11 of the 14 TC cases and 8 of the 13 WDNEC cases were correctly classified and 11/13 WDNEC cases and 28/32 SCLC cases, respectively. The WDNEC cases are more similar in chromatin structure to TC than to SCLC. For the survival analysis, only chromatin features were selected to differentiate patients with better and worse prognosis independent of staging and tumour type.