<p>The complexity of the tumor microenvironment impedes effectiveness of a single biomarker in accurately diagnosing, predicting or monitoring ovarian cancer. Multivariate Index/multi biomarker-based algorithms have better predictive accuracy. The present study was conducted to develop clinically useful cancer risk predictive algorithm, based on clinical, proteomic, image-based data of patients presenting with ovarian mass. Women presenting with ovarian mass were identified, prospectively enrolled, evaluated clinically and as per questionnaire developed. After ultrasonography, CT scan abdomen pelvis and biopsy with histopathology, biochemical and proteomic analysis were done. Predictive accuracy of parameters individually studied with ROC analysis. Youden’s J Index and at 90% specificity cut-off was determined that can correctly classify patients into benign, malignant groups. Training data used to generate non- linear regression modeling-based indexing algorithm, for predicting type of mass. Generated index was compared with ROMA index by ROC analysis, classification, Linn’s concordance. CA125 was determined to be most potent diagnostic parameter among individual biomarkers AUC 0.999 (<i>p</i> &lt; 0.001), cut off was &gt; 116&#xa0;IU/ml by Youden J index. When compared to single biomarkers, ROMA showed better classifying power represented by percentage classifiers, Linn’s Concordance Coefficient. This study generated indexing algorithm was found better than ROMA by ROC analysis, classification and concordance concluding that heterogenous clinical, biochemical, imaging, proteomic parameters-based algorithm has better diagnostic accuracy in detecting cancer among patients with ovarian mass. Comprehensive cancer risk assessment in ovarian mass is more accurate with wide spectrum approach, including diverse patient data; appropriate feature selection, design techniques, to generate robust risk predictive algorithm.</p>