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- Histopathological study towards selection of treatment modalities and outcome prediction in head and neck cancer
Projects
- Histological criteria for effectiveness of chemo- and radio-therapy for gastrointestinal tumors
- Study of premalignant and in-situ carcinoma for Bile-duct and pancreas group
- Histopathological study towards selection of treatment modalities and outcome prediction in head and neck cancer
- Studies by Breast Group
- Hepatocarcinogenesis using experimental liver cancer models
- Pathological studies for gynecological malignancies
- Diagnostic and etiologic studies for lung cancers
- Clinicopathological studies with aids of chromosome analysis for genitourinary cancers
- Pathological and molecular studies for bone and soft tissue tumors
- Pathological and molecular studies for hematological neoplasms
Histopathological study towards selection of treatment modalities and outcome prediction in head and neck cancer
Head and neck tumor group
Members: Drs. N. Yamamoto, Y Sato, N. Motoi, Y Kimura, M Kabeya, E Fujiwara
Head & Neck Tumor group provides pathological information for surgeons and participate in researches concerned with this anatomical region. A large variety of tumors including salivary and thyroid glands are treated in our hospital, however, head and neck squamous cell carcinoma (HNSCC) is the main target of our group. We place a high priority on studies that accurately predict outcome and provide information for choosing treatment options.
The best predictor of outcome in HNSCC remains the presence or absence of neck metastases. To augment the predictive power of the clinical staging system, we have examined stepwise sections of glossectomy materials. Through histopathological examinations, differentiation markers such as cancer pearl formation in the infiltrating front have proven valuable in predicting prognosis.
In an attempt to identify patients most likely to respond to chemo-radiotherapy, we also take part in research projects to determine gene expression profiles in HNSCC. Such an achievement would make it possible to select the best therapy for individual patients based on their distinct tumor features.
