| risk_malignancy_index | R Documentation |
Calculates the Risk of Malignancy Index (RMI I) to stratify the risk of ovarian cancer in women with adnexal masses. The score combines serum CA-125 levels, ultrasound findings, and menopausal status. An RMI score greater than 200 suggests a higher risk of malignancy and typically warrants referral to a gynecological oncologist.
risk_malignancy_index(ca125_level, menopausal_status, multilocular_cyst,
solid_areas, bilateral_lesions, ascites,
intraabdominal_metastases)
ca125_level |
Numeric. Serum CA-125 level in U/mL. |
menopausal_status |
String. "pre" (1 point) or "post" (3 points). Post-menopausal is defined as >1 year of amenorrhea or age >50 in hysterectomized women. |
multilocular_cyst |
Numeric (0 or 1). Presence of a multilocular cystic lesion on ultrasound. (1 = Yes). |
solid_areas |
Numeric (0 or 1). Presence of solid areas within the mass on ultrasound. (1 = Yes). |
bilateral_lesions |
Numeric (0 or 1). Presence of bilateral lesions on ultrasound. (1 = Yes). |
ascites |
Numeric (0 or 1). Presence of ascites on ultrasound. (1 = Yes). |
intraabdominal_metastases |
Numeric (0 or 1). Presence of intra-abdominal metastases on ultrasound. (1 = Yes). |
The formula for RMI I is:
RMI = U \times M \times CA125
Where:
U (Ultrasound Score): 0 if no features, 1 if 1 feature, 3 if 2-5 features.
M (Menopausal Status): 1 if Pre-menopausal, 3 if Post-menopausal.
CA125: Absolute value of serum CA-125.
A list containing:
RMI_Score |
The calculated Risk of Malignancy Index. |
Risk_Category |
Interpretation based on the threshold of 200 (High vs. Low). |
Recommendation |
Referral guidance. |
Jacobs I, Oram D, Fairbanks J, et al. A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer. Br J Obstet Gynaecol. 1990;97(10):922-929. doi:10.1111/j.1471-0528.1990.tb02448.x
# Example 1: High Risk
# CA125 100, Post-menopausal (3), 2 US features (Solid, Ascites -> U=3)
# RMI = 3 * 3 * 100 = 900
risk_malignancy_index(100, "post", 0, 1, 0, 1, 0)
# Example 2: Low Risk
# CA125 25, Pre-menopausal (1), 1 US feature (Multilocular -> U=1)
# RMI = 1 * 1 * 25 = 25
risk_malignancy_index(25, "pre", 1, 0, 0, 0, 0)
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