Added value of serum proteins to clinical and ultrasound information in predicting the risk of malignancy in ovarian tumors
Authors: Coosemans A , Ceusters J , Landolfo C , Baert T , Froyman W , Heremans R , Thirion G , Claes S , Oosterlynck J , Wouters R , Vankerckhoven A , Moro F , Mascilini F , Neumann A , Van Rompuy AS , Schols D , Billen J , Van Gorp T , Vergote I , Bourne T , Van Holsbeke C , Chiappa V , Scambia G , Testa A , Fischerova D , Timmerman D , Van Calster B 
Published: March 2024

Background

The ADNEX model (Assessment of Different NEoplasias in the adnexa) is the best performing model to predict the risk of malignancy (binary) and type of malignancy (multiclass) in ovarian tumors. The immune system plays a role in the onset and progression of ovarian cancer. Preliminary research has suggested that immune-related biomarkers can help in the discrimination of ovarian tumors. We aimed to assess which proteins have the most additional diagnostic value in addition to ADNEX’ clinical and ultrasound predictors.

Materials and methods

In this exploratory diagnostic study, 1086 patients with an adnexal mass scheduled for surgery were consecutively enrolled at five oncology centers and one non- oncology center in Belgium, Italy, Czech Republic and United Kingdom between 2015 and 2019. The quantification of 33 serum proteins was carried out preoperatively, using multiplex high throughput immunoassays (Luminex) and electrochemiluminescence immuno-assay (ECLIA). Logistic regression analysis was performed for ADNEX’ clinical and ultrasound predictors alone (age, maximum diameter of lesion, proportion of solid tissue, presence of >10 cyst locules, number of papillary projections, acoustic shadows and ascites) and after adding proteins. We reported the AUC for benign vs malignant, Polytomous Discrimination Index (PDI; a multiclass AUC) and pairwise AUCs for pairs of tumor types. AUCs were corrected for optimism using bootstrapping.

Results

After applying exclusion criteria, 932/1086 patients were eligible for analysis (474 benign, 135 borderline, 84 stage I primary invasive cancer, 208 stage II-IV primary invasive cancer, 31 secondary metastatic invasive tumors). ADNEX predictors alone had an AUC of 0.909 (95% CI 0.894-0.929) to discriminate benign from malignant tumors, and a PDI of 0.532 (0.510-0.589). HE4 yielded the highest increase in AUC (+0.026), followed by CA125 (+0.017). CA125 yielded the highest increase in PDI (+0.049), followed by HE4 (+0.036). Whereas CA125 mainly improved pairwise AUCs between different types of invasive tumors (increases between 0.020-0.165 over ADNEX alone), HE4 mainly improved pairwise AUCs for benign tumors versus stage I (+0.022) and benign tumors versus stage II-IV ovarian cancers (+0.028). CA72.4 might be useful to distinguishing secondary metastatic tumors from benign, borderline, and stage I tumors. CA15.3 might be useful to discriminate borderline tumors from stage I and stage II-IV tumors. Distinguishing stage I and borderline tumors (AUCs ≤ 0.72) and stage I and secondary metastatic tumors (AUCs ≤ 0.76) remained difficult after adding proteins.

Conclusions

CA125 had the highest added value over clinical and ultrasound predictors to distinguish between the five tumor types, followed by HE4. In addition, CA72.4 and CA15.3 may further improve discrimination but findings for these proteins should be confirmed. The immune-related proteins were in general not able to discriminate the groups.