Obstetrics and Gynecology · 2010 · 79 citations · 18 references
MedicineGynecologySurgeryGynecologic OncologyGynecologic UltrasoundSurgical OncologyBenign Ovarian TumorsBorderline Ovarian TumorsCancer StagingMalignant NeoplasmsOvarian CancerTumor ClassificationHistologic Tumor ClassificationMalignant Ovarian TumorsOvarian Mass Risk StratificationOvarian Neoplasia Risk StratificationOvarian Tumor DiagnosisTumor Morphology AnalysisTumor Morphology CorrelationUltrasound Risk Stratification
OBJECTIVE: To estimate the accuracy of preoperative ultrasonography, serum CA 125, and patient demographics as a means of predicting risk of malignancy in women with a ultrasonographically confirmed adnexal mass. METHODS: Tumor morphology derived from ultrasonographic images, tumor size, tumor bilaterality, serum CA 125, and patient demographics were evaluated preoperatively in 395 patients undergoing surgery from 2001 to 2008. Tumor morphology was classified as complex, solid, or cystic. Preoperative findings were compared with tumor histologic findings at the time of surgery. Multivariable classification and regression tree analysis were used to identify a group of patients at high risk of ovarian malignancy. RESULTS: One hundred eighteen patients had ovarian cancer, 13 patients had ovarian tumors of borderline malignancy, and 264 had benign ovarian tumors. Multivariable classification and regression tree analysis defined women at high risk of ovarian malignancy as those with an adnexal mass having complex or solid morphology and a serum CA 125 value greater than 35 units/mL. This definition had a positive predictive value of 84.7% and a negative predictive value of 92.4% and correctly identified 77.3% of patients with stage I and stage II ovarian cancer and 98.6% of patients with stage III and stage IV ovarian cancer. CONCLUSION: Patients with solid or complex ovarian tumors and an elevated serum CA 125 level (greater than 35 units/mL) are at high risk of ovarian malignancy. LEVEL OF EVIDENCE: II.
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Classification and Regression Trees.
John Van Ryzin, Leo Breiman, Jerome H. Friedman et al. · Journal of the American Statistical Association · 1986 · 21K citations
Classification and regression trees
J. Praagman · European Journal of Operational Research · 1985 · 10.2K citations
Ahmedin Jemal, Rebecca L. Siegel, Elizabeth Ward et al. · CA A Cancer Journal for Clinicians · 2009 · 9.7K citations · Full text
Frequency of Symptoms of Ovarian Cancer in Women Presenting to Primary Care Clinics
Barbara A. Goff · JAMA · 2004 · 497 citations
Medicine, Gynecology, Oncology +13
Development of an ovarian cancer symptom index
Barbara A. Goff, Lynn S. Mandel, Charles W. Drescher et al. · Cancer · 2006 · 402 citations
Medicine, Gynecology, Oncology +17