Mucinous adenocarcinoma involving the ovary: comparative evaluation of the classification algorithms using tumor size and laterality.
For intraoperative consultation of mucinous adenocarcinoma involving the ovary, it would be useful to have approaching methods in addition to the traditional limited microscopic findings in order to determine the nature of the tumors. Mucinous adenocarcinomas involving the ovaries were evaluated in 91 cases of metastatic mucinous adenocarcinomas and 19 cases of primary mucinous adenocarcinomas using both an original algorithm (unilateral >or=10 cm tumors were considered primary and unilateral <10 cm tumors or bilateral tumors were considered metastatic) and a modified cut-off size algorithm. With 10 cm, 13 cm, and 15 cm size cut-offs, the algorithm correctly classified primary and metastatic tumors in 82.7%, 87.3%, and 89.1% of cases and in 80.6%, 84.9%, and 87.1% of signet ring cell carcinoma (SRC) excluded cases. In total cases and SRC excluded cases, 98.0% and 97.2% of bilateral tumors were metastatic and 100% and 100% of unilateral tumors <10 cm were metastatic, respectively. In total cases and SRC excluded cases, 68.4% and 68.4% of unilateral tumors >or=15 cm were primary, respectively. The diagnostic algorithm using size and laterality, in addition to clinical history, preoperative image findings, and operative findings, is a useful adjunct tool for differentiation of metastatic mucinous adenocarcinomas from primary mucinous adenocarcinomas of the ovary.
Jung
Eun Sun
ES
Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Bae
Jeong Hoon
JH
Lee
Ahwon
A
Choi
Yeong Jin
YJ
Park
Jong-Sup
JS
Lee
Kyo-Young
KY
D002288:Adenocarcinoma, Mucinous / Q000145:classification* / Q000473:pathology / Q000556:secondary; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008875:Middle Aged; D009367:Neoplasm Staging; D010051:Ovarian Neoplasms / Q000145:classification* / Q000401:mortality / Q000473:pathology