A nomogram for estimating the probability of ovarian cancer

ObjectiveAccurate preoperative estimates of the probability of malignancy in women with adnexal masses are essential for ensuring optimal care. This study presents a new statistical model for combining predictive information and a graphic decision support tool for calculating risk of malignancy.MethodsThe study included 153 women treated with definitive surgery for adnexal mass between 2001 and 2007 with preoperative ultrasound testing and a serum CA125. Multivariable logistic regression was used to develop a statistical model for estimating the probability of ovarian cancer as a function of age, ultrasound score, and CA125 value, with adjustments for nonlinear and interactive relationships.ResultsA total of 20 cases of pathologically confirmed cancer (13 invasive malignancies, and 7 tumors of low malignant potential) were identified (20/153 = 13%). The model obtained excellent discrimination (ROC area = 0.87), explained nearly half of the observed variation in the risk of malignancy (R2 = 0.43), and was well calibrated across the full range of malignancy probabilities. The model equation is represented in the form of a nomogram, which can be used to calculate preoperative probability of malignancy. At a 5% risk of malignancy threshold, the model has a sensitivity of 90% and a specificity of 73%.ConclusionsStatistical models for estimating the probability of adnexal mass malignancy are substantially improved by including adjustments for non-linear relationships among key variables. A clinically relevant nomogram provides an objective tool to further aid clinicians in counseling patients and ensuring proper referral to surgical sub-specialists when indicated.

Research Highlights►This study presents a statistical model for measuring probability of ovarian cancer. ►Model includes age, ultrasound score, CA125 value, and nonlinearity adjustments. ►Excellent discrimination and calibration are obtained across the probability range.