摘要
Objective: To systematically analyze and summarize non-thyrogenous masses of the neck (NTMN) by consid-eration of new areas, a large sample size and multiple-aspect analysis. Methods: Our research involved 3125 NTMN cases. We summarized the proportion of various NTMN and the distribution of the neck diseases based on the new international clas-sification. The clinical traits such as sexual proportion and age, etc, were analyzed along with the unknown primary cervical metastatic carcinomas (UPCMC), and built up a mathematical model based on the data above. Results: There were 68 dif-ferent diseases identified. Among all the NTMN, the percentage of metastatic carcinomas was 63.3%. The neck masses with a focus above the clavicle comprised 62.3% of the metastatic carcinomas whose focuses were clear. Moreover, other results almost supported the "rule of 80%". There was an obvious distribution of traits at every sub level. For example, there were 23 different diseases in level III, of which the most common was lymphoma. UPCMC made up 12.3% of all metastatic carci-nomas. The clinic cases could be analyzed by our model even to form a primary diagnosis which showed a high coincident rate with clinic diagnosis. Conclusion: NTMN are complex and various, with a definite distribution in each neck level. Data relating component character, sex ratio and UPCMC et al to the clinical traits of NTMN will provide vigorous support for clinical applications. The mathematical model could be an efficient method to synthetically analyze complicate data of NTMN.
Objective： To systematically analyze and summarize non-thyrogenous masses of the neck （NTMN） by consideration of new areas, a large sample size and multiple-aspect analysis. Methods： Our research involved 3125 NTMN cases. We summarized the proportion of various NTMN and the distribution of the neck diseases based on the new international classification. The clinical traits such as sexual proportion and age, etc, were analyzed along with the unknown primary cervical metastatic carcinomas （UPCMC）, and built up a mathematical model based on the data above. Results： There were 68 different diseases identified. Among all the NTMN, the percentage of metastatic carcinomas was 63.3%. The neck masses with a focus above the clavicle comprised 62.3% of the metastatic carcinomas whose focuses were clear. Moreover, other results almost supported the ＂rule of 80%＂. There was an obvious distribution of traits at every sub level. For example, there were 23 different diseases in level Ⅲ, of which the most common was lymphoma. UPCMC made up 12.3% of all metastatic carcinomas. The clinic cases could be analyzed by our model even to form a primary diagnosis which showed a high coincident rate with clinic diagnosis. Conclusion： NTMN are complex and various, with a definite distribution in each neck level. Data relating component character, sex ratio and UPCMC et al to the clinical traits of NTMN will provide vigorous support for clinical applications. The mathematical model could be an efficient method to synthetically analyze complicate data of NTMN.
