Hye-Kyoung Yoon

Diagnostic Difficulties in Fine Needle Aspiration of Benign Salivary Glandular Lesions

Background The diagnostic accuracy of fine needle aspiration cytology (FNAC) of salivary lesions is relatively high, but cytologic interpretation might be confusing if the sample is lacking typical cytologic features. Methods There were 77 cases of benign salivary lesions, consisting of pleomorphic adenoma (PA) in 61 cases, Warthins tumor (WT) in 12 cases, and other benign lesions in 4 cases. The causes of the discrepancies between the FNAC and the histologic diagnoses were evaluated. Results Major discrepancies were noted in 4 of the 61 PA cases, and in 1 of 12 WT cases. The causes of the major discrepancies were a mislabeled site in 1 PA and 1 WT case, and an interpretation error in 3 PA cases. Minor discrepancies were more common in the WT cases (7 of 12 cases) than in the PA cases (11 of 61 cases). The causes of the minor discrepancies were a mislabeled site in 1 PA and 1 WT case, an inadequate sample in 7 PA and 2 WT cases, a lack of typical cytomorphology in 2 PA and 2 WT cases, and an interpretation error in 1 PA and 2 WT cases. Conclusions To increase the diagnostic accuracy in the benign salivary lesions, recognition of both characteristic and less typical cytomorphology is needed.

Classification of breast tissue images based on wavelet transform using discriminant analysis, neural network and SVM

In this paper, we described breast tissue image analyses using texture features from Haar wavelet transformed images to classify breast lesion of ductal organ Benign, DCIS and CA. The approach for creating a classifier is composed of 2 steps: feature extraction and classification. Therefore, in the feature extraction step, we extracted texture features from wavelet transformed images with 10/spl times/ magnification. In the classification step, we created three classifiers from each image of extracted features using statistical discriminant analysis, neural networks (back-propagation algorithm) and SVM (support vector machines). In this study, we conclude that the best classifier in histological sections of breast tissue in the texture features from second-level wavelet transformed images used in discriminant function.

Multi-resolution wavelet-transformed image analysis of histological sections of breast carcinomas

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

Image Analysis of Ductal Proliferative Lesions of Breast Using Architectural Features

We propose a method to classify breast lesions of ducatal origin. The materials were tissue sections of the intraductal proliferative lesions of the breast: benign(DH:ductal hyperplasia), ductal carcinoma in situ(DCIS). The total 40 images from 70 samples of ducts were digitally captured from 15 cases of DCIS and 25 cases of DH diagnosed by pathologist. To assess the correlation between computerized images analysis and visual analysis by a pathologist, we extracted the total lumen area/gland area, to segment the gland(duct) area used a snake algorithm, to segment the lumen used multilevel Otsus method in the duct from 20x images for distinguishing DH and DCIS. In duct image, we extracted the five texture features (correlation, entropy, contrast, angular second moment, and inverse difference moment) using the co-occurrence matrix for a distribution pattern of cells and pleomorphism of the nucleus. In the present study, we obtained classification accuracy rates of 91.33%, the architectural features of breast ducts has been advanced as a useful features in the classification for distiguishing DH and DCIS. We expect that the proposed method in this paper could be used as a useful diagnostic tool to differentiate the intraductal proliferative lesions of the breast.

A case of synchronous multiple myeloma and chronic myeloid leukemia

REFERENCES 1. Niscola P, Vischini G, Tendas A, et al. Management of hematological malignancies in patients affected by renal failure. Expert Rev Anticancer Ther 2011;11:415-32. 2. Niscola P, Tendas A, Luo XD, et al. The management of membranous glomerulopathy in allogeneic stem cells transplantation: updated literature. Cardiovasc Hematol Agents Med Chem 2013;11:67-76. 3. Launay-Vacher V, Oudard S, Janus N, et al. Prevalence of Renal Insufficiency in cancer patients and implications for anticancer drug management: the renal insufficiency and anticancer medications (IRMA) study. Cancer 2007;110:1376-84. 4. Hong J, Lee S, Chun G, et al. Baseline renal function as a prognostic indicator in patients with newly diagnosed diffuse large B-cell lymphoma. Blood Res 2016;51:113-21. 5. Terpos E, Christoulas D, Kastritis E, et al. The Chronic Kidney Disease Epidemiology Collaboration cystatin C (CKD-EPICysC) equation has an independent prognostic value for overall survival in newly diagnosed patients with symptomatic multiple myeloma; is it time to change from MDRD to CKD-EPI-CysC equations? Eur J Haematol 2013;91:347-55. 6. Ofran Y, Tallman MS, Rowe JM. How I treat acute myeloid leukemia presenting with preexisting comorbidities. Blood 2016; 128:488-96. 7. Niscola P, Scaramucci L, Vischini G, et al. The use of major analgesics in patients with renal dysfunction. Curr Drug Targets 2010;11:752-8. 8. Malik L, Mejia A, Weitman S. Eligibility of patients with renal impairment for Phase I trials: Time for a rethink? Eur J Cancer 2014;50:2893-6. 9. Salahudeen AK, Bonventre JV. Onconephrology: the latest frontier in the war against kidney disease. J Am Soc Nephrol 2013;24:26-30. A case of synchronous multiple myeloma and chronic myeloid leukemia