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著者: Kenneth R Hess, Gauri R Varadhachary, Sarah H Taylor, Wei Wei, Martin N Raber, Renato Lenzi, James L Abbruzzese
雑誌名: Cancer. 2006 Apr 1;106(7):1624-33. doi: 10.1002/cncr.21778.
Abstract/Text
BACKGROUND: Unique metastatic patterns cited in the literature often arise from anecdotal clinical observations and autopsy reports. The authors analyzed clinical data from a large number of patients with histologically confirmed, distant-stage adenocarcinoma to evaluate metastatic patterns.
METHODS: Tumor registry data were collected between 1994-1996 on 11 primary tumor sites and 15 metastatic sites from 4399 patients. The primary and metastatic sites were cross-tabulated in various ways to identify patterns, and the authors developed algorithms by using multinomial logistic regression analysis to predict the locations of primary tumors based on metastatic patterns.
RESULTS: Three primary tumors had single, dominant metastatic sites: ovary to abdominal cavity (91%), prostate to bone (90%), and pancreas to liver (85%). The liver was the dominant metastatic site for gastrointestinal (GI) primary tumors (71% of patients), whereas bone and lung metastases were noted most frequently in non-GI primary tumors (43% and 29%, respectively). In a study of combinations of liver, abdominal cavity, and bone metastases, 86% of prostate primary tumors had only bone metastases, 80% of ovarian primary tumors had only abdominal cavity metastases, and 74% of pancreas primary tumors had only liver metastases. A single organ was the dominant source of metastases in 7 sites: axillary lymph node from the breast (97%), intestinal lymph node from the colon (84%), thoracic lymph node from the lung (66%), brain from the lung (64%), mediastinal lymph node from the lung (62%), supraclavicular lymph node from the breast (51%), and adrenal gland from the lung (51%).
CONCLUSIONS: The algorithms that the authors developed achieved a cross-validated accuracy of 64% and an accuracy of 64% on an 1851-patient independent test set, compared with 9% accuracy when a random classifier was used.
Copyright 2006 American Cancer Society.
PMID 16518827 Cancer. 2006 Apr 1;106(7):1624-33. doi: 10.1002/cncr.21778.
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