Summary of main models and scoring systems for pre-operative diagnosis of ovarian tumors (modified from reference 63)
| Model or system: type | Predictor variables | Remarks |
| Simple descriptors: classification as benign or malignant | Benign descriptor (BD) 1: unilocular tumor with ground-glass echogenicity in a pre-menopausal woman BD2: unilocular tumor with mixed echogenicity and acoustic shadows in a pre-menopausal woman BD3: unilocular anechoic tumor with regular walls and maximum diameter of lesion <10 cm BD4: remaining unilocular tumor with regular walls Malignant descriptor (MD) 1: Tumor with ascites and at least moderate color Doppler blood flow in a post-menopausal woman MD2: age >50 years and CA 125 >100 U/mL | No risk estimates Based on clinical, ultrasound and CA 125 information Possible to calculate result without computer |
| RMI: score | CA 125, menopausal status, ultrasound score based on five binary ultrasound variables (multilocular cyst, solid areas, bilateral lesions, ascites, evidence of metastases on abdominal ultrasound) | No risk estimates Based on clinical, ultrasound and CA 125 information Possible to calculate result without computer Online calculators available |
| Simple Rules: classification as benign, inconclusive or malignant | Classification based on 10 binary features—five benign and five malignant features: Benign features: unilocular cyst, smooth multilocular cyst with largest diameter <100 mm, presence of solid areas with largest diameter <7 mm, acoustic shadows, no vascularization on color Doppler Malignant features: irregular solid tumor, irregular multilocular solid tumor with largest diameter ≥100 mm, presence of ascites, ≥4 papillary projections, very strong vascularization on color Doppler | No risk estimates Classification into only three groups Based on dichotomized ultrasound features Easy to use without computer Available as smartphone app |
| LR2: risk model based on logistic regression | Age (years), presence of acoustic shadows, presence of ascites, presence of papillary projections with blood flow, maximum diameter of largest solid component, irregular internal cyst walls | Risk estimates Based on clinical and ultrasound information Requires computer Available as smartphone app |
| Simple Rules risk: risk model based on logistic regression | The 10 binary features used in the Simple Rules, type of center (oncology center vs other) | Risk estimates Based on dichotomized ultrasound features Developed to add risk estimates for Simple Rules Available as online calculator; available in ultrasound machines from some manufacturers |
| ADNEX without CA 125: risk model based on multinomial logistic regression | Age (years), maximum diameter of lesion (mm), maximum diameter of largest solid component (mm), number of papillary projections (ordinal), presence of acoustic shadows, presence of ascites, presence of more than 10 cyst locules, type of center (oncology center vs other) | Risk estimates Also estimates risk of four subtypes of malignancy Based on clinical and ultrasound information Subjective predictors are avoided a priori (eg, color score or irregular cyst walls) Requires computer Available as smartphone app and as online calculator; available in ultrasound machines from some manufacturers |
| ADNEX with CA 125: risk model based on multinomial logistic regression | Same variables as for ADNEX without CA 125, and additionally serum CA 125 (IU/L) | Risk estimates Also estimates risk of four subtypes of malignancy Based on clinical, ultrasound, and CA 125 information Subjective predictors are avoided a priori (eg, color score or irregular cyst walls) Requires computer Available as smartphone app and as online calculator; available in ultrasound machines from some manufacturers |
ADNEX, Assessment of Different NEoplasias in the adneXa; CA 125, cancer antigen 125; RMI, risk of malignancy index.