Salivary gland carcinoma such as adenoid cystic carcinoma (AdCC) is characterized by slow growth, diffuse invasion and lung metastasis, which determine the patient's prognosis. It is important to clarify an attractant molecule leading tumor cells to migrate. We examined the effects of stromal cell-derived factor (SDF) -1, a chemokine, on salivary gland carcinoma cell clone HSG and its subclone HSG-AZA3. SDF-1 promoted the invasion and migration of HSG and HSG-AZA3 cells dose-dependently. Immunocytostaining and RT-PCR indicated that HSG and HSG-AZA3 cells expressed SDF-1 receptor, CXCR4, both in protein and mRNA level, respectively. CXCR4 was present on the cell surface of HSG cells, and was downregulated by SDF-1 addition. Finally, we confirmed that CXCR4 was expressed in the tissue of AdCC. Our study suggests that SDF-1 and CXCR4 play a role in the migration of carcinoma of salivary gland origin.

Magnetic resonance imaging (MRI) for diagnostic evaluation of the breast was performed in 61 patients who visted the Breast Clinic of our hospital and were suspected to have malignant tumors by physical examination and mammography between January and December 1999. In 58 patients undergoing histological diagnostic tests (8 with benignancy and 50 with breast cancer), we compared the imaging characteristics and the time-signal intensity curves acquired by dynamic imaging between benign and malignant lesions, and evaluated the usefulness of analyzing enhancement patterns on contrast MRI. Contrast MRI revealed strong tumor enhancement in all patients; the mean time required for the signal intensity to reach a peak was about 7 min in patients with benign tumors and about 2 min in those with breast cancer. Peripheral ring enhancement was observed in 40 of the 50 patients with breast cancer (80.0%), while such enhancement was not noted in any of the patients with benign tumors.
Although diagnosis of breast disease by imaging has primarily relied on mammography and ultrasonography, the pattern of contrast enhancement on dynamic MRI also appears to be useful for determining the treatment method of breast tumors.

Objective:The topic model is a well-known method used in the field of natural language processing (NLP)that defines adocument as constructed of topics that combine specific t erms. This method is used to model topic co-occurrencemathematically. In this study,we extracted topics from featu re vectors of explicit documents called medical package insertsby using cluster analysis. Methods:We counted the terms(nouns)recognized by the morphological analysis engine MeCab and created a documentterm matrix. A value of“tf・idf”was calculated in this matrix for term weighting to avoid the effect of term frequency. We reduced the dimensionality of the matrix using singular v alue decomposition,which removed unnecessary data,and weextracted feature vectors attributed to each medical package insert. The distance between feature vectors was calculatedusing cosine distance,and cluster analysis was performed based on the distance between the vectors.Results:Cluster analysis on our document-term matrix show ed that medical package inserts of drugs that have the sameefficacy or active ingredient were included in the same cl uster. Moreover, using term weighting and dimensionalityreduction,we could extract topics from medical package inserts.Conclusion:We obtained a foothold to apply our findings t o the recommendation of similar drugs. Cluster analysis ofmedical package inserts using NLP can contribute to the pro per application of drugs. In addition,our study revealed thesimilarities of drugs and suggested possibilities for new applications from several points of view.