Fumikazu Kimura1, Takaki Kobayashi1, Ryo Kanai1, Yukihiro Kobayashi2, Ohtani Yuhi3, Hiroyoshi Ota1, Masahiro Yamaguchi3, Yoshiharu Yokokawa4, Takeshi Uehara5, Keiko Ishii6. 1. Department of Biomedical Laboratory Sciences, School of Health Sciences, Shinshu University. 2. Department of Laboratory Medicine, Shinshu University Hospital. 3. Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology. 4. Division of Physical Therapy, Shinshu University School of Health Sciences. 5. Department of Laboratory Medicine, Shinshu University School of Medicine. 6. Division of Diagnostic Pathology, Okaya City Hospital.
Abstract
BACKGROUND: Lobular endocervical glandular hyperplasia (LEGH) was first described by Nucci et al. in 1999 and is believed to be a precancerous lesion of minimal deviation adenocarcinoma and gastric-type adenocarcinoma in the uterine cervix. LEGH lesions do not always exhibit apparent cellular and structural atypia, so are difficult to distinguish from normal endocervical cells (EC cells) with cytological examination. Therefore, we often struggle to make a definite diagnosis of LEGH. METHODS: We used microscopy images of cytological specimens that were diagnosed as EC cells and LEGH cells. Signal intensity in whole nuclear area and in heterochromatin and euchromatin regions, euchromatin area ratio, and nuclear morphological features were quantified in each cell nucleus of the cases. Statistical analyses were conducted to determine statistical significance. Finally, we performed linear support vector machine (LSVM) modeling as a discriminant analysis using the quantified features. RESULTS: Signal intensity in whole nuclear area, and heterochromatin and euchromatin regions of EC cell nuclei were higher than that of the LEGH cell nuclei. Morphologically, EC cell nuclei were larger than LEGH cell nuclei, and nuclei of LEGH cells had irregular nuclear respectively membrane structure and an elongated shape. The LSVM accuracy of 10-fold cross validation and leave-one-case-out cross-validation (LOCOCV) using all measured features were 84.7% to 89.3% and 78.6% to 86.0%, respectively. CONCLUSIONS: The LVSM analysis using features extracted from signal intensity and morphological analysis was useful for discrimination of EC cells vs LEGH cells. We therefore believe that this image analysis method could be used for early detection of LEGH.
BACKGROUND:Lobular endocervical glandular hyperplasia (LEGH) was first described by Nucci et al. in 1999 and is believed to be a precancerous lesion of minimal deviation adenocarcinoma and gastric-type adenocarcinoma in the uterine cervix. LEGH lesions do not always exhibit apparent cellular and structural atypia, so are difficult to distinguish from normal endocervical cells (EC cells) with cytological examination. Therefore, we often struggle to make a definite diagnosis of LEGH. METHODS: We used microscopy images of cytological specimens that were diagnosed as EC cells and LEGH cells. Signal intensity in whole nuclear area and in heterochromatin and euchromatin regions, euchromatin area ratio, and nuclear morphological features were quantified in each cell nucleus of the cases. Statistical analyses were conducted to determine statistical significance. Finally, we performed linear support vector machine (LSVM) modeling as a discriminant analysis using the quantified features. RESULTS: Signal intensity in whole nuclear area, and heterochromatin and euchromatin regions of EC cell nuclei were higher than that of the LEGH cell nuclei. Morphologically, EC cell nuclei were larger than LEGH cell nuclei, and nuclei of LEGH cells had irregular nuclear respectively membrane structure and an elongated shape. The LSVM accuracy of 10-fold cross validation and leave-one-case-out cross-validation (LOCOCV) using all measured features were 84.7% to 89.3% and 78.6% to 86.0%, respectively. CONCLUSIONS: The LVSM analysis using features extracted from signal intensity and morphological analysis was useful for discrimination of EC cells vs LEGH cells. We therefore believe that this image analysis method could be used for early detection of LEGH.