||Background: Ovarian carcinoma is composed of five major histological types which associate with outcome and predict therapeutic response. Our aim was to evaluate histological type assessments across centres participating in the Ovarian Tumor Tissue Analysis (OTTA) consortium using an immunohistochemical (IHC) prediction model. Methods: Tissue microarrays (TMAs) and clinical data were available for 524 pathologically confirmed ovarian carcinomas. Centralized IHC was performed for ARID1A, CDKN2A, DKK1, HNF1B, MDM2, PGR, TP53, TFF3, VIM, and WT1, and three histological type assessments were compared: the original pathologic type, an IHC-based calculated type (termed TB_COSPv2), and a WT1-assisted TMA core review. Results: The concordance between TB_COSPv2 type and original type was 73%. Applying WT1-assisted core review, the remaining 26% discordant cases subdivided into unclassifiable (6%), TB_COSPv2 error (6%), and original type error (14%). The largest discordant subgroup was classified as endometrioid carcinoma (EC) by original type and as high-grade serous carcinoma (HGSC) by TB_COSPv2. When TB_COSPv2 classification was used, the difference in overall survival of EC compared to HGSC became significant (RR 0.60, 95% CI 0.37-0.93, p=0.021), consistent with previous reports. In addition, 71 cases with unclear original type could be histologically classified by TB_COSPv2. Conclusions: Research cohorts, particularly those across different centres within consortia, show significant variability in original histological type diagnosis. Our IHC-based reclassification produced more homogeneous types with respect to outcome than original type. Impact: Biomarker-based classification of ovarian carcinomas is feasible, improves comparability of results across research studies, and can reclassify cases which lack reliable original pathology.