Phenotype and genotype associations of lung carcinoma with atypical adenomatoid hyperplasia, squamous cell dysplasia, and chromosome alterations in non-neoplastic bronchial mucosa

Abstract The frequency of preneoplastic lesions of the lung and bronchial mucosa as well as potential genotype alterations in spatial relationship to pulmonary malignancies still need intensive investigations in order to understand the occurrence and manifestation of lung cancer in detail. To investigate the contemporary manifestation of lung cancer precursor lesions, peripheral (non-neoplastic) lung parenchyma and bronchial mucosa of operated lung carcinomas were analyzed at distinct distances (1, 2, 3, and 4 cm) from the tumor boundary for pre-neoplastic lesions -atypical adenomatoid hyperplasia (AAH) and squamous cell dysplasia (SCD), in 150 surgical specimens. Short-term tissue cultures of additional 55 primary and secondary lung tumors and their surrounding non-neoplastic bronchial mucosa were performed at the same distances in order to search for chromosome alterations, i.e. genotype aberrations. In phenotype observations, atypical adenomatoid hyperplasia was noted in 19/150 (13%) cases, and squamous cell dysplasia in 46/150 (31%) cases. The degree of cellular atypia decreased with increasing distance from the tumor boundary in both AAH and SCM. AAH was observed more frequently in adenocarcinomas, SCQ more frequently in squamous cell carcinomas. In genotype observations, the average number of abnormal metaphases measured 4.5/10 high power fields (HPF) in primary lung carcinomas, and only 2/10 in metastases. Data indicate that the so-called preneoplastic lesions in the lung are not completely tumor-precursor lesions, but, in addition, induced by the tumor itself

Texture- and Object-Related Automated Information Analysis in Histological Still Images of Various Organs

OBJECTIVE: To create algorithms and application tools that can support routine diagnoses of various organs. MATERIALS: A generalized algorithm was developed that permits the evaluation of diagnosis-associated image features obtained from hematoxylin-eosin-stained histopathologic slides. The procedure was tested for screening of tumor tissue vs. tumor free tissue in 1,442 cases of various organs. Tissue samples studied include colon, lung, breast, pleura, stomach and thyroid. The algorithm distinguishes between texture- and object-related parameters. Texture-based information-defined as gray value per pixel measure-is independent from any segmentation procedure. It results in recursive vectors derived from time series analysis and image features obtained by spatial dependent and independent transformations. Object-based features are defined as gray value per biologic object measured. RESULTS: The accuracy of automated crude classification was between 95% and 100% based upon a learning set of 10 cases per diagnosis class. Results were independent from the analyzed organ. The algorithm can also distinguish between benign and malignant tumors of colon, between epithelial mesothelioma and pleural carcinomatosis or between different common pulmonary carcinomas. CONCLUSION: Our algorithm distinguishes accurately among crude histologic diagnoses of various organs. It is a promising technique that can assist tissue-based diagnosis and be expanded to virtual slide evaluation.306323335International Academy of Telepathology, HeidelbergVerein zur Forderung des biologisch technologischen Fortschritts in der Medizi